CN115281646B - Dynamic heart rate monitoring method and system based on intelligent wearable device - Google Patents

Abstract

The present invention provides a dynamic heart rate monitoring method and system based on smart wearable devices, which are used in the field of data monitoring; collect the first heart rate data of the human body; judge whether the first heart rate data reaches the preset data peak-valley value, and if so, set the The first heart rate data is input to the preset prediction model for prediction, and the second heart rate data corresponding to the prediction time period is obtained; the predicted peak-to-valley value of the second heart rate data is obtained; and it is judged whether the predicted peak-to-valley value reaches the data peak-to-valley value; if it reaches , the data information of the human health index corresponding to the second heart rate data is collected; the data information of the human health index is input into the prediction model to obtain the data analysis result of the human body; Abnormal, but the smart wearable device is not detected in time, and by predicting the heart rate activity of the human body, it can effectively prevent the occurrence of sudden diseases.

Description

Dynamic heart rate monitoring method and system based on smart wearable devices

technical field

The invention relates to the field of data monitoring, in particular to a dynamic heart rate monitoring method and system based on smart wearable devices.

Background technique

In the prior art, an MDSP (Modem Digital Signal Processor, modem digital signal processor) is usually provided in a chip of a smart wearable device. After the heart rate monitoring device in the smart wearable device (such as a smart watch) collects the user's heart rate information, it will send the collected raw data to the MDSP, and then the MDSP will process the received data, and then the MDSP will process the received data. The obtained user's heart rate data is sent to the CPU of the smart wearable device.

However, the heart rate monitoring device of this smart wearable device cannot continuously collect the human body's 24-hour continuous heart rate HRV heart rate abnormality index, resulting in a problem that the human body may have an abnormal heart rate index during a certain period of time, but the smart wearable device has not detected it in time.

Contents of the invention

The invention aims to solve the problem that the human body may have an abnormal heart rate index in a certain period of time, but the smart wearable device does not detect it in time, and provides a dynamic heart rate monitoring method and system based on the smart wearable device.

The present invention adopts following technical means for solving technical problems:

The present invention provides a dynamic heart rate monitoring method based on an intelligent wearable device, comprising the following steps:

Using a preset continuous heart rate sensor to collect first heart rate data of the human body, wherein the first heart rate data includes resting heart rate data and exercise heart rate data;

Judging whether the first heart rate data reaches a preset data peak-to-valley value, wherein the data peak-to-valley value includes a data peak value and a data valley value;

If it is reached, then input the first heart rate data into a preset prediction model for prediction, and obtain the second heart rate data corresponding to the prediction time period;

Acquiring predicted peak-to-valley values of the second heart rate data;

judging whether the predicted peak-to-valley value reaches the data peak-to-valley value;

If it is achieved, then collect the human health index data information corresponding to the second heart rate data;

Input the data information of the human body health index into the prediction model to obtain the data analysis result of the human body, wherein the data analysis result includes the human heart rate waveform data and the health index report.

Further, before the step of using the preset continuous heart rate sensor to collect the first heart rate data of the human body, it includes:

Capturing performance generated by preset sensors, wherein the performance includes temperature data and tactile data;

judging whether the performance meets the preset enabling conditions;

If yes, collect physical data of the human body, wherein the physical data includes electrocardiographic data, blood pressure data, and blood oxygen data.

Further, the step of using a preset continuous heart rate sensor to collect the first heart rate data of the human body includes:

Acquiring the rest time period and exercise time period of the human body;

Correspondingly classifying resting heart rate data and exercise heart rate data in the first heart rate data based on the resting time period and the exercise time period;

Collecting the peak-to-valley values of the resting heart rate data and the peak-to-valley values of the exercise heart rate data.

Further, before the step of inputting the first heart rate data into a preset prediction model for prediction, and obtaining the second heart rate data corresponding to the prediction time period, it includes:

Collect heart rate health data and health index data, wherein the health data includes heart rate interval data and heart rate variability interval data, and the health index data includes oxygen endurance, oxygen uptake, and oxygen consumption;

The health data and the health index data are used as training samples, input into a training model for training, and a trained prediction model is obtained.

Further, the step of inputting the first heart rate data into a preset prediction model for prediction, and obtaining the second heart rate data corresponding to the prediction time period includes:

Obtain part of the first heart rate data for a preset time period;

Collect health values corresponding to the part of the first heart rate data, wherein the health values include heart rate interval values and heart rate variability interval values;

Prediction is performed based on the preset time period and the health value to obtain predicted heart rate data of the human body at the same time as the preset time period.

