CN111643057A

CN111643057A - Physiological parameter data processing method and system

Info

Publication number: CN111643057A
Application number: CN202010531989.4A
Authority: CN
Inventors: 李江
Original assignee: Kangjian Information Technology Shenzhen Co Ltd
Current assignee: Kangjian Information Technology Shenzhen Co Ltd
Priority date: 2020-06-11
Filing date: 2020-06-11
Publication date: 2020-09-11
Anticipated expiration: 2040-06-11
Also published as: CN111643057B

Abstract

The invention provides a physiological parameter data processing method, which comprises the steps of obtaining physiological parameter data of a user, which is acquired by wearable equipment; analyzing the acquisition time of the acquired physiological parameter data of each category respectively so as to divide the physiological parameter data of each category into first weight data and second weight data; respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category; determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table; and sending the state parameters to terminal equipment so that the terminal equipment displays the states of all organs according to the state parameters. The invention can accurately find the state of each organ of the user in time; and the processing amount of physiological parameter data can be reduced, and the data processing efficiency is improved.

Description

Physiological parameter data processing method and system

Technical Field

The embodiment of the invention relates to the field of big data, in particular to a method and a system for processing physiological parameter data.

Background

Most people in real life do not have visual concepts on the knowledge of self health condition, body structure and medical care, the cognition of self physiological data is insufficient, and the health condition of the people is not well known due to shallow medical knowledge, so that a plurality of treatment optimal periods are missed.

Therefore, how to know the health condition of the user in time becomes a problem to be solved urgently.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method, an apparatus, a system, a computer device, and a computer-readable storage medium for processing physiological parameter data, so as to solve the problem in the prior art that the health status of a user cannot be obtained accurately in time.

The embodiment of the invention solves the technical problems through the following technical scheme:

a method of physiological parameter data processing, comprising:

acquiring physiological parameter data of a user, which is acquired by wearable equipment, wherein the physiological parameter data comprises at least two types of data of heart rate data, blood pressure data and blood oxygen data;

analyzing the acquisition time of the acquired physiological parameter data of each category respectively so as to divide the physiological parameter data of each category into first weight data and second weight data;

respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category;

determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table;

and sending the state parameters to terminal equipment so that the terminal equipment displays the states of all organs according to the state parameters.

Optionally, analyzing the acquired acquisition time of each category of physiological parameter data respectively to divide the physiological parameter data of each category into first weight data and second weight data includes:

analyzing the acquisition time of each piece of acquired physiological parameter data in each category, if the acquisition time of the currently analyzed physiological parameter data is within a preset time period, taking the currently analyzed physiological parameter data as one piece of physiological parameter data in the first weight data, and if the acquisition time of the currently analyzed physiological parameter data is not within the preset time period, taking the currently analyzed physiological parameter data as one piece of physiological parameter data in the second weight data;

and repeating the step of analyzing the acquisition time of each physiological parameter strip data in the physiological parameter data of each category until all the physiological parameter data in the physiological parameter data of each category are divided.

Optionally, the processing the first weight data and the second weight data of the physiological parameter data of each category respectively to obtain the physiological characteristic value corresponding to the physiological parameter data of each category includes:

acquiring weight values corresponding to first weight data and second weight data of physiological parameter data of each category;

selecting physiological parameter data of data volume corresponding to the obtained weight values from first weight data and second weight data of the physiological parameter data of each category according to the obtained weight values;

the selected physiological parameter data are used as the data to be processed of the physiological parameter data of each category;

and analyzing the data to be processed of the physiological parameter data of each category respectively to obtain physiological characteristic values corresponding to the physiological parameter data of each category.

Optionally, the analyzing the to-be-processed data of the physiological parameter data of each category respectively to obtain the physiological characteristic value corresponding to the physiological parameter data of each category includes:

acquiring standard physiological parameter values corresponding to various types of physiological parameter data, wherein the standard physiological parameter values comprise a plurality of standard physiological parameter values;

calculating the ratio of physiological parameter data in the to-be-processed data of the physiological parameter data of each category in a plurality of preset standard physiological parameter value intervals;

and determining physiological characteristic values corresponding to the physiological parameter data of each category according to the calculated ratio.

Optionally, before the step of analyzing the acquisition time of the acquired physiological parameter data of each category respectively to divide the physiological parameter data of each category into the first weight data and the second weight data, the method further includes:

and determining the number of the called CPUs according to the data volume of the acquired physiological parameter data of each category.

judging whether the data volume of the acquired physiological parameter data of each category is greater than or equal to a preset threshold value or not;

and if the data volume of the acquired physiological parameter data of each category is greater than or equal to the preset threshold, executing the step of analyzing the acquisition time of the acquired physiological parameter data of each category respectively so as to divide the physiological parameter data of each category into first weight data and second weight data.

