MALS human body function evaluation method and system based on big data

Abstract

The invention discloses an MALS human body function evaluation method and system based on big data, and relates to the technical field of human body function evaluation. According to the MALS human body function assessment method based on big data, human body function data of a to-be-assessed object is obtained and comprehensively analyzed, a human body function basic scoring index, a human body function pre-exercise scoring index, a human body function post-exercise scoring index and a human body function exercise change scoring index are obtained, and then comprehensive analysis is carried out, so that the human body function assessment accuracy is improved. And finally, comparing the human body function comprehensive scoring index with a preset evaluation interval to obtain an evaluation grade. Through introduction of comprehensive analysis before and after movement and static data, the limitation that only static health data is relied on traditionally is broken through, and the human body function state can be dynamically evaluated; and particularly, in the aspect of functional change after exercise, the health conditions of the individual in different activity states can be effectively reflected, so that the evaluation result is more comprehensive and scientific.

Description

Technical Field

[0001] The present invention relates to the technical field of human function assessment, and in particular to a MALS human function assessment method and system based on big data. Background Art

[0002] With the continuous development of health management and sports science, how to scientifically and comprehensively evaluate human functions has become an important research direction. Traditional human function evaluation methods are mostly based on static indicators, such as static blood pressure, heart rate, body fat, etc., or only focus on a certain stage of the exercise process. These methods can reflect the individual's health status to a certain extent, but due to the lack of analysis of dynamic changes and comprehensive impacts, it is often difficult to comprehensively and accurately evaluate the actual level of human function.

[0003] Prior art, such as the invention patent application with announcement number: CN113506627B, discloses a method and system for evaluating human physiological functions at night, the steps of which are: making a comprehensive prediction and assessment of the functional state of each system of the human body at different time points based on the collected physiological function data such as human body temperature, which can meet the needs of chronobiological data analysis, statistics, analysis and calculation, and realize the drawing of phase diagrams and mutual relationship diagrams of various functional states of the human body; establishing a human biological clock database through a human physiological and biochemical rhythm parameter relationship model, and being able to evaluate and predict the night combat capability of special personnel based on the collected physiological characteristic data; providing experimental data and theoretical basis for instructors to implement time-selected exercise training programs and adjust the biological rhythms of team members; and then scientifically increasing or decreasing the amount of exercise and changing the training methods to improve the training efficiency and maximize the biological movement potential of each team member; being able to accurately and artificially fine-tune the functional state level and time of the team members so that their best competitive state is at the critical moment of the game, and at the same time effectively overcome the jet lag reaction.

[0004] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. First, in the existing health assessment methods, usually only static data or single-stage assessment are focused on, which makes it difficult to fully reflect the dynamic changes of human body functions. Many methods only rely on static physiological indicators and ignore the functional state after exercise, making it difficult to accurately assess the health status of individuals in different activity states, which easily leads to the lack of comprehensiveness and timeliness of the assessment results. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a MALS human function assessment method and system based on big data, which solves the problem that the prior art only relies on static data and single-stage assessment, and is difficult to fully reflect the dynamic changes of human function.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a MALS human function assessment method based on big data, comprising the following steps: obtaining human function data of an object to be assessed, the human function data including basic human function data, initial human function data before setting the exercise mode, and human function movement data after setting the exercise mode; comprehensively analyzing the human function data of the object to be assessed to obtain a basic human function scoring index, a human function pre-exercise scoring index, a human function post-exercise scoring index, and a human function movement change scoring index of the object to be assessed; comprehensively analyzing the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be assessed to obtain a human function movement scoring index of the object to be assessed, and comprehensively analyzing the basic human function scoring index of the object to be assessed to obtain a comprehensive human function scoring index of the object to be assessed; judging and analyzing the comprehensive human function scoring index of the object to be assessed and a preset human function assessment interval to obtain a human function assessment level of the object to be assessed.

[0007] Furthermore, the basic human function data include family genetic risk score, bone density score, and body fat status score; the initial human function data include static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value; the human function exercise data include exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value.