Further, the step of acquiring the predicted peak-valley value of the second heart rate data includes:

Obtaining the duration of the predicted peak-to-valley value;

judging whether the duration is less than a preset time period;

If so, classifying the second heart rate data based on the predicted peak-to-valley value to obtain resting heart rate data and exercise heart rate data corresponding to the second heart rate data.

Further, the step of inputting the human body health index data information into the predictive model to obtain the data analysis results of the human body includes:

Obtain the comprehensive risk assessment score in the data information of the human health index;

judging whether the comprehensive risk assessment score is greater than a preset score evaluation interval;

If yes, a corresponding risk assessment report is established based on the human health index data information.

Further, after the step of inputting the human body health index data information into the prediction model and obtaining the data analysis results of the human body, it includes:

acquiring heart rate interval data corresponding to the second heart rate data;

Judging whether the heart rate interval data conforms to a preset heart rate pattern;

If so, comparing the second heart rate data with the first heart rate data to form a comparison of human heart rate waveform data in different time periods.

The present invention also provides a dynamic heart rate monitoring system based on smart wearable devices, including:

The first collection module is used to collect the first heart rate data of the human body by using a preset continuous heart rate sensor, wherein the first heart rate data includes resting heart rate data and exercise heart rate data;

A first judgment module, configured to judge whether the first heart rate data reaches a preset data peak-to-valley value, wherein the data peak-to-valley value includes a data peak value and a data valley value;

The first execution module is configured to input the first heart rate data into a preset prediction model for prediction if it is reached, and obtain the second heart rate data corresponding to the prediction time period;

A first acquisition module, configured to acquire the predicted peak-to-valley value of the second heart rate data;

The second judging module is used to judge whether the predicted peak-to-valley value reaches the data peak-to-valley value;

The second execution module is configured to collect the human health index data information corresponding to the second heart rate data if it is achieved;

The first analysis module is configured to input the data information of the human body health index into the prediction model to obtain the data analysis result of the human body, wherein the data analysis result includes human heart rate waveform data and a health index report.

Further, it also includes:

The first capture module is configured to capture performance generated by preset sensors, wherein the performance includes temperature data and touch data;

A third judging module, configured to judge whether the performance meets a preset enabling condition;

The third execution module is configured to, if yes, collect physical data of the human body, wherein the physical data includes electrocardiographic data, blood pressure data and blood oxygen data.

The invention provides a dynamic heart rate monitoring method and system based on an intelligent wearable device, which has the following beneficial effects:

In the present invention, after collecting the heart rate data of a certain time period of the human body, the heart rate data is input into the prediction model for prediction, and another heart rate data corresponding to the time period is obtained, and the heart rate data of two different time periods are compared , to generate the human body's heart rate waveform data and the corresponding health indicator report, which effectively reduces the possibility that the human body may have an abnormal heart rate index in a certain period of time, but the smart wearable device does not detect it in time, and by predicting the human body's heart rate activity It can effectively prevent the occurrence of sudden diseases.

Description of drawings

Fig. 1 is the schematic flow chart of an embodiment of the dynamic heart rate monitoring method based on smart wearable device of the present invention;

Fig. 2 is a structural block diagram of an embodiment of the dynamic heart rate monitoring system based on the smart wearable device of the present invention.

Detailed ways

It should be understood that the specific embodiments described here are only used to explain the present invention, and are not used to limit the present invention. The realization of the purpose, functions and advantages of the present invention will be further explained with reference to the accompanying drawings in combination with the embodiments.

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

With reference to accompanying drawing 1, be the dynamic heart rate monitoring method based on smart wearable device in an embodiment of the present invention, comprise:

S1: Using a preset continuous heart rate sensor to collect first heart rate data of the human body, wherein the first heart rate data includes resting heart rate data and exercise heart rate data;

S2: judging whether the first heart rate data reaches a preset data peak-valley value, wherein the data peak-valley value includes a data peak-valley value and a data valley value;

S3: If it is reached, input the first heart rate data into a preset prediction model for prediction, and obtain the second heart rate data corresponding to the prediction time period;

S4: Obtain the predicted peak-to-valley value of the second heart rate data;

S5: judging whether the predicted peak-to-valley value reaches the data peak-to-valley value;

S6: If it is achieved, collect the human health index data information corresponding to the second heart rate data;

S7: Input the data information of the human body health index into the prediction model to obtain the data analysis result of the human body, wherein the data analysis result includes human body heart rate waveform data and a health index report.