In order to achieve the above object, an embodiment of the present invention further provides a physiological parameter data processing apparatus, including:

the wearable device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring physiological parameter data of a user, which are acquired by the wearable device, and the physiological parameter data comprise at least two types of data in heart rate data, blood pressure data and blood oxygen data;

the analysis module is used for analyzing the acquired acquisition time of the physiological parameter data of each category respectively so as to divide the physiological parameter data of each category into first weight data and second weight data;

the processing module is used for respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category;

the determining module is used for determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table;

and the sending module is used for sending the state parameters to the terminal equipment so that the terminal equipment displays the states of all organs according to the state parameters.

In order to achieve the above object, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the physiological parameter data processing method as described above when executing the computer program.

In order to achieve the above object, the present invention also provides a computer-readable storage medium, in which a computer program is stored, the computer program being executable by at least one processor to cause the at least one processor to execute the steps of the physiological parameter data processing method as described above.

In order to achieve the above object, an embodiment of the present invention further provides a physiological parameter data processing system, where the physiological parameter data processing system includes a wearable device, a terminal device, and a server, where:

the wearable device is used for acquiring physiological parameter data of a user and uploading the acquired physiological parameter data to the server, wherein the physiological parameter data comprises at least two types of data of heart rate data, blood pressure data and blood oxygen data;

the server is used for analyzing the acquisition time of the physiological parameter data of each category uploaded by the wearable device so as to divide the physiological parameter data of each category into first weight data and second weight data; respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category; determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table; sending the state parameters to the terminal equipment;

and the terminal equipment is used for displaying the state of each organ according to the state parameters sent by the server.

According to the physiological parameter data processing method, the device, the system, the computer equipment and the computer readable storage medium provided by the embodiment of the invention, the physiological parameter data of the user is acquired by the wearable equipment, and then the acquisition time of the acquired physiological parameter data of each category is analyzed, so that the physiological parameter data of each category is divided into the first weight data and the second weight data; respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category; the state parameters of each organ are determined according to the physiological characteristic values corresponding to the physiological parameter data of each category and the preset organ state parameter table, so that the states of each organ of a user can be timely and accurately found, and meanwhile, when the physiological parameter data of each category are analyzed, the physiological parameter data of each category are divided into the first weight data and the second weight data, so that the data processing amount can be reduced, and the data processing efficiency is improved.

The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.

Drawings

FIG. 1 is a diagram of a hardware architecture of a physiological parameter data processing system according to the present invention;

FIG. 2 is a flowchart illustrating a method for processing physiological parameter data according to an embodiment of the present invention;

fig. 3 is a detailed schematic view of a first embodiment of the present invention illustrating a process of analyzing the acquisition time of the acquired physiological parameter data of each category to divide the physiological parameter data of each category into first weight data and second weight data;

fig. 4 is a detailed schematic diagram of a first embodiment of the present invention, illustrating a process flow of processing first weight data and second weight data of physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category;

fig. 5 is a detailed schematic view of a process flow of analyzing to-be-processed data of physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category according to a first embodiment of the present invention;

FIG. 6 is a flowchart illustrating steps of monitoring data amount in a physiological parameter data processing method according to an embodiment of the present invention;

fig. 7 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Technical solutions between various embodiments may be combined with each other, but must be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.

It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.

Fig. 1 shows a hardware architecture diagram of a physiological parameter data processing system according to the present invention. As shown in fig. 1, in an exemplary embodiment, the physiological parameter data processing system includes a wearable device 1, a terminal device 2, and a server 4. The wearable device 1 is used for acquiring physiological parameter data of a user and uploading the acquired physiological parameter data to the server 4, wherein the physiological parameter data comprises at least two types of data of heart rate data, blood pressure data and blood oxygen data;

the server 4 is configured to analyze the acquisition time of each category of physiological parameter data uploaded by the wearable device 1, so as to divide each category of physiological parameter data into first weight data and second weight data; respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category; determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table; sending the state parameters to the terminal device 2;

and the terminal device 2 is configured to display the state of each organ according to the state parameter sent by the server 4.

For example, the processing steps of the server 4 on the acquired physiological parameter data will be described in the following embodiments, which will not be described first.

It is noted that the wearable device 1, the terminal device 2 may communicate with the server 4 via a network 3, wherein the network 3 may comprise a wireless link, such as a cellular link, a satellite link, a Wi-Fi link, and/or the like. The wearable device 1 and the terminal device 2 in the embodiment of the present invention may include a plurality of devices, the terminal device 2 may be an electronic device such as a mobile phone and a computer, and the server 4 may be a rack server, a blade server, a tower server or a cabinet server (including an independent server or a server cluster formed by a plurality of servers).

Example one

Referring to fig. 2, a flowchart illustrating steps of a physiological parameter data processing method according to an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by taking a computer device as an execution subject, specifically as follows:

as shown in fig. 2, the physiological parameter data processing method may include steps S20 to S24, wherein:

step S20, acquiring physiological parameter data of the user collected by the wearable device, where the physiological parameter data includes at least two types of data among heart rate data, blood pressure data, and blood oxygen data.

