Data processing method and system based on wearable device, electronic device and medium

Abstract

The invention discloses a data processing method and system based on a wearable device, an electronic device and a storage medium, and the method comprises the steps: obtaining a motion parameter of a user through the wearable device, and determining a metabolism index of the user; inputting the motion parameters and the metabolism indexes into a preset regression model for oxygen uptake calculation, and outputting target oxygen uptake; and sending the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and displaying the target oxygen uptake and the target health prompt through the wearable device. According to the embodiment of the invention, by combining metabolic health with motion data, multi-dimensional evaluation of the cardiopulmonary function is realized, and real-time cardiopulmonary function evaluation and health guidance can be provided for the user.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection technology, and in particular to a data processing method, system, electronic device and medium based on a wearable device. Background Art

[0002] Traditional oxygen uptake measurement methods require specialized equipment such as treadmills and gas metabolism analyzers in a laboratory setting, making the measurement process complex, costly, and risky. Due to these shortcomings, this method is not commonly used in clinical settings to routinely assess cardiorespiratory fitness.

[0003] Although existing wearable devices can monitor biological signals such as resting heart rate and physical activity in a free living environment, they can only monitor common indicators such as vital signs, physical activity and other indicators with low sensitivity and specificity, and ignore the impact of other indicators on cardiopulmonary function. Summary of the Invention

[0004] The embodiments of the present application provide a data processing method, system, electronic device, and storage medium based on a wearable device to achieve accurate assessment of cardiopulmonary function.

[0005] In a first aspect, an embodiment of the present application provides a data processing method based on a wearable device, the method comprising:

[0006] Obtaining the user's exercise parameters through the wearable device and determining the user's metabolic indicators;

[0007] Inputting the exercise parameters and the metabolic index into a preset regression model to calculate oxygen uptake, and outputting a target oxygen uptake;

[0008] The target oxygen uptake is sent to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and the target oxygen uptake and the target health prompt are displayed through the wearable device.

[0009] In some embodiments, the wearable device is provided with a sensor; the motion parameter is obtained by the following steps:

[0010] sensing the user's average heart rate through the sensor;

[0011] Determining the user's metabolic index includes:

[0012] Receive blood sugar data and blood lipid data input by the user, or monitor the user's blood sugar data and blood lipid data through a preset non-invasive detection technology;

[0013] A metabolic index is determined based on the blood sugar data and the blood lipid data.

[0014] In some embodiments, inputting the exercise parameter and the metabolic index into a preset regression model to calculate oxygen uptake and outputting a target oxygen uptake includes:

[0015] The exercise parameters and the metabolic index are input into a preset regression model, so that the regression model calculates the maximum oxygen uptake based on the exercise parameters and the metabolic index and outputs the target oxygen uptake.

[0016] In some embodiments, after obtaining the user's exercise parameters and metabolic indicators through the wearable device, the method further includes:

[0017] Obtaining gender information, white blood cell information, and acute pancreatitis protein information input by the user, and obtaining the user's blood pressure information through the wearable device;

[0018] The preset regression model is modified in a gender-specific manner according to the gender information, or the regression model is optimized according to the white blood cell information, the acute protein information and the blood pressure information.

[0019] In some embodiments, the blood glucose data includes fasting blood glucose data, and the blood lipid data includes triglyceride data; and determining the metabolic index based on the blood glucose data and the blood lipid data includes:

[0020] Performing logarithmic operations on the fasting blood glucose data and the triglyceride data to obtain metabolic indicators.

[0021] In some embodiments, the wearable device stores a correspondence between oxygen uptake and health level; and sending the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake includes:

[0022] Sending the target oxygen uptake to the wearable device to determine a target health level corresponding to the target oxygen uptake and a prompt color corresponding to the target health level;

[0023] Determining health advice corresponding to the target health level in a preset language library;

[0024] A target health prompt is generated according to the target health level, the prompt color, and the health advice.

[0025] In some embodiments, performing gender-specific correction on a preset regression model based on the gender information includes:

[0026] When the gender information is female, the preset regression model is specifically corrected according to the preset first correction coefficient;

[0027] When the gender information is male, the preset regression model is specifically corrected according to the preset second correction coefficient.