[0008] Furthermore, the specific formula for calculating the comprehensive scoring index of the human body function of the subject to be evaluated is as follows: Among them, JnZ is the comprehensive scoring index of human function of the object to be evaluated, e is a natural constant, JcP is the basic scoring index of human function of the object to be evaluated, δ 1 is the comprehensive coefficient of human function stored in the database, JyD is the human function movement score index of the object to be evaluated, δ 2 is the human body function motion coefficient stored in the database, δ 1 +δ 2 =1, π is the ratio of a circle to a circle.

[0009] Furthermore, the specific steps for obtaining the basic human function score index of the subject to be evaluated are as follows: comprehensively analyzing the family genetic risk score, bone density score, and body fat status score of the subject to be evaluated to obtain the basic human function score index of the subject to be evaluated; wherein, the specific formula for calculating the basic human function score index of the subject to be evaluated is as follows: Among them, JcP is the basic scoring index of human function of the subject to be evaluated, YcF is the family genetic risk score of the subject to be evaluated, ξ 1 is the genetic scoring coefficient stored in the database, GmD is the bone density score of the subject to be evaluated, ξ 2 is the bone density scoring coefficient stored in the database, TzZ is the body fat status score of the subject to be evaluated, ξ 3 is the body fat score coefficient stored in the database, ξ 1 +ξ 2 +ξ 3 =1, e is a natural constant.

[0010] Furthermore, the specific steps for obtaining the pre-exercise scoring index of the human function of the object to be evaluated are as follows: the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated are respectively standardized; the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, and static skin temperature value of the object to be evaluated after the standardization are comprehensively analyzed to obtain the static health index of the human function of the object to be evaluated; the static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated after the standardization are comprehensively analyzed to obtain the static burden index of the human function of the object to be evaluated; the static health index and static burden index of the human function of the object to be evaluated are comprehensively analyzed to obtain the pre-exercise scoring index of the human function of the object to be evaluated; wherein, the specific formulas for calculating the static health index, static burden index, and pre-exercise scoring index of the human function of the object to be evaluated are as follows: Among them, QjK is the static health index of the human body function of the object to be evaluated, QxF is the static blood flow distribution index of the object to be evaluated after standardization, θ 1 is the static blood flow distribution coefficient stored in the database, QjB is the static muscle oxygen saturation value of the object to be evaluated after standardization, θ 2 is the static muscle oxygen saturation coefficient stored in the database, QrS is the static lactate value of the subject to be evaluated after standardization, θ 3 is the static lactic acid coefficient stored in the database, QpW is the static skin temperature value of the object to be evaluated after standardization, θ 4 is the static skin temperature coefficient stored in the database, θ 1 +θ 2 +θ 3 +θ 4 =1, QjF is the static burden index of human body function of the subject to be evaluated, QxL is the static heart rate value of the subject to be evaluated after standardization, ψ 1is the static heart rate coefficient stored in the database, QxY is the static blood pressure value of the subject to be evaluated after standardization, ψ 2 is the static blood pressure coefficient stored in the database, QxP is the static respiratory rate value of the subject to be evaluated after standardization, ψ 3 is the static respiratory rate coefficient stored in the database, QjY is the static muscle hardness value of the object to be evaluated after standardization, ψ 4 is the static muscle stiffness coefficient stored in the database, ψ 1 +ψ 2 +ψ 3 +ψ 4 =1, QyD is the pre-exercise scoring index of the human function of the subject to be evaluated.