For example, the wearable device may be a smart wearable device such as a smart watch, a smart bracelet, or smart glasses. The wearable device has the advantages of all-weather and high-frequency monitoring, a heart rate sensor, a blood pressure sensor, a blood oxygen sensor, a motion sensor, a body temperature sensor and the like can be integrated in the wearable device, and physiological parameter data of a user can be acquired in real time or at regular time through the sensors in the wearable device. In one embodiment of the present invention, the heart rate sensor utilizes light transmittance measurement (photoplethysmography) to measure the heart rate. The blood pressure sensor can detect the blood pressure related signals of the user, and in one embodiment of the invention, the blood pressure sensor is a photoelectric sensor, and the blood pressure value is obtained by utilizing the collected pulse waves and analyzing the pulse waves. The blood oxygen sensor can detect the blood oxygen related signals of a user, in one embodiment of the invention, the blood oxygen sensor is a reflection type photoelectric sensor, and the blood oxygen value is obtained by adopting an LED (light emitting diode) to emit light, using a CMOS (complementary metal oxide semiconductor) receiving end to receive reflected light and changing the reflected light. The motion state sensor may detect a motion related signal of the user, and in one embodiment of the present invention, the motion state sensor may be an acceleration sensor, such as a linear accelerometer (G sensor, gravity sensor).

In the embodiment of the invention, after the wearable device acquires the physiological parameters of the user, the acquired physiological parameters can be uploaded to the computer device in real time or at regular time through the communication module in the wearable device. The communication module can be a 2G module, a 4G module and the like.

It should be noted that the exercise data in the embodiment of the present invention refers to data reflecting the exercise condition of the user, and the number of steps taken by the user per day and the sleep condition of the user can be determined by the exercise data.

Step S21, analyzing the acquired collection time of each category of physiological parameter data, respectively, to divide the physiological parameter data of each category into first weight data and second weight data.

For example, assuming that the acquired physiological parameter data includes a heart rate, a diastolic blood pressure, a systolic blood pressure, and a blood oxygen saturation level of the user, and each piece of physiological parameter data includes an acquisition time, the acquisition times of all acquired heart rate data may be analyzed to divide all heart rate data into first weight data and second weight data. Similarly, the acquisition time of all the obtained diastolic pressure data, systolic pressure data and blood oxygen saturation data needs to be analyzed, so as to divide all the diastolic pressure data into first weight data and second weight data, divide all the systolic pressure data into first weight data and second weight data, divide all the blood oxygen saturation data into first weight data and second weight data, and divide all the motion data into first weight data and second weight data.

In an exemplary embodiment, referring to fig. 3, step S21 includes:

step S30, analyzing the acquisition time of each piece of acquired physiological parameter data of each category, respectively, executing step S31 if the acquisition time of the currently analyzed physiological parameter data is within a preset time period, and executing step S32 if the acquisition time of the currently analyzed physiological parameter data is not within the preset time period. After the acquisition time of the currently analyzed physiological parameter data is analyzed, the step of analyzing the acquisition time of each physiological parameter strip data in each category of physiological parameter data is repeated until all physiological parameter data in each category of physiological parameter data are divided.

In step S31, the currently analyzed physiological parameter data is used as one piece of physiological parameter data in the first weight data.

In step S32, the currently analyzed physiological parameter data is used as one piece of physiological parameter data in the second weight data.

Illustratively, after obtaining physiological parameter data of various categories, for example, 10 heart rate data, 10 diastolic pressure data, 10 systolic pressure data, and 10 blood oxygen saturation data, the acquisition times of the 10 heart rate data may be analyzed first, assuming that the acquisition times are t1, t2, t3, t4, t5, t6, t7, t8, t9, and t10 in sequence, and t1, t2, t3, t4, t5, t6, and t7 are located in a preset time period, and t8, t9, and t10 are not located in the preset time period, after analyzing the acquisition time of each heart rate data, the acquired heart rate data of t1, t2, t3, t4, t5, t6, and 7 may be used as first weight data, and the acquired heart rate data of t8, t9, t10 are used as second weight data.

Similarly, after the analysis of 10 pieces of heart rate data is completed, the acquisition times of 10 pieces of diastolic pressure data, 10 pieces of systolic pressure data, and 10 pieces of blood oxygen saturation data may be analyzed, respectively, so as to divide the 10 pieces of diastolic pressure data into first weight data and second weight data, divide the 10 pieces of systolic pressure data into first weight data and second weight data, and divide the 10 pieces of blood oxygen saturation data into first weight data and second weight data.