[0028] In a second aspect, an embodiment of the present application further provides a data processing system based on a wearable device, the system comprising:

[0029] A data acquisition module, configured to obtain the user's exercise parameters through the wearable device and determine the user's metabolic indicators;

[0030] a model calculation module, configured to input the exercise parameters and the metabolic index into a preset regression model to calculate oxygen uptake and output a target oxygen uptake;

[0031] A result display module is used to send the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and display the target oxygen uptake and the target health prompt through the wearable device.

[0032] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the data processing method based on the wearable device as described in the first aspect.

[0033] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the data processing method based on a wearable device as described in the first aspect.

[0034] The data processing method based on a wearable device provided in the embodiment of the present application has at least the following beneficial effects: first, the user's motion parameters are obtained through the wearable device, so that the user's heart rate changes during exercise can be determined, and the user's metabolic indicators can be determined, so that the user's metabolic status can be determined through the metabolic indicators, and the user's metabolic health can be further evaluated. The motion parameters and metabolic indicators are then input into a preset regression model to calculate oxygen uptake, so as to measure the user's cardiopulmonary endurance and aerobic exercise capacity through oxygen uptake, and a target oxygen uptake is output to achieve measurement of the user's cardiopulmonary endurance. Afterwards, the target oxygen uptake is sent to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and the target oxygen uptake and target health prompt are displayed through the wearable device to achieve a visual display of the target oxygen uptake and the target health prompt, so that the user can intuitively understand his or her own cardiopulmonary endurance. The embodiment of the present application combines metabolic health with motion data to achieve a multi-dimensional assessment of cardiopulmonary function, and can provide users with real-time cardiopulmonary function assessment and health guidance.

[0035] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained through the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the examples of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0037] Figure 1 is a flowchart of a specific method of a data processing method based on a wearable device provided in an embodiment of the present application;

[0038] Figure 2 This is a specific flow chart for determining a user's metabolic index provided in an embodiment of the present application;

[0039] Figure 3 This is a specific flow chart of step S102 provided in an embodiment of the present application;

[0040] Figure 4 is a specific flow chart of a data processing method based on a wearable device provided in another embodiment of the present application;

[0041] Figure 5 This is a specific flow chart of step S203 provided in an embodiment of the present application;

[0042] Figure 6 This is a specific flow chart for generating a target health prompt corresponding to a target oxygen uptake provided by an embodiment of the present application;

[0043] Figure 7 This is a specific flow chart of performing gender-specific correction on a preset regression model based on gender information provided in an embodiment of the present application;

[0044] Figure 8 Schematic diagram of a data processing system based on a wearable device according to an embodiment of the present invention;

[0045] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0047] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] The data processing method based on a wearable device provided in an embodiment of the present application can be applied to a terminal, a server, or software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, or a smart watch; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms; the software can be an application that implements the above method, but is not limited to the above forms.

[0050] Embodiments of the present application can be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0051] Traditional oxygen uptake measurement methods require specialized equipment such as treadmills and gas metabolism analyzers in a laboratory setting, making the measurement process complex, costly, and risky. Due to these shortcomings, this method is not commonly used in clinical settings to routinely assess cardiorespiratory fitness.

[0052] Although existing wearable devices can monitor biological signals such as resting heart rate and physical activity in a free living environment, they can only monitor common indicators such as vital signs, physical activity and other indicators with low sensitivity and specificity, and ignore the impact of other indicators on cardiopulmonary function.

[0053] To solve the above problems, the embodiments of the present application provide a data processing method, system, electronic device and storage medium based on a wearable device. First, the user's motion parameters are obtained through the wearable device, so that the user's heart rate changes during exercise can be determined, and the user's metabolic indicators can be determined, so that the user's metabolic status can be determined through the metabolic indicators, and the user's metabolic health can be further evaluated. The motion parameters and metabolic indicators are then input into a preset regression model to calculate oxygen uptake, so as to measure the user's cardiopulmonary endurance and aerobic exercise capacity through oxygen uptake, and a target oxygen uptake is output to measure the user's cardiopulmonary endurance. Afterwards, the target oxygen uptake is sent to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and the target oxygen uptake and target health prompt are displayed through the wearable device to achieve a visual display of the target oxygen uptake and the target health prompt, so that the user can intuitively understand his or her own cardiopulmonary endurance. The embodiments of the present application combine metabolic health with motion data to achieve a multi-dimensional assessment of cardiopulmonary function, and can provide users with real-time cardiopulmonary function assessment and health guidance.