In this embodiment, the system uses the continuous heart rate sensor preset in the smart wearable device to collect the first heart rate data of the human body for a certain period of time, including (resting heart rate data and exercise heart rate data), and then the system judges the first Whether the heart rate data can reach the pre-set heart rate data peak value, including (heart rate data peak value and heart rate data valley value), so as to perform different corresponding steps; for example, the system judges that the first heart rate data has not reached the pre-set value For example, the highest heart rate in the first heart rate data is 78 beats/min, the lowest heart rate is 65 beats/min, while the pre-set data peak-valley value is 80 beats/min, and the lowest heart rate is 62 beats/min. time/minute, then the system will directly generate the human health index data information corresponding to the first heart rate data, and input the human health index data information into the prediction model to obtain the human heart rate waveform corresponding to the first heart rate data Data and health indicator reports; for example, the system judges that the first heart rate data has reached the pre-set data peak and valley values, such as the highest heart rate in the first heart rate data is 81 beats/min, and the lowest heart rate is 60 beats/min, When the pre-set data peak-to-valley value is known to be 80 beats/min and the lowest heart rate is 62 beats/min, the system will input the first heart rate data into the pre-set prediction model for prediction , to predict the second heart rate data at the same time period as the first heart rate data but at a different time; For example, if the system judges that the predicted peak-to-valley value has reached the data peak-to-valley value, then the system will collect the human health index data information corresponding to the predicted second heart rate data, including (oxygen endurance, oxygen uptake and oxygen Consumption), and input these human health index data information into the prediction model for prediction, so as to predict the data analysis results of the human body, including (human heart rate waveform data and health index report), the data analysis of the first heart rate data Comparing the results with the data analysis results of the second heart rate data can effectively reduce the possibility that the human body may have an abnormal heart rate index in a certain period of time, but the smart wearable device does not detect it in time, and by predicting the heart rate activity of the human body, it can effectively Prevent the occurrence of sudden diseases.

In this embodiment, using a preset continuous heart rate sensor, before the step S1 of collecting the first heart rate data of the human body, includes:

S101: Capture performance generated by a preset sensor, wherein the performance includes temperature data and touch data;

S102: Determine whether the performance meets a preset enabling condition;

S103: If yes, collect physical data of the human body, where the physical data includes electrocardiographic data, blood pressure data, and blood oxygen data.

In this embodiment, the system captures the functions preset in the smart wearable device, including (temperature data and touch data), and judges whether these performances meet the preset conditions for enabling the function of the smart wearable device, so as to perform corresponding different functions. Steps; for example, the system judges that these performances meet the pre-set conditions for enabling smart wearable devices. For example, when wearing a smart wearable device, the human body temperature needs to reach the interval value of 30-40°C, and the sensor of the smart wearable device acquires the temperature and reaches This interval value can enable the smart wearable device to enable the heart rate monitoring function; for example, when wearing a smart wearable device, the touch range set by the sensor of the smart wearable device is touched by the human body for a period of time (for example, the smart wearable ring is worn on the finger of the human body) , the smart wearable watch is worn on the human wrist), at this time the smart wearable device will enable the heart rate monitoring function, and can also monitor the human body's physical data such as (ECG data, blood pressure data and blood oxygen data), and included in In the smart wearable device; for example, the system judges that these performances do not meet the pre-set conditions for enabling the smart wearable device, that is, the smart wearable device will not work at this time, that is, it cannot monitor the heart rate normally.

In this embodiment, the step S1 of collecting the first heart rate data of the human body by using a preset continuous heart rate sensor includes:

S11: Obtain the rest time period and exercise time period of the human body;

S12: Correspondingly classify resting heart rate data and exercise heart rate data in the first heart rate data based on the rest time period and the exercise time period;

S13: Collect peak-to-valley heart rate values of the resting heart rate data and peak-to-valley heart rate values of the exercise heart rate data.

In this embodiment, after acquiring the resting time period and the exercise time period of the human body, the system correspondingly classifies the different heart rates in the first heart rate data as resting Time period and exercise time period to obtain resting heart rate data and exercise heart rate data. At the same time, the system will collect the peak and valley values of the two heart rate data for comparison with the pre-set data peak and valley values.

In this embodiment, the first heart rate data is input into a preset prediction model for prediction, and before step S3 of obtaining the second heart rate data corresponding to the prediction time period, it includes:

S301: Collect heart rate health data and health index data, wherein the health data includes heart rate interval data and heart rate variability interval data, and the health index data includes oxygen endurance, oxygen uptake, and oxygen consumption;

S302: Input the health data and the health index data as training samples into a training model for training to obtain a trained prediction model.