In the embodiment of the present invention, the preset time periods corresponding to the acquisition times of the physiological parameter data of each category may be the same or different, and in this embodiment, the preset time periods corresponding to the acquisition times of the physiological parameter data of each category are preferably the same. The preset time period is a preset time period, and the preset time period may be only one time period or may include a plurality of time periods, which is not limited in this embodiment.

In the embodiment of the invention, the physiological parameter data of each category are divided into the first weight data and the second weight data, so that the data can be selected and processed according to the importance of the data in the first weight data and the second weight data, the data processing amount is reduced, and the data processing efficiency is improved.

Step S22, the first weight data and the second weight data of the physiological parameter data of each category are processed respectively to obtain physiological characteristic values corresponding to the physiological parameter data of each category.

For example, assuming that the acquired physiological parameter data includes a heart rate, a diastolic pressure, a systolic pressure, and a blood oxygen saturation level of the user, the first weight data and the second weight data of the heart rate data may be processed to obtain a physiological characteristic value corresponding to the heart rate data.

Similarly, the first weight data and the second weight data of the diastolic pressure data can be processed to obtain a physiological characteristic value corresponding to the diastolic pressure data; the first weight data and the second weight data of the systolic pressure data can be processed to obtain a physiological characteristic value corresponding to the systolic pressure data; the first weight data and the second weight data of the blood oxygen saturation data can be processed to obtain a physiological characteristic value corresponding to the blood oxygen saturation data; the first weight data and the second weight data of the motion data can be processed to obtain a physiological characteristic value corresponding to the motion data.

The physiological characteristic value is a value representing a physiological health condition, such as "early warning", "health", "sub-health", and the like.

In an exemplary embodiment, referring to fig. 4, the step S22 includes:

in step S40, weight values corresponding to the first weight data and the second weight data of the physiological parameter data of each category are obtained.

For example, the database stores the weight values corresponding to the first weight data and the second weight data of physiological parameter data of each category in advance, for example, the weight values corresponding to the first weight data and the second weight data of heart rate data are respectively 0.8 and 0.2; the weight values corresponding to the first weight data and the second weight data for storing the diastolic pressure data are respectively 0.6 and 0.4; the weight values corresponding to the first weight data and the second weight data for storing the systolic pressure data are respectively 0.6 and 0.4; the first weight data and the second weight data of the stored blood oxygen saturation data have weight values of 0.5 and 0.5, respectively.

In another embodiment of the present invention, the first weight data and the second weight data of the physiological parameter data of each category may have the same weight value, for example, the first weight data and the second weight data have the same weight value of 0.5.

In step S41, physiological parameter data of a data amount corresponding to the acquired weight value is selected from the first weight data and the second weight data of the physiological parameter data of each category according to the acquired weight value.

For example, assuming that the first weight data and the second weight data of the acquired heart rate data have corresponding weight values of 0.8 and 0.2, respectively, 80% of the heart rate data may be selected from the first weight data of the heart rate data, and 20% of the heart rate data may be selected from the second weight data of the heart rate data. For example, if the first weight data of the heart rate data includes 100 pieces of heart rate data, 80 pieces of heart rate data can be selected from the heart rate data; if the second weight data of the heart rate data comprises 40 pieces of heart rate data, 8 pieces of heart rate data can be selected from the heart rate data.

Similarly, if the first weight data and the second weight data of the diastolic pressure data have weight values of 0.6 and 0.4, respectively, 60% of the diastolic pressure data may be selected from the first weight data of the diastolic pressure data, and 40% of the diastolic pressure data may be selected from the second weight data of the diastolic pressure data; if the first weight data and the second weight data of the systolic blood pressure data have weight values of 0.6 and 0.4, respectively, 60% of the systolic blood pressure data may be selected from the first weight data of the diastolic blood pressure data, and 40% of the systolic blood pressure data may be selected from the second weight data of the diastolic blood pressure data.

In step S42, the selected physiological parameter data is used as the data to be processed of the physiological parameter data of each category.

For example, after the selection of physiological parameter data of each category is completed, the selected physiological parameter data can be respectively used as the data to be processed of the physiological parameter data of each category.

Step S43, analyzing the to-be-processed data of the physiological parameter data of each category respectively to obtain physiological characteristic values corresponding to the physiological parameter data of each category.

For example, it is assumed that the to-be-processed data of the heart rate data includes 150 pieces of heart rate data, the to-be-processed data of the diastolic pressure data includes 180 pieces of diastolic pressure data, the to-be-processed data of the systolic pressure data includes 180 pieces of systolic pressure data, and the to-be-processed data of the blood oxygen saturation data includes 200 pieces of blood oxygen saturation data. Analyzing the 150 pieces of heart rate data to obtain physiological characteristic values corresponding to the heart rate data; analyzing the 180 pieces of systolic pressure data to obtain physiological characteristic values corresponding to the systolic pressure data; analyzing 180 diastolic pressure data to obtain physiological characteristic values corresponding to the diastolic pressure data; and analyzing the 200 pieces of blood oxygen saturation data to obtain physiological characteristic values corresponding to the blood oxygen saturation data.