[0054] Reference Figure 1 , Figure 1 10 is a flowchart of a specific method of a data processing method based on a wearable device provided in an embodiment of the present application. In some embodiments, the data processing method based on a wearable device includes but is not limited to steps S101 to S103.

[0055] Step S101: obtaining the user's exercise parameters through a wearable device and determining the user's metabolic index.

[0056] In step S101 of some embodiments, the user's motion parameters are obtained through the wearable device. Specifically, before obtaining the user's motion parameters through the wearable device, the user is required to complete a thirty-minute brisk walk while wearing the wearable device to collect the user's average heart rate and obtain the user's motion parameters, so as to determine the user's heart rate changes during exercise and determine the user's metabolic indicators, so as to determine the user's metabolic status through the metabolic indicators, and further evaluate the user's metabolic health.

[0057] In some embodiments, the wearable device may be provided with sensors, accelerometers, blood pressure monitoring modules, and the like. Specifically, the wearable device of the embodiment of the present application is provided with an optical heart rate sensor, which senses the user's average heart rate through the optical heart rate sensor of the wearable device, thereby realizing real-time monitoring of the user's heart rate.

[0058] Step S102: Input the exercise parameters and metabolic indicators into a preset regression model to calculate oxygen uptake and output a target oxygen uptake.

[0059] In step S102 of some embodiments, the motion parameters and metabolic indicators are input into a preset regression model to calculate oxygen uptake to measure the user's cardiopulmonary endurance and aerobic exercise capacity, and the target oxygen uptake is output to evaluate the user's cardiopulmonary function based on the target oxygen uptake.

[0060] It can be understood that the target oxygen uptake can measure the user's cardiopulmonary endurance and aerobic exercise capacity. Specifically, the target oxygen uptake can reflect the heart's ability to pump blood, the lungs' oxygen absorption and exchange efficiency, the blood's ability to transport oxygen, and the efficiency of muscles' oxygen utilization.

[0061] Step S103: Send the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and display the target oxygen uptake and the target health prompt through the wearable device.

[0062] In step S103 of some embodiments, the target oxygen uptake is sent to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, so as to evaluate the user's cardiopulmonary endurance through oxygen uptake, thereby providing personalized health assessment and suggestions for each person, and displaying the target oxygen uptake and target health prompt through the wearable device to remind the user of the cardiopulmonary condition in real time, thereby achieving the purpose of promoting health and preventing disease.

[0063] Reference Figure 2 , Figure 2 This is a specific flow chart for determining a user's metabolic index provided by an embodiment of the present application. In some embodiments, the method includes but is not limited to step S201 and step S203.

[0064] Step S201: receiving blood sugar data and blood lipid data input by a user.

[0065] Step S202: Monitor the user's blood sugar data and blood lipid data using a preset non-invasive detection technology.

[0066] Step S203: determining metabolic indicators based on the blood sugar data and the blood lipid data.

[0067] In steps S201 to S203 of some embodiments, in the process of determining the user's metabolic indicators, the embodiments of the present application can directly receive blood glucose data and blood lipid data input by the user, wherein the blood glucose data is the blood glucose data input by the user in a fasting state, and the blood lipid data is used to characterize the user's fat metabolism. Alternatively, the embodiments of the present application can also monitor the user's blood glucose data and blood lipid data through a preset non-invasive detection technology, wherein the non-invasive detection technology can be near-infrared spectroscopy, infrared spectroscopy, etc., to achieve real-time monitoring of the user's blood glucose and blood lipids, and then determine the metabolic indicators based on the blood glucose data and blood lipid data. By monitoring these two indicators, the user's glucose and lipid metabolism status can be understood, thereby evaluating the comprehensive metabolic situation.

[0068] Reference Figure 3 , Figure 3 102 is a specific flow chart of step S102 provided in an embodiment of the present application. In some embodiments, step S102 specifically includes but is not limited to step S301.

[0069] Step S301 : inputting the exercise parameters and metabolic indexes into a preset regression model, so that the regression model calculates the maximum oxygen uptake based on the exercise parameters and metabolic indexes and outputs the target oxygen uptake.