[0011] Furthermore, the specific steps for obtaining the post-exercise scoring index of the human function of the object to be evaluated are as follows: the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory frequency value, and exercise muscle hardness value of the object to be evaluated are respectively standardized; the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the object to be evaluated after the standardization are comprehensively analyzed to obtain the human function exercise health index of the object to be evaluated; the exercise heart rate value, exercise blood pressure value, exercise respiratory frequency value, and exercise muscle hardness value of the object to be evaluated after the standardization are comprehensively analyzed to obtain the human function exercise burden index of the object to be evaluated; the human function exercise health index and the human function exercise burden index of the object to be evaluated are comprehensively analyzed to obtain the human function pre-exercise scoring index of the object to be evaluated; wherein, the specific formulas for calculating the human function exercise health index, the human function exercise burden index, and the human function pre-exercise scoring index of the object to be evaluated are as follows: Among them, HjK is the human functional sports health index of the subject to be evaluated, HxF is the sports blood flow distribution index of the subject to be evaluated after standardized processing, α 1 is the exercise blood flow distribution coefficient stored in the database, HjB is the exercise muscle oxygen saturation value of the object to be evaluated after standardization, α 2 is the sports muscle oxygen saturation coefficient stored in the database, HrS is the sports lactate value of the subject to be evaluated after standardized processing, α 3 is the sports lactate coefficient stored in the database, HpW is the sports skin temperature value of the subject to be evaluated after standardization, α 4 is the motion skin temperature coefficient stored in the database, α 1 +α 2 +α 3 +α 4=1, HjF is the human body function exercise burden index of the subject to be evaluated, HxL is the exercise heart rate value of the subject to be evaluated after standardization, β 1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the subject to be evaluated after standardization, β 2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise respiratory rate value of the subject to be evaluated after standardization, β 3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the object to be evaluated after standardization, β 4 is the sports muscle stiffness coefficient stored in the database, β 1 +β 2 +β 3 +β 4 =1, HyD is the pre-exercise scoring index of the human function of the subject to be evaluated.

[0012] Furthermore, the specific steps for obtaining the human function movement change scoring index of the object to be evaluated are as follows: the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, and static skin temperature value of the object to be evaluated after standardization are respectively combined with the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the object to be evaluated after standardization for comprehensive analysis to obtain the human function movement health change index of the object to be evaluated; the static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated after standardization are respectively combined with the exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value of the object to be evaluated after standardization for comprehensive analysis to obtain the human function movement burden change index of the object to be evaluated; the human function movement health change index and the human function movement burden change index of the object to be evaluated are comprehensively analyzed to obtain the human function movement change scoring index of the object to be evaluated.

[0013] Furthermore, the specific formulas for calculating the human function movement health change index, human function movement burden change index, and human function movement change score index of the subject to be evaluated are as follows:

[0014]

[0015] Among them, YjB is the human body function movement health change index of the object to be evaluated, HxF is the movement blood flow distribution index of the object to be evaluated after standardization, and QxF is the static blood flow distribution index of the object to be evaluated after standardization. is the blood flow distribution variation coefficient stored in the database, HjB is the exercise muscle oxygen saturation value of the subject to be evaluated after standardization, QjB is the static muscle oxygen saturation value of the subject to be evaluated after standardization, is the muscle oxygen saturation variation coefficient stored in the database, HrS is the sports lactate value of the subject to be evaluated after standardization, QrS is the static lactate value of the subject to be evaluated after standardization, is the lactate variation coefficient stored in the database, HpW is the exercise skin temperature value of the subject to be evaluated after standardization, QpW is the static skin temperature value of the subject to be evaluated after standardization, is the skin temperature variation coefficient stored in the database, YjF is the human body function exercise burden change index of the subject to be evaluated, HxL is the exercise heart rate value of the subject to be evaluated after standardization, QxL is the static heart rate value of the subject to be evaluated after standardization, η 1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the subject to be evaluated after standardization, QxY is the static blood pressure value of the subject to be evaluated after standardization, η 2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise respiratory rate value of the subject to be evaluated after standardization, QxP is the static respiratory rate value of the subject to be evaluated after standardization, η 3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the object to be evaluated after standardization, QjY is the static muscle hardness value of the object to be evaluated after standardization, η 4 is the sports muscle stiffness coefficient stored in the database, η 1 +η 2 +η 3 +η 4 =1, YdB is the human body function movement change score index of the object to be evaluated.