In an exemplary embodiment, referring to fig. 5, step S43 includes:

step S50, obtaining standard physiological parameter values corresponding to the physiological parameter data of each category, where the standard physiological parameter values include multiple ones.

Step S51, calculating the ratio of the physiological parameter data in the to-be-processed data of the physiological parameter data of each category in a plurality of preset standard physiological parameter value intervals.

And step S52, determining physiological characteristic values corresponding to the physiological parameter data of each category according to the calculated ratio.

For example, a standard physiological parameter value corresponding to each category of physiological parameter data, a standard physiological parameter value interval corresponding to each category of physiological parameter data, and a physiological characteristic value corresponding to a physiological parameter data ratio of each category of physiological parameter data are preset.

It should be noted that, in the embodiment of the present invention, the standard physiological parameter value and the standard physiological parameter value interval may be set according to actual conditions, or may be modified according to specific conditions after the setting is completed, which is not limited in the embodiment of the present invention.

In an exemplary application scenario, the standard physiological parameter values for setting the heart rate are A1, A2 and A3

Setting a corresponding physiological characteristic value as 'early warning' when the ratio of the frequency of the heart rate frequency less than or equal to A1 to the total measurement frequency of the heart rate is more than 70%;

setting a physiological characteristic value corresponding to the ratio of the times of heart rate frequency being more than A2 to the total heart rate measurement times being more than 70% as early warning;

setting the physiological characteristic value corresponding to the ratio of the times of heart rate frequency being less than or equal to A1 to the total heart rate measurement times being more than 50% and less than or equal to 70% as sub-healthy;

setting the physiological characteristic value corresponding to the condition that the ratio of the times of heart rate frequency being more than A2 to the total heart rate measurement times is more than 50% and less than or equal to 70% as sub-healthy;

the physiological characteristic value corresponding to the heart rate frequency being more than A1 and the frequency being less than A2 accounting for more than 50% of the total measurement frequency is set as "healthy".

In the embodiment of the invention, after each heart rate value in the acquired to-be-processed data of the heart rate data is obtained, the statistical analysis can be performed on each heart rate value, then the proportion of the heart rate value in each standard physiological parameter interval is calculated, and finally the physiological characteristic value corresponding to the current heart rate data can be determined according to the proportion.

In another exemplary application scenario, systolic blood pressure standard physiological parameter values of blood pressure can be set as B1, B2, B3, B4, B5; the standard physiological parameter values of the diastolic blood pressure are C1, C2, C3 and C4.

Setting the physiological characteristic value corresponding to the ratio of the times of the systolic pressure being greater than or equal to B1 and less than or equal to B2 and being greater than 70% as 'early warning';

setting the physiological characteristic value corresponding to the condition that the frequency ratio of diastolic pressure is more than or equal to C1 and less than or equal to C2 is more than 70% as 'early warning';

setting the physiological characteristic value corresponding to the times that the systolic pressure is greater than or equal to B1 and less than or equal to B2 in the ratio of greater than 50% and less than or equal to 70% as sub-healthy;

setting the physiological characteristic value corresponding to the times that the diastolic blood pressure is greater than or equal to C1 and less than or equal to C2 in the ratio of greater than 50% and less than or equal to 70% as 'sub-healthy';

setting the physiological characteristic value corresponding to the frequency ratio of the systolic pressure being greater than or equal to B3 and being greater than 60% as early warning;

setting the physiological characteristic value corresponding to the diastolic pressure being more than or equal to C3 and the frequency ratio being more than 60% as early warning;

setting the physiological characteristic value corresponding to the time ratio of the systolic pressure being greater than or equal to B3 and being greater than 50% and less than or equal to 60% as 'early warning';

setting the physiological characteristic value corresponding to the diastolic pressure of more than or equal to C3 and more than 50% and less than or equal to 60% as early warning;

setting a physiological characteristic value corresponding to the condition that the frequency proportion of the systolic pressure is less than B4 is more than 70% as early warning;

setting a physiological characteristic value corresponding to that the frequency percentage of diastolic pressure is less than C4 is more than 70% as early warning;

setting physiological characteristic values of healthy corresponding to the systolic pressure of B4 or more and B5 or less, the diastolic pressure of C4 or more and the times of C1 or less exceeding 50%;

in the embodiment of the invention, after acquiring each systolic pressure and diastolic pressure in the data to be processed of the systolic pressure and diastolic pressure values, the statistical analysis can be performed on each systolic pressure and diastolic pressure value, then the ratio of the systolic pressure and diastolic pressure values in each standard physiological parameter interval is calculated, and finally the physiological characteristic value corresponding to the current systolic pressure and diastolic pressure value can be determined according to the ratio.

In another exemplary application scenario, the standard physiological parameter values of the pre-blood oxygenation are D1, D2, D3, D4.