[0070] In step S301 of some embodiments, in the process of inputting motion parameters and metabolic indicators into a preset regression model to calculate oxygen uptake, the embodiment of the present application inputs the motion parameters and metabolic indicators into the preset regression model so that the regression model calculates the maximum oxygen uptake based on the motion parameters and metabolic indicators to measure the user's cardiopulmonary endurance and aerobic exercise capacity, and outputs the target oxygen uptake to evaluate the user's cardiopulmonary function based on the target oxygen uptake.

[0071] It should be noted that the regression model in the embodiment of the present application is a multiple linear regression model, and the regression model includes a negative correlation coefficient between metabolic indicators and oxygen uptake.

[0072] It is understood that the exercise parameters include the average heart rate. The formula for calculating the maximum oxygen uptake by the regression model in the embodiment of the present application is as follows:

[0073] VO2max = ab × TyG + c × average heart rate;

[0074] Among them, a, b, and c are model coefficients obtained through training with cross-sectional study data, and TyG is a metabolic index.

[0075] Reference Figure 4 , Figure 4 4 is a specific flow chart of a data processing method based on a wearable device provided in another embodiment of the present application. In some embodiments, the method includes but is not limited to steps S401 to S403.

[0076] Step S401: Obtain the gender information, white blood cell information, and acute pancreatitis protein information input by the user, and obtain the user's blood pressure information through the wearable device.

[0077] Step S402: Perform gender-specific correction on the preset regression model according to the gender information.

[0078] Step S403: Optimize the regression model based on the white blood cell information, acute protein information, and blood pressure information.

[0079] In steps S401 to S403 of some embodiments, the embodiments of the present application will also optimize the regression model to improve the predictive ability of the regression model. Specifically, the embodiments of the present application first obtain the gender information, white blood cell information and acute protein information input by the user, and obtain the user's blood pressure information through a wearable device to facilitate subsequent optimization of the regression model. On the one hand, since the metabolic capacity of men and women is different, the embodiments of the present application can perform gender-specific corrections to the preset regression model based on the gender information, thereby improving the accuracy of the regression model and avoiding errors caused by gender differences. On the other hand, the embodiments of the present application can directly optimize the regression model based on the white blood cell information, acute protein information and blood pressure information. By including biomarkers related to specific health conditions, the model can more accurately predict health outcomes and further improve the accuracy of cardiopulmonary function prediction.

[0080] It is understandable that the white blood cell information in the embodiments of the present application includes but is not limited to the total number of white blood cells in the blood, such as neutrophils, lymphocytes, monocytes, etc., and the embodiments of the present application do not impose specific limitations.

[0081] Reference Figure 5 , Figure 5 203. In some embodiments, step S203 specifically includes but is not limited to step S501.

[0082] It should be noted that blood sugar data includes fasting blood sugar data, and blood lipid data includes triglyceride data.

[0083] Step S501 : performing logarithmic operations on fasting blood glucose data and triglyceride data to obtain metabolic indicators.

[0084] In step S501 of some embodiments, in the process of determining metabolic indicators based on blood glucose data and blood lipid data, the embodiments of the present application directly perform logarithmic operations on the fasting blood glucose data in the blood glucose data and the triglyceride data in the blood lipid data to evaluate the user's metabolic health, and further evaluate the impact of exercise on metabolic health.

[0085] Specifically, the calculation process of the metabolic index in the embodiment of the present application is as follows:

[0086] TyG=Ln[TG(mg / dl)×FPG(mg / dl) / 2];

[0087] Among them, TG is triglyceride and FPG is fasting blood glucose.

[0088] Reference Figure 6 , Figure 6 This is a specific flow chart of generating a target health reminder corresponding to a target oxygen uptake provided by an embodiment of the present application. In some embodiments, the method includes but is not limited to steps S601 to S603.

[0089] It should be noted that the wearable device stores the correspondence between oxygen intake and health level.

[0090] Step S601: Send a target oxygen uptake to a wearable device to determine a target health level corresponding to the target oxygen uptake and a prompt color corresponding to the target health level.

[0091] Step S602: Determine health advice corresponding to the target health level in a preset language library.

[0092] Step S603: Generate a target health prompt based on the target health level, prompt color, and health advice.