In this embodiment, the system collects optimal heart rate health data and health index data on the Internet, including (heart rate interval data and heart rate variability interval data), and inputs these data into a blank training model for training, You can get a prediction model that takes the best heart rate data as the center value and can predict the heart rate pattern in the next period of time. You only need to input the heart rate data of a certain period of time into the prediction model, and you can get it through the prediction model. The heart rate data of the next time period connected to this time period, for example, the collected first heart rate data belongs to the heart rate law data within a week, and the heart rate law data input into the prediction model for prediction belongs to the heart rate law data of the next week; For example, the collected first heart rate data belongs to the heart rate data within three days, then the heart rate data input to the prediction model for prediction also belongs to the heart rate data after three days, which means the predicted heart rate data and the input heart rate data time period Proportional.

In this embodiment, the first heart rate data is input into a preset prediction model for prediction, and the step S3 of obtaining the second heart rate data corresponding to the prediction time period includes:

S31: Obtain part of the first heart rate data for a preset time period;

S32: Collect a health value corresponding to the part of the first heart rate data, wherein the health value includes a heart rate interval value and a heart rate variability interval value;

S33: Predict based on the preset time period and the health value, and obtain predicted heart rate data of the human body at the same time as the preset time period.

In this embodiment, the system obtains part of the first heart rate data corresponding to the preset time period, and collects the corresponding health values in the part of the first heart rate data, including (heart rate interval value and heart rate variability interval value), The pre-set time period and health value can be predicted through the pre-trained prediction model to obtain the predicted heart rate data of the human body at the same time period as the pre-set time period but at different times; for example, part of the first heart rate data collected It belongs to the heart rate law data within one week, and the heart rate law data input into the prediction model for prediction belongs to the heart rate law data of the next week; The heart rate pattern data predicted in the prediction model also belongs to the heart rate pattern data after three days, which means that the predicted heart rate data is proportional to the time period of the input heart rate data.

In this embodiment, the step S4 of obtaining the predicted peak-to-valley value of the second heart rate data includes:

S41: Obtain the duration of the predicted peak-to-valley value;

S42: judging whether the duration is shorter than a preset time period;

S43: If yes, classify the second heart rate data based on the predicted peak-to-valley value, and obtain resting heart rate data and exercise heart rate data corresponding to the second heart rate data.

In this embodiment, the system obtains the duration of the predicted peak-to-valley value corresponding to the second heart rate data, and judges whether the duration is less than a preset time period, so as to perform corresponding different steps; for example, the system judges that the predicted peak-to-valley value If the duration is less than the preset time period, the system will classify and unify the second heart rate data based on the predicted peak and valley values at this time, so as to obtain the resting heart rate data and exercise heart rate data in the second heart rate data by classification, namely The valley value at rest and the peak value during exercise can be known when the peak-valley value is predicted; for example, if the system judges that the duration of the predicted peak-valley value is not less than the preset time period, The second heart rate data has the same peak-valley value as the first heart rate data, which means that the human body may have the same symptoms in the next predicted time period, and preventive measures can be taken for the human body in advance, such as doing more aerobic exercise and maintaining adequate sleep .

In this embodiment, the step S7 of inputting the data information of the human body health index into the prediction model and obtaining the data analysis result of the human body includes:

S71: Obtain the comprehensive risk assessment score in the human health index data information;

S72: Judging whether the comprehensive risk assessment score is greater than a preset score evaluation interval;

S73: If yes, create a corresponding risk assessment report based on the human health index data information.

In this embodiment, the system obtains the corresponding comprehensive risk assessment score in the human health index data information based on the preset score evaluation table, and judges whether the comprehensive risk assessment score is greater than the preset score evaluation interval, so as to perform corresponding different steps; for example, if the system judges that the comprehensive risk assessment score is 66 points, and the pre-set score evaluation interval is set to 60 points, then the system will judge that the comprehensive risk assessment score is greater than the preset score evaluation interval, then A corresponding risk assessment report will be established based on the data information of human health indicators. The content of the risk assessment report includes reminding the human body to maintain a balanced heart rate, because the comprehensive risk assessment score is on the edge of the score evaluation range, and preventive measures need to be taken. Aerobic exercise, maintaining adequate sleep, etc.; for example, the system judges that the comprehensive risk assessment score is 45 points, and if the pre-set score evaluation interval is set to 60 points, the system will judge the comprehensive risk assessment score at this time If it is less than the preset score evaluation interval, a corresponding warning report will be set up based on the data information of human health indicators to remind the user that the heart rate of a certain period of time is abnormal, so as to warn the user that they need to change their heart rate and pay attention to their eating habits and life. Habit.

In this embodiment, after step S7 of inputting the data information of the human body health index into the prediction model and obtaining the data analysis result of the human body, it includes:

S701: Obtain heart rate interval data corresponding to the second heart rate data;

S702: Judging whether the heart rate interval data conforms to a preset heart rate law;

S703: If yes, compare the second heart rate data with the first heart rate data to form a comparison of human heart rate waveform data in different time periods.