Setting a physiological characteristic value corresponding to the condition that the ratio of the number of times that the blood oxygen saturation is less than D1 to the total blood oxygen examination number is more than 70% as 'early warning';

setting the physiological characteristic value corresponding to the condition that the ratio of the blood oxygen saturation degree is less than D2 to the total blood oxygen examination frequency is more than 50% and less than or equal to 70% as early warning;

setting the physiological characteristic value corresponding to the condition that the blood oxygen saturation is less than D2, and the ratio of the times of D1 to the total blood oxygen examination times is more than 70% as sub-healthy;

setting the physiological characteristic value corresponding to the blood oxygen saturation degree of less than D2, the frequency of more than or equal to D1 accounts for more than 50% of the total blood oxygen examination frequency, and the frequency of less than or equal to 70% as 'early warning';

setting the physiological characteristic value corresponding to the condition that the blood oxygen saturation is less than D3, and the ratio of the times of D2 to the total blood oxygen examination times is more than 70% as sub-healthy;

setting the physiological characteristic value of the blood oxygen saturation degree to be less than D3, wherein the times of D2 or more account for more than 50% of the total blood oxygen examination times, and the physiological characteristic value of the blood oxygen saturation degree to be less than or equal to 70% is 'healthy';

setting the physiological characteristic value corresponding to the condition that the blood oxygen saturation is less than or equal to D4, and the ratio of the times of more than or equal to D3 to the total blood oxygen examination times is more than 50% as 'healthy';

in the embodiment of the invention, after acquiring each blood oxygen saturation degree data in the to-be-processed data of the blood oxygen saturation degree, the statistical analysis can be carried out on each blood oxygen saturation degree, then the proportion of the blood oxygen saturation degree in each standard physiological parameter interval is calculated, and finally the physiological characteristic value corresponding to the current blood oxygen saturation degree value can be determined according to the proportion.

In another exemplary application scenario, when the motion data of the user is collected by the wearable device, the standard physiological parameter value within the sleep fixed time period may also be set as E1, E2.

Setting the physiological characteristic value corresponding to the sleep time < E1 as sub-healthy;

setting the physiological characteristic value corresponding to E1< sleep time ═ E2 as 'healthy';

setting the physiological characteristic value corresponding to the sleep time > E2 as sub-healthy;

in the embodiment of the invention, after the movement data is acquired, whether the user is in a sleep state and the sleep time of the user are determined according to the acquired movement data. After the sleep time is obtained, the standard physiological parameter value interval to which the sleep time belongs can be determined according to the obtained sleep time, and then the physiological characteristic value corresponding to the exercise data is determined according to the obtained standard physiological parameter interval.

In another embodiment, when the motion data of the user is collected by the wearable device, the standard physiological parameter value of the step number of the user can be set to be S.

Setting the physiological characteristic value corresponding to the step number smaller than S every day as sub-health;

setting the physiological characteristic value of which the number of steps per day is greater than or equal to S as 'healthy';

in the embodiment of the invention, after the step number data of the user is acquired, the physiological characteristic value corresponding to the step number of the user can be determined according to the step number.

Step S23, determining the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and the preset organ state parameter table.

For example, an organ state table is stored in the database in advance, and the organ state table includes a mapping relationship between a state parameter of each organ and a physiological characteristic value corresponding to physiological parameter data of each category. For example, the organ state table includes heart rate "healthy", systolic pressure "early warning", diastolic pressure "healthy", blood oxygen saturation "sub-healthy" corresponding to lymph "normal", liver "normal", and heart "risk 1", and for example, heart rate "sub-healthy", systolic pressure "normal", diastolic pressure "healthy", blood oxygen saturation "healthy" corresponding to lymph "risk 1", liver "normal", and heart "normal".

In the embodiment of the invention, after the physiological characteristic value corresponding to the physiological parameter data of each category is obtained, the state parameter of each organ can be obtained by inquiring the organ state parameter table.

Note that the organs in the present embodiment include, but are not limited to, lymph, spine, liver, gallbladder, pericardium, heart, lung, spleen, stomach, large intestine, small intestine, kidney, and the like.

Step S24, sending the status parameters to a terminal device, so that the terminal device displays the status of each organ according to the status parameters.

For example, after obtaining the state parameters of each organ, the state parameters may be sent to the terminal device, so that the terminal device may display the state of each organ according to the state parameters, and thus, the user may query the state of each organ of the user through the terminal device, so that the user may know the physical condition of the user in time according to the state of each organ.

In an embodiment, when the state parameter is sent to the terminal device, in order to enable the user to view the state parameter in time, a notification message may be sent to remind the user to view the state of each organ when the state parameter is sent to the terminal device. The notification message can be an APP notification, a short message prompt and other methods for prompting the user.