[0093] In steps S601 to S603 of some embodiments, in the process of generating a target health prompt corresponding to the target oxygen uptake, the wearable device stores a correspondence between oxygen uptake and health level. For example, when the oxygen uptake is greater than or equal to 55.4 ml / kg / min, the corresponding health level is excellent; when the oxygen uptake is greater than or equal to 45.4 ml / kg / min and less than 55.4 ml / kg / min, the corresponding health level is normal; when the oxygen uptake is less than 45.4 ml / kg / min, the corresponding health level is requiring attention. Based on the above correspondence between oxygen uptake and health level, the embodiment of the present application first sends the target oxygen uptake to the wearable device to determine in which oxygen uptake range the target oxygen uptake is in, and further determines the target health level corresponding to the target oxygen uptake. In the embodiment of the present application, different health levels correspond to different prompt colors. Therefore, the embodiment of the present application will also determine the prompt color corresponding to the target health level. For example, the prompt color for an excellent health level is green, the prompt color for a normal health level is yellow, and the prompt color for a health level requiring attention is red. Afterwards, health recommendations corresponding to the target health level are determined in the preset language library to assess the user's cardiopulmonary endurance through oxygen uptake, thereby providing each person with personalized health assessments and recommendations to achieve the goal of promoting health and preventing disease.

[0094] It should be noted that the language library in the embodiment of the present application is a set of predefined sentences or phrases, which provide suggestions for different health levels.

[0095] It can be understood that, taking the target oxygen uptake of 30ml / kg / min as an example, the health level is in need of attention, the prompt color is red, and the target health prompt generated is "reduce sedentary time and increase daily activity"; taking the target oxygen uptake of 50ml / kg / min as an example, the health level is excellent, the prompt color is green, and the target health prompt generated is "continue to maintain a high level of aerobic exercise", etc., and the embodiments of the present application do not make specific restrictions.

[0096] Reference Figure 7 , Figure 7 This is a specific flow chart of performing gender-specific correction on a preset regression model according to gender information provided by an embodiment of the present application. In some embodiments, the method includes but is not limited to steps S701 to S702.

[0097] Step S701: When the gender information is female, a specific correction is performed on the preset regression model according to a preset first correction coefficient.

[0098] Step S702: When the gender information is male, a specific correction is performed on the preset regression model according to a preset second correction coefficient.

[0099] In steps S701 to S702 of some embodiments, in the process of performing gender-specific correction on the preset regression model based on gender information, when the gender information is female, the preset regression model is specifically corrected according to the preset first correction coefficient; when the gender information is male, the preset regression model is specifically corrected according to the preset second correction coefficient, thereby improving the prediction accuracy for users of different genders.

[0100] It can be understood that the first correction coefficient and the second correction coefficient in the embodiment of the present application can be set according to the needs of the user. For example, the first correction coefficient is set to 0.7, 0.8, 0.85, etc., and the second correction coefficient is set to 1, 1.1, 1.2, etc. The embodiment of the present application does not make specific restrictions.

[0101] It is worth noting that when the gender information is female, taking the fasting blood glucose of 85 mg / dL, the triglyceride of 100 mg / dL, and the first correction coefficient of 0.85 as an example, the triglyceride-glucose index TyG at this time is ln(100×85 / 2)=ln(4250)=8.35, and the corrected regression model is VO2max=(45-1.24×8.35)×0.85+0.05×120=35.45 mL / kg / min. When the gender information is male, taking the fasting blood glucose of 95 mg / dL, the triglyceride of 140 mg / dL, and the second correction coefficient of 1.15 as an example, the triglyceride-glucose index TyG at this time is ln(140×95 / 2)=ln(6650)=8.80, and the corrected regression model is VO2max=(45-1.24×8.80)×1.15+0.05×100=44.2 mL / kg / min.

[0102] See also Figure 8 The present application also provides a data processing system based on a wearable device, which can implement the above-mentioned data processing method based on a wearable device. The system includes:

[0103] The data acquisition module 801 is used to obtain the user's exercise parameters through the wearable device and determine the user's metabolic indicators;

[0104] Model calculation module 802, for inputting exercise parameters and metabolic indicators into a preset regression model to calculate oxygen uptake and output a target oxygen uptake;

[0105] The result display module 803 is used to send the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and display the target oxygen uptake and the target health prompt through the wearable device.