In this embodiment, after obtaining the heart rate interval data corresponding to the second heart rate data, the system judges whether the heart rate interval data conforms to the preset heart rate pattern, so as to perform corresponding different steps; for example, the system obtains the heart rate interval data is 68-76 beats/min, and the pre-set heart rate pattern is 65-78 beats/min, then the system will judge that the heart rate interval data can meet the pre-set heart rate pattern, that is, the system will predict the prediction model to get The second heart rate data of the human body is compared with the first heart rate data obtained at the beginning of monitoring to form a comparison of human heart rate waveform data of different time periods but belonging to the same time, so as to know whether the human body will have abnormal heart rate in the next time period conditions, effectively preventing the occurrence of human heart rate diseases; It will be judged that the heart rate interval data cannot meet the preset heart rate pattern, that is, the system will set up a corresponding warning report based on the data information of human health indicators to remind the user that the heart rate is abnormal in a certain period of time, so as to warn the user that he needs to change his heart rate pattern , Pay attention to your eating habits and living habits.

With reference to accompanying drawing 2, be the dynamic heart rate monitoring system based on smart wearable device in an embodiment of the present invention, comprise:

The first collection module 10 is configured to use a preset continuous heart rate sensor to collect first heart rate data of the human body, wherein the first heart rate data includes resting heart rate data and exercise heart rate data;

The first judging module 20 is configured to judge whether the first heart rate data reaches a preset data peak-to-valley value, wherein the data peak-to-valley value includes a data peak-to-valley value and a data valley value;

The first execution module 30 is configured to input the first heart rate data into a preset prediction model for prediction if it is reached, and obtain the second heart rate data corresponding to the prediction time period;

A first acquisition module 40, configured to acquire the predicted peak-to-valley value of the second heart rate data;

The second judging module 50 is used to judge whether the predicted peak-to-valley value reaches the data peak-to-valley value;

The second execution module 60 is configured to collect the human health index data information corresponding to the second heart rate data if it is reached;

The first analysis module 70 is configured to input the human body health index data information into the prediction model to obtain the data analysis results of the human body, wherein the data analysis results include human heart rate waveform data and health index reports.

In this embodiment, the first collection module 10 collects the first heart rate data of the human body for a certain period of time by using the continuous heart rate sensor preset in the smart wearable device, including (resting heart rate data and exercise heart rate data), and then The first judging module 20 judges whether the first heart rate data can reach the preset heart rate data peak-valley value, including (heart rate data peak value and heart rate data valley value), so as to perform corresponding different steps; for example, the system judges that the first The heart rate data does not reach the pre-set data peak and valley value, such as the highest heart rate in the first heart rate data is 78 beats/min, the lowest heart rate is 65 beats/min, and the pre-set data peak and valley value is 80 beats /min, and the lowest heart rate is 62 beats/min, then the system will directly generate the human health index data information corresponding to the first heart rate data, and input the human health index data information into the prediction model to obtain the Human heart rate waveform data and health index reports corresponding to the first heart rate data; heart rate is 60 beats/min, and the pre-set data peak-to-valley value is 80 beats/min, and the lowest heart rate is 62 beats/min, then the first execution module 30 will set the first heart rate The data is input into a pre-set prediction model for prediction, so as to predict the second heart rate data at the same time period as the first heart rate data but at a different time; the first acquisition module 40 acquires the predicted peak and valley values of the second heart rate data, The second judging module 50 judges whether the predicted peak-to-valley value reaches the peak-to-valley value of the heart rate data to perform corresponding steps; The obtained human health index data information corresponding to the second heart rate data includes (oxygen endurance, oxygen uptake and oxygen consumption), and the first analysis module 70 inputs these human health index data information into the prediction model for prediction, so as to predict Obtain the data analysis results of the human body, including (human heart rate waveform data and health index reports), and compare the data analysis results of the first heart rate data with the data analysis results of the second heart rate data, which can effectively reduce the possibility that the human body may be in a certain There is an abnormal heart rate index in a period of time, but the intelligent wearable device does not detect the probability in time, and by predicting the heart rate activity of the human body, the occurrence of sudden diseases can be effectively prevented.

In this embodiment, it also includes:

The first capture module is configured to capture performance generated by preset sensors, wherein the performance includes temperature data and touch data;

A third judging module, configured to judge whether the performance meets a preset enabling condition;

The third execution module is configured to, if yes, collect physical data of the human body, wherein the physical data includes electrocardiographic data, blood pressure data and blood oxygen data.