In the embodiment, the physiological parameter data of the user is acquired through the wearable device, and then the acquired physiological parameter data of each category is divided into the first weight data and the second weight data by analyzing the acquisition time of the acquired physiological parameter data of each category; respectively processing the first weight data and the second weight data of the physiological parameter data of each category to obtain physiological characteristic values corresponding to the physiological parameter data of each category; the state parameters of each organ are determined according to the physiological characteristic values corresponding to the physiological parameter data of each category and the preset organ state parameter table, so that the states of each organ of a user can be timely and accurately found, and meanwhile, when the physiological parameter data of each category are analyzed, the physiological parameter data of each category are divided into the first weight data and the second weight data, so that the data processing amount can be reduced, and the data processing efficiency is improved.

In an exemplary embodiment, before the step of analyzing the acquisition times of the acquired physiological parameter data of the respective categories respectively to divide the physiological parameter data of the respective categories into the first weight data and the second weight data, the method further includes:

judging whether the data volume of the acquired physiological parameter data of each category is greater than or equal to a preset threshold value, if so, executing the step of analyzing the acquisition time of the acquired physiological parameter data of each category respectively to divide the physiological parameter data of each category into first weight data and second weight data, if the data volume of the acquired physiological parameter data of each category is less than the preset threshold value, not executing the step of analyzing the acquisition time of the acquired physiological parameter data of each category respectively to divide the physiological parameter data of each category into the first weight data and the second weight data until the data volume of the acquired physiological parameter data of each category is greater than or equal to the preset threshold value, the step of analyzing the acquired acquisition time of the physiological parameter data of each category respectively to divide the physiological parameter data of each category into first weight data and second weight data is executed.

Illustratively, the preset threshold is a preset number, for example, 1000. The data amount is the number of pieces of physiological parameter data.

In the embodiment of the invention, the acquired data is analyzed only when the data volume of the acquired physiological parameter data of each category is greater than or equal to the preset threshold value, so that the problem of inaccurate finally obtained state parameters caused by insufficient data volume can be avoided.

For example, since the data volume of the acquired physiological parameter data is relatively large, in order to improve the data processing speed, in the embodiment of the present invention, when analyzing the physiological parameter data, the number of CPUs for invoking and processing the physiological parameter data may be determined according to the data volume of the physiological parameter data to be analyzed, for example, if the data volume of the physiological parameter data to be analyzed is 10G, 10 CPUs may be invoked to process the physiological parameter data to be analyzed; for another example, if the data size of the physiological parameter data to be processed is 20G, 15 CPUs may be called to process the physiological parameter data to be analyzed.

In the embodiment of the invention, the physiological parameter data is processed by calling the number of CPUs (central processing units) with different data according to different data volumes, so that the data processing efficiency can be improved.

Example two

With reference to fig. 6, a schematic diagram of program modules of the physiological parameter data processing device according to the present invention is shown. In this embodiment, the physiological parameter data processing device 600 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the above-described physiological parameter data processing method. The program modules referred to in the embodiments of the present invention refer to a series of computer program instruction segments capable of performing specific functions, and are better suited than the program itself for describing the execution process of the physiological parameter data processing apparatus 600 in the storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:

the acquiring module 601 is configured to acquire physiological parameter data of a user acquired by a wearable device, where the physiological parameter data includes at least two types of data among heart rate data, blood pressure data, and blood oxygen data.

The analysis module 602 is configured to analyze the acquired acquisition time of each category of physiological parameter data, so as to divide the physiological parameter data of each category into first weight data and second weight data.

In an exemplary embodiment, the analysis module 602 is further configured to analyze the acquisition time of each piece of acquired physiological parameter data in each category, if the acquisition time of the currently analyzed physiological parameter data is within a preset time period, use the currently analyzed physiological parameter data as one piece of physiological parameter data in the first weight data, and if the acquisition time of the currently analyzed physiological parameter data is not within the preset time period, use the currently analyzed physiological parameter data as one piece of physiological parameter data in the second weight data.

Illustratively, after the analysis of the acquisition time of the currently analyzed physiological parameter data is completed, the above-mentioned step of analyzing the acquisition time of each physiological parameter data in the physiological parameter data of each category is repeated until all the physiological parameter data in the physiological parameter data of each category are divided.

After physiological parameter data of each category is obtained, for example, 10 pieces of heart rate data, 10 pieces of diastolic pressure data, 10 pieces of systolic pressure data, and 10 pieces of blood oxygen saturation data are obtained, the acquisition time of the 10 pieces of heart rate data may be analyzed first, and assuming that the acquisition time is t1, t2, t3, t4, t5, t6, t7, t8, t9, and t10 in sequence, and t10, t10 are located in a preset time period, and t10, and t10 are not located in the preset time period, after the acquisition time of each piece of heart rate data is analyzed, the heart rate data acquired by t10, and t10 may be used as first weight data, and the heart rate data acquired by t10, t10 may be used as second weight data.