[0106] The data processing system based on a wearable device in an embodiment of the present application is used to execute the data processing method based on a wearable device in the above embodiment. Its specific processing process is the same as the data processing method based on a wearable device in the above embodiment, and will not be repeated here.

[0107] An embodiment of the present application also provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is used to execute the data processing method based on the wearable device in the above embodiment of the present application.

[0108] See also Figure 9 , Figure 9 The hardware structure of the electronic device provided in the embodiment of the present application is illustrated. The electronic device includes:

[0109] The processor 901 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0110] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the data processing method based on the wearable device in the embodiments of this application.

[0111] Input / output interface 903, used to implement information input and output;

[0112] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0113] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0114] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0115] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned data processing method based on a wearable device.

[0116] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0118] It will be understood by those skilled in the art that Figure 1-9 The technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or a combination of certain steps, or different steps.

[0119] The system embodiment described above is merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0120] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0121] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0122] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0124] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0127] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A data processing method based on a wearable device, characterized in that: The method comprises: Obtaining the user's exercise parameters through the wearable device and determining the user's metabolic indicators; Inputting the exercise parameters and the metabolic index into a preset regression model to calculate oxygen uptake, and outputting a target oxygen uptake; The target oxygen uptake is sent to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and the target oxygen uptake and the target health prompt are displayed through the wearable device.

2. The data processing method based on a wearable device according to claim 1, characterized in that: The wearable device is provided with a sensor; the motion parameters are obtained by the following steps: sensing the user's average heart rate through the sensor; Determining the user's metabolic index includes: Receive blood sugar data and blood lipid data input by the user, or monitor the user's blood sugar data and blood lipid data through a preset non-invasive detection technology; A metabolic index is determined based on the blood sugar data and the blood lipid data.

3. The data processing method based on a wearable device according to claim 1, characterized in that: The step of inputting the exercise parameters and the metabolic index into a preset regression model to calculate oxygen uptake and outputting a target oxygen uptake includes: The exercise parameters and the metabolic index are input into a preset regression model, so that the regression model calculates the maximum oxygen uptake based on the exercise parameters and the metabolic index and outputs the target oxygen uptake.

4. The data processing method based on a wearable device according to claim 1, characterized in that: After obtaining the user's exercise parameters and metabolic indicators through the wearable device, the method further includes: Obtaining gender information, white blood cell information, and acute pancreatitis protein information input by the user, and obtaining the user's blood pressure information through the wearable device; The preset regression model is modified in a gender-specific manner according to the gender information, or the regression model is optimized according to the white blood cell information, the acute protein information and the blood pressure information.

5. The data processing method based on a wearable device according to claim 2, characterized in that: The blood glucose data includes fasting blood glucose data, and the blood lipid data includes triglyceride data; and determining the metabolic index based on the blood glucose data and the blood lipid data includes: Performing logarithmic operations on the fasting blood glucose data and the triglyceride data to obtain metabolic indicators.

6. The data processing method based on a wearable device according to claim 1, characterized in that: The wearable device stores a correspondence between oxygen uptake and health level; The sending the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake includes: Sending the target oxygen uptake to the wearable device to determine a target health level corresponding to the target oxygen uptake and a prompt color corresponding to the target health level; Determining health advice corresponding to the target health level in a preset language library; A target health prompt is generated according to the target health level, the prompt color, and the health advice.

7. The data processing method based on a wearable device according to claim 4, characterized in that: The performing gender-specific correction on the preset regression model according to the gender information includes: When the gender information is female, the preset regression model is specifically corrected according to the preset first correction coefficient; When the gender information is male, the preset regression model is specifically corrected according to the preset second correction coefficient.

8. A data processing system based on a wearable device, characterized in that: The system comprises: A data acquisition module, configured to obtain the user's exercise parameters through the wearable device and determine the user's metabolic indicators; a model calculation module, configured to input the exercise parameters and the metabolic index into a preset regression model to calculate oxygen uptake and output a target oxygen uptake; A result display module is used to send the target oxygen uptake to the wearable device to generate a target health prompt corresponding to the target oxygen uptake, and display the target oxygen uptake and the target health prompt through the wearable device.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the data processing method based on the wearable device according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the data processing method based on a wearable device according to any one of claims 1 to 7.

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