In this embodiment, the system captures the functions preset in the smart wearable device, including (temperature data and touch data), and judges whether these performances meet the preset conditions for enabling the function of the smart wearable device, so as to perform corresponding different functions. Steps; for example, the system judges that these performances meet the pre-set conditions for enabling smart wearable devices. For example, when wearing a smart wearable device, the human body temperature needs to reach the interval value of 30-40°C, and the sensor of the smart wearable device acquires the temperature and reaches This interval value can enable the smart wearable device to enable the heart rate monitoring function; for example, when wearing a smart wearable device, the touch range set by the sensor of the smart wearable device is touched by the human body for a period of time (for example, the smart wearable ring is worn on the finger of the human body) , the smart wearable watch is worn on the human wrist), at this time the smart wearable device will enable the heart rate monitoring function, and can also monitor the human body's physical data such as (ECG data, blood pressure data and blood oxygen data), and included in In the smart wearable device; for example, the system judges that these performances do not meet the pre-set conditions for enabling the smart wearable device, that is, the smart wearable device will not work at this time, that is, it cannot monitor the heart rate normally.

In this embodiment, the first collection module also includes:

a first acquiring unit, configured to acquire the rest time period and the exercise time period of the human body;

A first classification unit, configured to correspondingly classify resting heart rate data and exercise heart rate data in the first heart rate data based on the resting time period and the exercise time period;

The first collection unit is configured to collect peak-to-valley heart rate values of the resting heart rate data and peak-to-valley heart rate values of the exercise heart rate data.

In this embodiment, after acquiring the resting time period and the exercise time period of the human body, the system correspondingly classifies the different heart rates in the first heart rate data as resting Time period and exercise time period to obtain resting heart rate data and exercise heart rate data. At the same time, the system will collect the peak and valley values of the two heart rate data for comparison with the pre-set data peak and valley values.

In this embodiment, it also includes:

The second collection module is used to collect heart rate health data and health index data, wherein the health data includes heart rate interval data and heart rate variability interval data, and the health index data includes oxygen endurance, oxygen uptake, and oxygen consumption ;

The first training module is configured to input the health data and the health index data as training samples into a training model for training to obtain a trained prediction model.

In this embodiment, the system collects optimal heart rate health data and health index data on the Internet, including (heart rate interval data and heart rate variability interval data), and inputs these data into a blank training model for training, You can get a prediction model that takes the best heart rate data as the center value and can predict the heart rate pattern in the next period of time. You only need to input the heart rate data of a certain period of time into the prediction model, and you can get it through the prediction model. The heart rate data of the next time period connected to this time period, for example, the collected first heart rate data belongs to the heart rate law data within a week, and the heart rate law data input into the prediction model for prediction belongs to the heart rate law data of the next week; For example, the collected first heart rate data belongs to the heart rate data within three days, then the heart rate data input to the prediction model for prediction also belongs to the heart rate data after three days, which means the predicted heart rate data and the input heart rate data time period Proportional.

In this embodiment, the first execution module further includes:

a second acquisition unit, configured to acquire part of the first heart rate data for a preset time period;

The second acquisition unit is configured to acquire health values corresponding to the part of the first heart rate data, wherein the health values include heart rate interval values and heart rate variability interval values;

The first prediction unit is configured to perform prediction based on the preset time period and the health value, and obtain predicted heart rate data of the human body at the same time as the preset time period.

In this embodiment, the system obtains part of the first heart rate data corresponding to the preset time period, and collects the corresponding health values in the part of the first heart rate data, including (heart rate interval value and heart rate variability interval value), The pre-set time period and health value can be predicted through the pre-trained prediction model to obtain the predicted heart rate data of the human body at the same time period as the pre-set time period but at different times; for example, part of the first heart rate data collected It belongs to the heart rate law data within one week, and the heart rate law data input into the prediction model for prediction belongs to the heart rate law data of the next week; The heart rate pattern data predicted in the prediction model also belongs to the heart rate pattern data after three days, which means that the predicted heart rate data is proportional to the time period of the input heart rate data.

In this embodiment, the first acquisition module also includes:

A third acquisition unit, configured to acquire the duration of the predicted peak-to-valley value;

a first judging unit, configured to judge whether the duration is shorter than a preset time period;

The first execution unit is configured to, if yes, classify the second heart rate data based on the predicted peak-to-valley value, and obtain resting heart rate data and exercise heart rate data corresponding to the second heart rate data.

In this embodiment, the system obtains the duration of the predicted peak-to-valley value corresponding to the second heart rate data, and judges whether the duration is less than a preset time period, so as to perform corresponding different steps; for example, the system judges that the predicted peak-to-valley value If the duration is less than the preset time period, the system will classify and unify the second heart rate data based on the predicted peak and valley values at this time, so as to obtain the resting heart rate data and exercise heart rate data in the second heart rate data by classification, namely The valley value at rest and the peak value during exercise can be known when the peak-valley value is predicted; for example, if the system judges that the duration of the predicted peak-valley value is not less than the preset time period, The second heart rate data has the same peak-valley value as the first heart rate data, which means that the human body may have the same symptoms in the next predicted time period, and preventive measures can be taken for the human body in advance, such as doing more aerobic exercise and maintaining adequate sleep .

In this embodiment, the first analysis module also includes:

A fourth obtaining unit, configured to obtain the comprehensive risk assessment score in the human health index data information;

The second judging unit is used to judge whether the comprehensive risk assessment score is greater than a preset score judging interval;

The second execution unit is configured to, if yes, create a corresponding risk assessment report based on the human health index data information.

In this embodiment, the system obtains the corresponding comprehensive risk assessment score in the human health index data information based on the preset score evaluation table, and judges whether the comprehensive risk assessment score is greater than the preset score evaluation interval, so as to perform corresponding different steps; for example, if the system judges that the comprehensive risk assessment score is 66 points, and the pre-set score evaluation interval is set to 60 points, then the system will judge that the comprehensive risk assessment score is greater than the preset score evaluation interval, then A corresponding risk assessment report will be established based on the data information of human health indicators. The content of the risk assessment report includes reminding the human body to maintain a balanced heart rate, because the comprehensive risk assessment score is on the edge of the score evaluation range, and preventive measures need to be taken. Aerobic exercise, maintaining adequate sleep, etc.; for example, the system judges that the comprehensive risk assessment score is 45 points, and if the pre-set score evaluation interval is set to 60 points, the system will judge the comprehensive risk assessment score at this time If it is less than the preset score evaluation interval, a corresponding warning report will be set up based on the data information of human health indicators to remind the user that the heart rate of a certain period of time is abnormal, so as to warn the user that they need to change their heart rate and pay attention to their eating habits and life. Habit.

In this embodiment, it also includes:

A second acquiring module, configured to acquire heart rate interval data corresponding to the second heart rate data;

The fourth judging module is used to judge whether the heart rate interval data conforms to the preset heart rate rule;

The fourth execution module is configured to compare the second heart rate data with the first heart rate data if yes, to form a comparison of human heart rate waveform data in different time periods.

In this embodiment, after obtaining the heart rate interval data corresponding to the second heart rate data, the system judges whether the heart rate interval data conforms to the preset heart rate pattern, so as to perform corresponding different steps; for example, the system obtains the heart rate interval data is 68-76 beats/min, and the pre-set heart rate pattern is 65-78 beats/min, then the system will judge that the heart rate interval data can meet the pre-set heart rate pattern, that is, the system will predict the prediction model to get The second heart rate data of the human body is compared with the first heart rate data obtained at the beginning of monitoring to form a comparison of human heart rate waveform data of different time periods but belonging to the same time, so as to know whether the human body will have abnormal heart rate in the next time period conditions, effectively preventing the occurrence of human heart rate diseases; It will be judged that the heart rate interval data cannot meet the preset heart rate pattern, that is, the system will set up a corresponding warning report based on the data information of human health indicators to remind the user that the heart rate is abnormal in a certain period of time, so as to warn the user that he needs to change his heart rate pattern , Pay attention to your eating habits and living habits.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. and modifications, the scope of the invention is defined by the appended claims and their equivalents.

Claims (8)

1. The dynamic heart rate monitoring method based on intelligent wearable equipment, is characterized in that, may further comprise the steps: Using a preset continuous heart rate sensor to collect first heart rate data of the human body, wherein the first heart rate data includes resting heart rate data and exercise heart rate data; Judging whether the first heart rate data reaches a preset data peak-to-valley value, wherein the data peak-to-valley value includes a data peak value and a data valley value; If it is reached, then input the first heart rate data into a preset prediction model for prediction, and obtain the second heart rate data corresponding to the prediction time period; Acquiring predicted peak-to-valley values of the second heart rate data; Obtaining the duration of the predicted peak-to-valley value; judging whether the duration is less than a preset time period; If so, classifying the second heart rate data based on the predicted peak-to-valley value to obtain resting heart rate data and exercise heart rate data corresponding to the second heart rate data; judging whether the predicted peak-to-valley value reaches the data peak-to-valley value; If it is achieved, then collect the human health index data information corresponding to the second heart rate data; Input the human body health index data information into the prediction model to obtain the data analysis result of the human body, wherein the data analysis result includes human heart rate waveform data and health index report; acquiring heart rate interval data corresponding to the second heart rate data; Judging whether the heart rate interval data conforms to a preset heart rate pattern; If so, comparing the second heart rate data with the first heart rate data to form a comparison of human heart rate waveform data in different time periods. 2. The dynamic heart rate monitoring method based on smart wearable device according to claim 1, wherein, before the step of collecting the first heart rate data of the human body by using a preset continuous heart rate sensor, it includes: Capturing performance generated by preset sensors, wherein the performance includes temperature data and tactile data; judging whether the performance meets the preset enabling conditions; If yes, collect physical data of the human body, wherein the physical data includes electrocardiographic data, blood pressure data, and blood oxygen data. 3. The dynamic heart rate monitoring method based on an intelligent wearable device according to claim 1, wherein the step of collecting the first heart rate data of the human body by using a preset continuous heart rate sensor includes: Acquiring the rest time period and exercise time period of the human body; Correspondingly classifying resting heart rate data and exercise heart rate data in the first heart rate data based on the resting time period and the exercise time period; Collecting the peak-to-valley values of the resting heart rate data and the peak-to-valley values of the exercise heart rate data. 4. The dynamic heart rate monitoring method based on smart wearable devices according to claim 1, wherein the first heart rate data is input to a preset prediction model for prediction, and the first heart rate data corresponding to the prediction time period is obtained. Before the steps of the two heart rate data, including: Collect heart rate health data and health index data, wherein the health data includes heart rate interval data and heart rate variability interval data, and the health index data includes oxygen endurance, oxygen uptake, and oxygen consumption; The health data and the health index data are used as training samples, input into a training model for training, and a trained prediction model is obtained. 5. The dynamic heart rate monitoring method based on smart wearable devices according to claim 1, wherein the first heart rate data is input to a preset prediction model for prediction, and the first heart rate data corresponding to the prediction time period is obtained. The steps of the second heart rate data include: Obtain part of the first heart rate data for a preset time period; Collect health values corresponding to the part of the first heart rate data, wherein the health values include heart rate interval values and heart rate variability interval values; Prediction is performed based on the preset time period and the health value to obtain predicted heart rate data of the human body at the same time as the preset time period. 6. The dynamic heart rate monitoring method based on smart wearable device according to claim 1, characterized in that, the input of the human body health index data information into the predictive model to obtain the data analysis results of the human body Steps include: Obtain the comprehensive risk assessment score in the data information of the human health index; judging whether the comprehensive risk assessment score is greater than a preset score evaluation interval; If yes, a corresponding risk assessment report is established based on the human health index data information. 7. A dynamic heart rate monitoring system based on smart wearable devices, characterized in that, comprising: The first collection module is used to collect the first heart rate data of the human body by using a preset continuous heart rate sensor, wherein the first heart rate data includes resting heart rate data and exercise heart rate data; A first judgment module, configured to judge whether the first heart rate data reaches a preset data peak-to-valley value, wherein the data peak-to-valley value includes a data peak value and a data valley value; The first execution module is configured to input the first heart rate data into a preset prediction model for prediction if it is reached, and obtain the second heart rate data corresponding to the prediction time period; A first acquisition module, configured to acquire the predicted peak-to-valley value of the second heart rate data; A third acquisition unit, configured to acquire the duration of the predicted peak-to-valley value; a first judging unit, configured to judge whether the duration is shorter than a preset time period; The first execution unit is configured to, if yes, classify the second heart rate data based on the predicted peak-to-valley value, and obtain resting heart rate data and exercise heart rate data corresponding to the second heart rate data; The second judging module is used to judge whether the predicted peak-to-valley value reaches the data peak-to-valley value; The second execution module is configured to collect the human health index data information corresponding to the second heart rate data if it is achieved; The first analysis module is used to input the data information of the human body health index into the prediction model to obtain the data analysis result of the human body, wherein the data analysis result includes human heart rate waveform data and health index report; A second acquiring module, configured to acquire heart rate interval data corresponding to the second heart rate data; The fourth judging module is used to judge whether the heart rate interval data conforms to the preset heart rate rule; The fourth execution module is configured to compare the second heart rate data with the first heart rate data if yes, to form a comparison of human heart rate waveform data in different time periods. 8. The dynamic heart rate monitoring system based on smart wearable device according to claim 7, further comprising: The first capture module is configured to capture performance generated by preset sensors, wherein the performance includes temperature data and touch data; A third judging module, configured to judge whether the performance meets a preset enabling condition; The third execution module is configured to, if yes, collect physical data of the human body, wherein the physical data includes electrocardiographic data, blood pressure data and blood oxygen data.

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