The processing module 603 is configured to process the first weight data and the second weight data of the physiological parameter data of each category, respectively, to obtain a physiological characteristic value corresponding to the physiological parameter data of each category.

Illustratively, the processing module 603 is further configured to obtain weight values of the first weight data and the second weight data of the physiological parameter data of each category.

The processing module 603 is further configured to select, according to the obtained weight values, physiological parameter data of a data amount corresponding to the obtained weight values from the first weight data and the second weight data of the physiological parameter data of each category.

The processing module 603 is further configured to use the selected physiological parameter data as data to be processed of physiological parameter data of each category.

The processing module 603 is further configured to analyze the data to be processed of the physiological parameter data of each category, respectively, to obtain physiological characteristic values corresponding to the physiological parameter data of each category.

In an exemplary embodiment, the processing module 603 is further configured to obtain a plurality of standard physiological parameter values corresponding to physiological parameter data of each category; calculating the ratio of physiological parameter data in the to-be-processed data of the physiological parameter data of each category in a plurality of preset standard physiological parameter value intervals; and determining physiological characteristic values corresponding to the physiological parameter data of each category according to the calculated ratio.

The determining module 604 is configured to determine the state parameters of each organ according to the physiological characteristic values corresponding to the physiological parameter data of each category and a preset organ state parameter table.

A sending module 605, configured to send the state parameter to a terminal device, so that the terminal device displays the states of the organs according to the state parameter.

In an exemplary embodiment, the physiological parameter data processing apparatus 600 further includes:

and the judging module is used for judging whether the data volume of the acquired physiological parameter data of each category is greater than or equal to a preset threshold value.

For example, if the data amount of the acquired physiological parameter data of each category is greater than or equal to the preset threshold, analyzing the acquisition time of the acquired physiological parameter data of each category respectively to divide the physiological parameter data of each category into first weight data and second weight data, if the data volume of the acquired physiological parameter data of each category is less than the preset threshold value, the acquisition time of the acquired physiological parameter data of each category is not analyzed so as to divide the physiological parameter data of each category into first weight data and second weight data, the acquisition time of the acquired physiological parameter data of each category is not analyzed until the data amount of the acquired physiological parameter data of each category is greater than or equal to the preset threshold value, so as to divide the physiological parameter data of each category into a first weight data and a second weight data.

The preset threshold is a preset number, for example, 1000. The data amount is the number of pieces of physiological parameter data.

and the calling module is used for determining the number of called CPUs according to the data volume of the acquired physiological parameter data of each category.

EXAMPLE III

Fig. 7 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In this embodiment, the computer device 7 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 7 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown in fig. 7, the computer device 7 includes, but is not limited to, at least a memory 21, a processor 22, and a network interface 23, which are communicatively connected to each other through a system bus. Wherein:

in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 7, such as a hard disk or a memory of the computer device 7. In other embodiments, the memory 21 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device 7. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 7 and an external storage device thereof. In the present embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 7 and various types of application software, such as the program codes of the physiological parameter data processing apparatus 600 of the above-mentioned embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.

Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other physiological parameter data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 7. In the present embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, execute the physiological parameter data processing apparatus 600, so as to implement the physiological parameter data processing method of the above-mentioned embodiment.

The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is typically used for establishing a communication connection between the computer device 7 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 7 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 7 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.

It is noted that fig. 7 only shows a computer device 7 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.

In this embodiment, the physiological parameter data processing device 600 stored in the memory 21 can be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to complete the present invention.

Example four

The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the embodiment is used for storing the physiological parameter data processing device 600, and when being executed by the processor, the physiological parameter data processing method of the embodiment is implemented.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. A method of processing physiological parameter data, comprising:

2. The method according to claim 1, wherein analyzing the acquisition time of the acquired physiological parameter data of each category to divide the physiological parameter data of each category into first weight data and second weight data comprises:

3. The method as claimed in claim 1, wherein the processing the first weight data and the second weight data of the physiological parameter data of each category respectively to obtain the physiological characteristic value corresponding to the physiological parameter data of each category comprises:

4. The method according to claim 3, wherein the analyzing the data to be processed of the physiological parameter data of each category to obtain the physiological characteristic value corresponding to the physiological parameter data of each category comprises:

5. The method according to claim 1, wherein before the step of analyzing the acquisition time of the acquired physiological parameter data of each category to divide the physiological parameter data of each category into the first weight data and the second weight data, the method further comprises:

6. The method according to claim 1, wherein before the step of analyzing the acquisition time of the acquired physiological parameter data of each category to divide the physiological parameter data of each category into the first weight data and the second weight data, the method further comprises:

7. A physiological parameter data processing apparatus, comprising:

8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the physiological parameter data processing method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor for causing the at least one processor to carry out the steps of the method of processing physiological parameter data according to any one of claims 1 to 6.

10. A physiological parameter data processing system is characterized by comprising a wearable device, a terminal device and a server, wherein: