Healthy diet scheme generation method and system based on multi-device data

Abstract

The invention relates to the technical field of health management, in particular to a healthy diet scheme generation method and system based on multi-device data, which is responsible for acquiring physiological index data of a user from various health devices; comprehensively analyzing the data collected from the multiple devices, and evaluating the overall health state of the user; according to a data analysis result, personalized diet management and recuperation suggestions are generated for the user; according to the physiological risk low signal, a recuperation reflection value curve obtained through drawing is used for ensuring that the diet management and recuperation scheme better meets the actual requirements of the user; according to the invention, data of a plurality of health devices can be effectively integrated, and comprehensive user health analysis is provided; based on comprehensive analysis of various physiological indexes, more accurate and personalized diet management and recuperation suggestions are provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a method and system for generating a healthy diet plan based on multi-device data. Background Art

[0002] As people's health awareness in modern society increases, more and more smart devices are used in health monitoring and management, such as health bracelets, body composition analyzers, blood pressure meters, blood glucose meters, uric acid meters, etc. These devices can obtain users' physiological index data in real time, providing basic data support for health management.

[0003] However, there is currently a lack of a system on the market that can integrate data from multiple devices and provide personalized diet management and conditioning plans based on the user's health status. Therefore, it is particularly necessary to provide a healthy diet management and conditioning method and system based on multi-device data. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for generating a healthy diet plan based on multi-device data. The technical problem solved by the present invention is: there is currently a lack of a system on the market that can integrate data from multiple devices and provide personalized diet management and conditioning plans based on the user's health status.

[0005] The purpose of the present invention can be achieved by the following technical solutions: A healthy diet plan generation system based on multi-device data, including the following modules: Device data collection module, which is responsible for obtaining the user's physiological indicator data from a variety of health devices; Data analysis module, which comprehensively analyzes the data collected from multiple devices to evaluate the user's overall health status; specifically: Obtain the number of large signals related to the generated device data, and calculate the device data correlation impact ratio BG; If the device data association impact ratio BG is greater than or equal to the device data association impact ratio threshold, a physiological risk high signal is generated; Feedback adjustment module, which draws the adjustment response value curve based on the low physiological risk signal to ensure that the diet management and adjustment plan are more in line with the actual needs of the user.

[0006] As a further solution of the present invention: the process of generating the large signal related to device data is as follows: Based on the continued analysis signal, the device data corresponding to any two device data warning signals are obtained, and the slope within the analysis period is marked as the device data warning signal generation slope; Calculate the difference between any two device data warning signal generation slopes to obtain the device data warning signal generation slope difference; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold, a device data related large impact signal is generated.

[0007] As a further solution of the present invention: the generation process of the continued analysis signal is: Obtain the device data corresponding to any two device data warning signals, and obtain the time when the device data unqualified signal is generated, and mark it as the device data unqualified signal generation time; Calculate the difference between the generation times of any two device data failure signals to obtain the device data failure signal generation time difference; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, a continue analysis signal is generated.

[0008] As a further solution of the present invention: the process of generating the equipment data early warning signal is: If the equipment data warning value ZZY is greater than or equal to the equipment data warning threshold, an equipment data warning signal is generated.

[0009] As a further solution of the present invention: the calculation process of the equipment data warning value ZZY is: Compare the collected device data with the corresponding device data range values ​​respectively; If the device data is not within the device data range, a device data unqualified signal is generated; Based on the unqualified signal of the equipment data, the collected equipment data is further analyzed for trend warning. The specific analysis process is as follows; When an equipment data failure signal is obtained, the equipment data corresponding to the equipment data failure signal is analyzed to obtain a first equipment data warning value and a second equipment data warning value, which are marked as ZY1 and ZY2 respectively, and the equipment data warning value ZZY is calculated.

[0010] As a further solution of the present invention: the process of obtaining the first warning value of the equipment data is: Obtain the device data corresponding to the device data unqualified signal, mark it as unqualified device data, set the analysis cycle, obtain the unqualified device data at the end of the analysis cycle time, calculate the difference between the unqualified device data at the end of the analysis cycle time and the preset device warning data, and take the absolute value to obtain the unqualified device data warning difference; The ratio of the unqualified equipment data warning difference to the preset equipment warning data is calculated to obtain the first warning value of the equipment data.

[0011] As a further solution of the present invention: the process of obtaining the second warning value of the equipment data is: The device data corresponding to the device data unqualified signal is obtained, marked as unqualified device data, the analysis period is set, the slope angle corresponding to the unqualified device data at the end of the analysis period time is obtained, and marked as the unqualified slope angle of unqualified device data in the analysis period; the slope angle is obtained by taking the unqualified device data at the end of the analysis period time as a tangent and measuring the angle between the tangent and the horizontal line; The ratio of the unqualified slope angle of the unqualified equipment data in the analysis period to the preset angle is calculated to obtain the second warning value of the equipment data.

[0012] As a further solution of the present invention: in the feedback adjustment module, the slope of each point of the conditioning reflection value curve is obtained, and then the mean value is calculated to obtain the conditioning reflection slope; If the slope of the conditioning reflection is greater than or equal to 0, it means that the dietary intake has a bad effect on its physiological indicators, and it is recommended that the user be hospitalized for examination in time; If the conditioning response slope is less than 0 and the conditioning response slope is greater than the conditioning response slope threshold, it means that the dietary intake has a certain impact on the physiological indicators, and the user is advised to increase the dietary intake to further adjust the physiological indicators.

[0013] As a further solution of the present invention: the construction process of the conditioning response value curve is: When a low physiological risk signal is obtained, an adjustment period is set to obtain the user's dietary intake and device data warning value ZZY during the adjustment period, and the ratio of the device data warning value ZZY to the dietary intake at the same time point is calculated to obtain the maintenance response value; a two-dimensional coordinate system is constructed with the adjustment period time as the X-axis and the maintenance response value as the Y-axis, and the maintenance response values ​​of all time points in the adjustment period are substituted into the coordinate system to draw a maintenance response value curve.

[0014] A method for generating a healthy diet plan based on multi-device data includes the following steps: Step 1: Responsible for obtaining the user's physiological indicator data from a variety of health devices; Step 2: Comprehensively analyze the data collected from multiple devices to evaluate the user's overall health status; Step 3: Generate personalized diet management and conditioning suggestions for users based on data analysis results; Step 4: Based on the low physiological risk signal, draw the obtained conditioning response value curve to ensure that the diet management and conditioning plan better meet the actual needs of the user.

[0015] Beneficial effects of the present invention: The present invention is responsible for obtaining the user's physiological indicator data from a variety of health devices; comprehensively analyzing the data collected from multiple devices to evaluate the user's overall health status; generating personalized diet management and conditioning suggestions for the user based on the data analysis results; and ensuring that the diet management and conditioning plans are more in line with the user's actual needs by drawing the conditioning reflection value curve obtained based on the low physiological risk signal; the present invention can effectively integrate the data of multiple health devices to provide a comprehensive user health analysis; based on the comprehensive analysis of multiple physiological indicators, provide users with more accurate and personalized diet management and conditioning suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 is a system block diagram of Embodiment 1 of the present invention; Figure 2 It is a flowchart of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1

[0020] See also Figure 1 As shown, the present invention is a healthy diet plan generation system based on multi-device data, comprising: Device data collection module, which is responsible for obtaining the user's physiological index data from a variety of health devices; mainly includes: By measuring the micro-resistance of different parts of the human body, information about the physiological state of the human body can be obtained; this helps to assess the overall health of an individual, including but not limited to the following aspects: Water balance: The water content of the human body will affect the resistance value; microresistance measurement can indirectly reflect the water status of the body, which has a certain reference value for determining whether there are problems such as dehydration or edema; Electrolyte balance: Electrolytes play an important physiological role in the human body, and changes in their concentration will also affect microresistance. By monitoring microresistance, we can understand the balance of electrolytes, such as the levels of sodium, potassium, calcium and other elements; Tissue health: Different tissues have different electrical resistance properties, and microresistance measurements can provide clues about the health of the tissue; for example, abnormal tissue resistance may indicate the presence of inflammation, injury, or disease; Health bracelet: used to monitor the user's daily activity, heart rate, sleep quality, etc.; Human body composition analyzer: used to measure body fat percentage, body water, muscle mass, etc.; Blood pressure meter: used to measure the user's blood pressure level; Blood glucose meter: used to monitor the user's blood glucose level; Uric acid meter: used to monitor the uric acid level in the user's body; Data storage module, which is used to store all data collected from the above devices. Data storage can use local database or cloud storage to ensure data security and availability; Data analysis module, which comprehensively analyzes data collected from multiple devices to evaluate the user's overall health status; In some implementation schemes, the collected device data are compared with the corresponding device data range values; If the device data is within the device data range, a device data qualified signal is generated; If the device data is not within the device data range, a device data unqualified signal is generated; Based on the unqualified signal of the equipment data, the collected equipment data is further analyzed for trend warning. The specific analysis process is as follows; When a signal of unqualified equipment data is obtained, the equipment data corresponding to the signal of unqualified equipment data is analyzed to obtain the first warning value of the equipment data and the second warning value of the equipment data, which are marked as ZY1 and ZY2 respectively. , the equipment data warning value ZZY is calculated, where a1 and a2 are weight ratio coefficients, a1+a2=1, a1 is 0.67, and a2 is 0.33; The process of obtaining the first warning value of equipment data is as follows: Obtain the device data corresponding to the device data unqualified signal, mark it as unqualified device data, set the analysis cycle, obtain the unqualified device data at the end of the analysis cycle time, calculate the difference between the unqualified device data at the end of the analysis cycle time and the preset device warning data, and take the absolute value to obtain the unqualified device data warning difference; Calculate the ratio of the unqualified equipment data warning difference to the preset equipment warning data to obtain the first warning value of the equipment data; The process of obtaining the second warning value of equipment data is as follows: The device data corresponding to the device data unqualified signal is obtained, marked as unqualified device data, the analysis period is set, the slope angle corresponding to the unqualified device data at the end of the analysis period time is obtained, and marked as the unqualified slope angle of unqualified device data in the analysis period; the slope angle is obtained by taking the unqualified device data at the end of the analysis period time as a tangent and measuring the angle between the tangent and the horizontal line; Calculate the ratio of the unqualified slope angle of unqualified equipment data in the analysis period to the preset angle to obtain the second warning value of the equipment data; Compare the device data warning value ZZY with the corresponding device data warning threshold; If the equipment data warning value ZZY is greater than or equal to the equipment data warning threshold, an equipment data warning signal is generated; If the equipment data warning value ZZY is less than the equipment data warning threshold, a non-warning signal of equipment data is generated; Further analysis based on the obtained equipment data warning signals; Obtain the device data corresponding to any two device data warning signals, and obtain the time when the device data unqualified signal is generated, and mark it as the device data unqualified signal generation time; Calculate the difference between the generation times of any two device data failure signals to obtain the device data failure signal generation time difference; Compare the device data unqualified signal generation time difference value with the device data unqualified signal generation time difference threshold; If the device data unqualified signal generation time difference value is greater than or equal to the device data unqualified signal generation time difference threshold value, then a device data related impact small signal is generated; the device data related impact small signal indicates that the probability of correlation between the data corresponding to the two devices having an abnormal situation is low; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, a continue analysis signal is generated; Based on the continued analysis signal, the device data corresponding to any two device data warning signals are obtained, and the slope within the analysis period is marked as the device data warning signal generation slope; Calculate the difference between any two device data warning signal generation slopes to obtain the device data warning signal generation slope difference; comparing the device data warning signal generation slope difference value with the device data warning signal generation slope difference threshold; If the device data unqualified signal generation time difference value is greater than or equal to the device data unqualified signal generation time difference threshold, then a device data related impact small signal is generated; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, then a device data correlation large impact signal is generated; the device data correlation large impact means that the probability of correlation between the data corresponding to the two devices having an abnormal situation is high; Get the number of large influencing signals related to the generated device data, denoted as Ny, through the formula , calculate the device data association impact ratio BG; where n represents the number of types of user physiological indicator data obtained, It means taking any two elements from n different elements and forming a group; Compare the obtained device data association impact ratio BG with the device data association impact ratio threshold; If the device data association impact ratio BG is greater than or equal to the device data association impact ratio threshold, a physiological risk high signal is generated; If the device data association impact ratio BG is less than the device data association impact ratio threshold, a physiological risk low signal is generated; Personalized diet and conditioning advice module, which generates personalized diet management and conditioning advice for users based on data analysis results; In some embodiments, when a device data warning signal, a device data non-warning signal, a physiological risk high signal, or a physiological risk low signal is obtained, When the device data does not indicate a warning signal, continue to collect and analyze the user's physiological indicator data; When physiological risk signals are high, users are advised to go to the hospital for examination in time; When a low physiological risk signal is received, personalized dietary management and conditioning suggestions are generated for the user, and a dietary plan that suits the user's health status can be recommended, including food types, intake, nutrient distribution, etc.; User interface module: This module provides users with a visual interface, where users can view their health data, analysis results, and dietary recommendations through mobile devices (such as mobile phones, tablets) or computers. The user interface should be simple and intuitive, and easy for users to operate and view.

[0021] Feedback adjustment module, which draws the conditioning response value curve based on the physiological risk low signal to ensure that the diet management and conditioning plan are more in line with the actual needs of the user; In some embodiments, when a low physiological risk signal is obtained, an adjustment period is set, the dietary intake and device data warning value ZZY of the user in the adjustment period are obtained, and the device data warning value ZZY and the dietary intake at the same time point are calculated to obtain a conditioning reflection value; a two-dimensional coordinate system is constructed with the time of the adjustment period as the X-axis and the conditioning reflection value as the Y-axis, and the conditioning reflection values ​​of all time points in the adjustment period are substituted into the coordinate system to draw a conditioning reflection value curve; Obtain the slope of each point of the maintenance response value curve, and then perform mean calculation to obtain the maintenance response slope; If the slope of the conditioning reflection is greater than or equal to 0, it means that the dietary intake has a bad effect on its physiological indicators, and it is recommended that the user be hospitalized for examination in time; If the adjustment response slope is less than 0 and the adjustment response slope is greater than the adjustment response slope threshold, it means that the dietary intake has a certain impact on the physiological index, and the user is advised to increase the dietary intake to further adjust the physiological index; If the conditioning response slope is less than the conditioning response slope threshold, it means that the dietary intake has a good effect on its physiological indicators and the physiological indicators meet normal requirements.

[0022] Example 2

[0023] See also Figure 2 As shown, the present invention is a method for generating a healthy diet plan based on multi-device data, comprising the following steps: Step 1: Responsible for obtaining the user's physiological indicator data from a variety of health devices; Step 2: Comprehensively analyze the data collected from multiple devices to evaluate the user's overall health status; The specific process of comprehensive analysis is as follows: Compare the collected device data with the corresponding device data range values ​​respectively; If the device data is within the device data range, a device data qualified signal is generated; If the device data is not within the device data range, a device data unqualified signal is generated; Based on the unqualified signal of the equipment data, the collected equipment data is further analyzed for trend warning. The specific analysis process is as follows; When a signal of unqualified equipment data is obtained, the equipment data corresponding to the signal of unqualified equipment data is analyzed to obtain the first warning value of the equipment data and the second warning value of the equipment data, which are marked as ZY1 and ZY2 respectively. , the equipment data warning value ZZY is calculated, where a1 and a2 are weight ratio coefficients, a1+a2=1, a1 is 0.67, and a2 is 0.33; The process of obtaining the first warning value of equipment data is as follows: Obtain the device data corresponding to the device data unqualified signal, mark it as unqualified device data, set the analysis cycle, obtain the unqualified device data at the end of the analysis cycle time, calculate the difference between the unqualified device data at the end of the analysis cycle time and the preset device warning data, and take the absolute value to obtain the unqualified device data warning difference; Calculate the ratio of the unqualified equipment data warning difference to the preset equipment warning data to obtain the first warning value of the equipment data; The process of obtaining the second warning value of equipment data is as follows: The device data corresponding to the device data unqualified signal is obtained, marked as unqualified device data, the analysis period is set, the slope angle corresponding to the unqualified device data at the end of the analysis period time is obtained, and marked as the unqualified slope angle of unqualified device data in the analysis period; the slope angle is obtained by taking the unqualified device data at the end of the analysis period time as a tangent and measuring the angle between the tangent and the horizontal line; Calculate the ratio of the unqualified slope angle of unqualified equipment data in the analysis period to the preset angle to obtain the second warning value of the equipment data; Compare the device data warning value ZZY with the corresponding device data warning threshold; If the equipment data warning value ZZY is greater than or equal to the equipment data warning threshold, an equipment data warning signal is generated; If the equipment data warning value ZZY is less than the equipment data warning threshold, a non-warning signal of equipment data is generated; Further analysis based on the obtained equipment data warning signals; Obtain the device data corresponding to any two device data warning signals, and obtain the time when the device data unqualified signal is generated, and mark it as the device data unqualified signal generation time; Calculate the difference between the generation times of any two device data failure signals to obtain the device data failure signal generation time difference; Compare the device data unqualified signal generation time difference value with the device data unqualified signal generation time difference threshold; If the device data unqualified signal generation time difference value is greater than or equal to the device data unqualified signal generation time difference threshold value, then a device data related impact small signal is generated; the device data related impact small signal indicates that the probability of correlation between the data corresponding to the two devices having an abnormal situation is low; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, a continue analysis signal is generated; Based on the continued analysis signal, the device data corresponding to any two device data warning signals are obtained, and the slope within the analysis period is marked as the device data warning signal generation slope; Calculate the difference between any two device data warning signal generation slopes to obtain the device data warning signal generation slope difference; comparing the device data warning signal generation slope difference value with the device data warning signal generation slope difference threshold; If the device data unqualified signal generation time difference value is greater than or equal to the device data unqualified signal generation time difference threshold, then a device data related impact small signal is generated; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, then a device data correlation large impact signal is generated; the device data correlation large impact means that the probability of correlation between the data corresponding to the two devices having an abnormal situation is high; Get the number of large influencing signals related to the generated device data, denoted as Ny, through the formula , calculate the device data association impact ratio BG; where n represents the number of types of user physiological indicator data obtained, It means taking any two elements from n different elements and forming a group; Compare the obtained device data association impact ratio BG with the device data association impact ratio threshold; If the device data association impact ratio BG is greater than or equal to the device data association impact ratio threshold, a physiological risk high signal is generated; If the device data association impact ratio BG is less than the device data association impact ratio threshold, a physiological risk low signal is generated; Step 3: Generate personalized diet management and conditioning suggestions for users based on data analysis results; Step 4: Based on the low physiological risk signal, draw the obtained conditioning response value curve to ensure that the diet management and conditioning plan better meet the actual needs of the user.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0025] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A healthy diet plan generation system based on multi-device data, characterized in that: Includes the following modules: Device data collection module, which is responsible for obtaining the user's physiological indicator data from a variety of health devices; Data analysis module, which comprehensively analyzes the data collected from multiple devices to evaluate the user's overall health status; specifically: Obtain the number of large signals related to the generated device data, and calculate the device data correlation impact ratio BG; If the device data association impact ratio BG is greater than or equal to the device data association impact ratio threshold, a physiological risk high signal is generated; Feedback adjustment module, which draws the adjustment response value curve based on the low physiological risk signal to ensure that the diet management and adjustment plan are more in line with the actual needs of the user.

2. A healthy diet plan generation system based on multi-device data according to claim 1, characterized in that: The process of generating large signals related to equipment data is as follows: Based on the continued analysis signal, the device data corresponding to any two device data warning signals are obtained, and the slope within the analysis period is marked as the device data warning signal generation slope; Calculate the difference between any two device data warning signal generation slopes to obtain the device data warning signal generation slope difference; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold, a device data related large impact signal is generated.

3. A healthy diet plan generation system based on multi-device data according to claim 2, characterized in that: Continue to analyze the signal generation process: Obtain the device data corresponding to any two device data warning signals, and obtain the time when the device data unqualified signal is generated, and mark it as the device data unqualified signal generation time; Calculate the difference between the generation times of any two device data failure signals to obtain the device data failure signal generation time difference; If the device data unqualified signal generation time difference value is less than the device data unqualified signal generation time difference threshold value, a continue analysis signal is generated.

4. A healthy diet plan generation system based on multi-device data according to claim 3, characterized in that: The generation process of equipment data warning signal is as follows: If the equipment data warning value ZZY is greater than or equal to the equipment data warning threshold, an equipment data warning signal is generated.

5. A healthy diet plan generation system based on multi-device data according to claim 4, characterized in that: The calculation process of the equipment data warning value ZZY is: Compare the collected device data with the corresponding device data range values ​​respectively; If the device data is not within the device data range, a device data unqualified signal is generated; Based on the unqualified signal of the equipment data, the collected equipment data is further analyzed for trend warning. The specific analysis process is as follows; When an equipment data failure signal is obtained, the equipment data corresponding to the equipment data failure signal is analyzed to obtain a first equipment data warning value and a second equipment data warning value, which are marked as ZY1 and ZY2 respectively, and the equipment data warning value ZZY is calculated.

6. A healthy diet plan generation system based on multi-device data according to claim 5, characterized in that: The process of obtaining the first warning value of equipment data is as follows: Obtain the device data corresponding to the device data unqualified signal, mark it as unqualified device data, set the analysis cycle, obtain the unqualified device data at the end of the analysis cycle time, calculate the difference between the unqualified device data at the end of the analysis cycle time and the preset device warning data, and take the absolute value to obtain the unqualified device data warning difference; The ratio of the unqualified equipment data warning difference to the preset equipment warning data is calculated to obtain the first warning value of the equipment data.

7. A healthy diet plan generation system based on multi-device data according to claim 6, characterized in that: The process of obtaining the second warning value of equipment data is as follows: The device data corresponding to the device data unqualified signal is obtained, marked as unqualified device data, the analysis period is set, the slope angle corresponding to the unqualified device data at the end of the analysis period time is obtained, and marked as the unqualified slope angle of unqualified device data in the analysis period; the slope angle is obtained by taking the unqualified device data at the end of the analysis period time as a tangent and measuring the angle between the tangent and the horizontal line; The ratio of the unqualified slope angle of the unqualified equipment data in the analysis period to the preset angle is calculated to obtain the second warning value of the equipment data.

8. The healthy diet plan generation system based on multi-device data according to claim 1, characterized in that: In the feedback adjustment module, the slope of each point of the adjustment response value curve is obtained, and then the mean value is calculated to obtain the adjustment response slope; If the slope of the adjustment response is greater than or equal to 0, it is recommended that the user be hospitalized for examination in time; If the adjustment response slope is less than 0 and the adjustment response slope is greater than the adjustment response slope threshold, it is recommended that the user increase dietary intake to further adjust the physiological indicators.

9. A healthy diet plan generation system based on multi-device data according to claim 8, characterized in that: The construction process of the conditioning response value curve is: When a low physiological risk signal is obtained, an adjustment period is set to obtain the user's dietary intake and device data warning value ZZY during the adjustment period, and the ratio of the device data warning value ZZY to the dietary intake at the same time point is calculated to obtain the maintenance response value; a two-dimensional coordinate system is constructed with the adjustment period time as the X-axis and the maintenance response value as the Y-axis, and the maintenance response values ​​of all time points in the adjustment period are substituted into the coordinate system to draw a maintenance response value curve.

10. A method for generating a healthy diet plan based on multi-device data, characterized in that: The following steps are involved: Step 1: Responsible for obtaining the user's physiological indicator data from a variety of health devices; Step 2: Comprehensively analyze the data collected from multiple devices to evaluate the user's overall health status; Step 3: Generate personalized diet management and conditioning suggestions for users based on data analysis results; Step 4: Based on the low physiological risk signal, draw the obtained conditioning response value curve to ensure that the diet management and conditioning plan better meet the actual needs of the user.

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