[0016] Furthermore, the specific formula for calculating the human body function movement score index of the subject to be evaluated is as follows: Among them, JyD is the human function movement score index of the subject to be evaluated, QyD is the human function pre-movement score index of the subject to be evaluated, μ 1 is the pre-exercise scoring coefficient stored in the database, HyD is the post-exercise scoring index of the human function of the subject to be evaluated, μ 2 is the post-exercise scoring coefficient stored in the database, YdB is the human body function movement change scoring index of the object to be evaluated, μ 3 is the motion change scoring coefficient stored in the database, μ 1 +μ 2 +μ 3 =1.

[0017] The MALS human function evaluation system based on big data includes: a data acquisition module, a data analysis module, a comprehensive analysis module, and a judgment analysis module; the data acquisition module is used to acquire the human function data of the object to be evaluated, and the human function data includes basic human function data, initial human function data before setting the exercise mode, and human function movement data after setting the exercise mode; the data analysis module is used to perform comprehensive analysis on the human function data of the object to be evaluated, and obtain the basic human function scoring index, the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be evaluated; the comprehensive analysis module is used to perform comprehensive analysis on the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be evaluated, and perform comprehensive analysis in combination with the basic human function scoring index of the object to be evaluated, and obtain the comprehensive human function scoring index of the object to be evaluated; the judgment analysis module is used to perform judgment analysis on the comprehensive human function scoring index of the object to be evaluated and the preset human function evaluation interval, and obtain the human function evaluation level of the object to be evaluated.

[0018] The present invention has the following beneficial effects:

[0019] (1) The MALS human function assessment method based on big data breaks through the limitation of traditional reliance on static health data by introducing comprehensive analysis of pre-exercise and static data. It can dynamically assess the functional status of the human body, especially the functional changes after exercise. It can effectively reflect the health status of individuals under different activity states, making the assessment results more comprehensive and scientific. This method can provide more accurate data support for personalized health management, exercise program design and disease prevention, and improve the practicality and timeliness of the assessment.

[0020] (2) The MALS human function assessment method based on big data can not only evaluate the health status of individuals before and after exercise by standardizing and comprehensively analyzing static data and exercise data before and after exercise, but also accurately calculate the changes in the burden of exercise on the body, further revealing the individual's adaptability and potential health problems during exercise. This detailed assessment of dynamic changes can help to discover potential health risks in advance and guide individuals to develop reasonable exercise and health intervention plans, thereby effectively improving exercise effectiveness and safety.

[0021] (3) The MALS human function assessment method based on big data combines multi-dimensional data of the human body, such as blood flow distribution, muscle oxygen saturation, lactate value, etc., and comprehensively analyzes static and motion data to provide a more comprehensive reference for human function scoring. By calculating the changes in multiple dimensions and conducting a comprehensive analysis, the accuracy and reliability of the assessment results are ensured, avoiding the assessment bias that is easily caused by a single data indicator. This multi-dimensional data fusion can not only provide a comprehensive scientific basis for health assessment, but also help to formulate more accurate health intervention and exercise recommendation plans.

[0022] (4) The MALS human function assessment system based on big data adopts four modular designs: data acquisition, data analysis, comprehensive analysis and judgment analysis. It can effectively share the tasks of each module and ensure the efficiency and accuracy of the assessment process. The data acquisition module focuses on accurately acquiring human function data, while the data analysis module performs comprehensive data processing. The comprehensive analysis module integrates various data through multi-dimensional evaluation, and the judgment analysis module obtains the final assessment level based on the comprehensive score. The modular design not only simplifies the entire assessment process, but also can be flexibly adjusted and optimized according to different needs to meet the health management needs of different user groups. At the same time, the system has a clear structure and is easy to operate, which is convenient for health managers or doctors to operate efficiently, improving the overall assessment work efficiency and real-time response capabilities.

[0023] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the MALS human function assessment method based on big data of the present invention.

[0025] Figure 2 The present invention is a flowchart of the specific steps of obtaining the pre-exercise scoring index of the human function of the subject to be evaluated in the MALS human function evaluation method based on big data.

[0026] Figure 3 This is a block diagram of the MALS human function assessment system based on big data of the present invention. DETAILED DESCRIPTION

[0027] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0028] First, obtain the human function data of the subject to be evaluated, including basic data, pre-exercise data and post-exercise data. The basic data of human function include family genetic risk, bone density and body fat status; the pre-exercise and post-exercise data include blood flow distribution, muscle oxygen saturation, lactate value, skin temperature, heart rate, blood pressure, respiratory rate and muscle hardness. Perform a comprehensive analysis of the collected data, and calculate the basic score, pre-exercise score, post-exercise score and exercise change score of human function respectively. Then, comprehensively analyze the pre-exercise, post-exercise and change scores to obtain the exercise score of human function, and combine the basic score to obtain the comprehensive score index. Then, compare the comprehensive score with the preset evaluation interval, and finally obtain the human function evaluation level of the subject to be evaluated, and clarify their health status and exercise adaptability.

[0029] See also Figure 1 The embodiment of the present invention provides a technical solution: a MALS human function evaluation method based on big data, comprising the following steps: obtaining human function data of an object to be evaluated, the human function data including basic human function data, initial human function data before setting the exercise mode, and human function movement data after setting the exercise mode; comprehensively analyzing the human function data of the object to be evaluated to obtain a basic human function scoring index, a human function pre-exercise scoring index, a human function post-exercise scoring index, and a human function movement change scoring index of the object to be evaluated; comprehensively analyzing the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be evaluated to obtain a human function movement scoring index of the object to be evaluated, and comprehensively analyzing the basic human function scoring index of the object to be evaluated to obtain a comprehensive human function scoring index of the object to be evaluated; judging and analyzing the comprehensive human function scoring index of the object to be evaluated and a preset human function evaluation interval to obtain a human function evaluation level of the object to be evaluated.

[0030] Among them, the specific implementation examples of the preset human function assessment intervals are as follows:

[0031] Excellent range: [80, 100], indicating that the subject’s human function is excellent and in good health, without the need for special management or intervention.

[0032] Good range: [60, 80), indicating that the human body function state of the subject to be evaluated is relatively stable and the overall condition is good, but there may be some room for improvement.

[0033] General range: [40, 60), indicating that the subject’s human function status has mild hidden dangers and requires appropriate health management intervention.

[0034] Poor range: [0, 40), indicating that the human body function status of the subject to be evaluated is not ideal, there is a health risk, and attention needs to be paid and certain measures should be taken.

[0035] The basic data of human function include family genetic risk score, bone density score, and body fat status score. The initial data of human function include static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value. The human function exercise data include exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value.

[0036] Among them, the family genetic risk score can be obtained through the following steps: perform whole genome sequencing or specific genomic site testing on the subject to be evaluated through a genetic testing agency (such as 23andMe, BGI, etc.), select risk gene sites related to disease or health (such as cardiovascular disease, diabetes, cancer, etc.), and the risk value returned by each test site represents a relative risk (Relative Risk, RR), and the risk value of each test site is weighted and summed to obtain the family genetic risk score.

[0037] The bone density score can be obtained through the following steps: use professional instruments to detect the bone density of the subject to be evaluated (usually the lumbar spine, hip and other parts, and the results are expressed in the form of T scores), and map the bone density test value (T score) into a health score (i.e., bone density score): bone density score = ((T+2.5) / 3.5)*100, where T is the T score of bone density, and when T≥-1, it means that the bone density score is 80-100, when -2.5<T<-1, it means that the bone density score is 60-79 (less bone mass), and when T≤-2.5, it means that the bone density score is below 60 (osteoporosis).

[0038] The body fat status score can be obtained by the following steps: by using a smart body fat scale and a soft tape measure to measure the body fat percentage and waist-to-hip ratio of the subject to be evaluated, and obtaining the healthy body fat percentage range (including the minimum healthy body fat percentage and the maximum healthy body fat percentage), the healthy waist-to-hip ratio range (including the minimum healthy waist-to-hip ratio and the maximum healthy waist-to-hip ratio), and then calculating the body fat status score as follows:

[0039] Body fat status score = 100-(0.5*((body fat percentage of the subject to be evaluated - minimum healthy body fat percentage) / (maximum healthy body fat percentage - minimum healthy body fat percentage))+0.5*((minimum healthy waist-to-hip ratio of the subject to be evaluated) / (maximum healthy waist-to-hip ratio - minimum healthy waist-to-hip ratio)))*100, and when the body fat status score is greater than 80, it indicates health; when 80≥body fat status score≥60, it indicates sub-health; when the body fat status score is less than 60, it indicates unhealthy.

[0040] The blood flow distribution index can be obtained by using near-infrared spectroscopy (NIRs) or laser Doppler imaging equipment to measure the blood flow ratio of each preset part of the human body (i.e., blood flow of the current part / total blood flow of the whole body), and performing weighted analysis on the blood flow ratio measurement values ​​of each preset part of the human body. When it is static, it is used to evaluate the blood supply capacity of the internal organs and reflect the basic circulatory health. After exercise, it is used to reflect the body's ability to adapt to exercise needs (whether it can quickly adjust blood flow to the muscles to meet oxygen and energy needs).

[0041] The muscle oxygen saturation value can be obtained by using a muscle oxygen monitoring device (such as NI RS) to measure the oxygen saturation of each preset human muscle part, and performing a weighted analysis on the oxygen saturation measurement value of each preset human muscle part. It is used to reflect the blood oxygen supply efficiency of the muscle tissue when it is static, and it is used to reflect the oxygen supply capacity and oxygen utilization efficiency of the muscle under high-load conditions after exercise.

[0042] The lactate value can be measured using a portable lactate meter. When the lactate value is static, it is used to reflect the basal metabolism and anaerobic metabolism levels (normal people's static lactate should be lower than 2 mmol / L). When the lactate value is post-exercise, it is used to reflect the anaerobic metabolic burden and lactate clearance capacity.

[0043] The skin temperature value can be obtained by using an infrared thermometer or a thermal imager to measure the temperature of each preset measurement point on the human skin surface, and performing a weighted analysis on the temperature measurement value of each preset measurement point on the human skin surface. When it is static, it is used to reflect the basic peripheral circulation capacity (a higher normal skin temperature indicates better peripheral circulation), and after exercise, it is used to reflect the heat dissipation capacity and blood circulation regulation capacity.

[0044] The heart rate value can be measured by using a heart rate monitoring device (smart watch, heart rate belt). When it is static, it is used to reflect the basal metabolic rate and the heart's pumping efficiency. When it is after exercise, it is used to reflect the exercise intensity and heart load capacity.

[0045] Blood pressure values ​​can be obtained by measuring static blood pressure (systolic pressure / diastolic pressure) using an electronic sphygmomanometer. It is used to reflect vascular health when static (ideal range of systolic pressure / diastolic pressure: 120 / 80 mmHg), and it is used to reflect vascular elasticity and neural regulation ability after exercise.

[0046] The respiratory rate value can be measured by using a respiratory monitoring device (portable respiratory sensor or chest strap). It is used to reflect the stability of the respiratory center and the basal metabolic level when at rest (resting respiratory rate for adults: 12-16 times / minute), and it is used to reflect the oxygen demand and regulatory capacity of the respiratory center after exercise.

[0047] The muscle hardness value can be obtained by using a portable muscle hardness measuring instrument (such as Myoton) to measure the hardness of each preset human muscle part, and performing weighted analysis on the hardness measurement values ​​of each preset human muscle part. It is used to reflect muscle tension and relaxation state when in static state, and it is used to reflect muscle fatigue and recovery ability after exercise.

[0048] Specifically, the specific formula for calculating the comprehensive scoring index of human body function of the subject to be evaluated is as follows: Wherein, JnZ is the comprehensive scoring index of human body function of the object to be evaluated, e is a natural constant, which is 2.718 in this embodiment, JcP is the basic scoring index of human body function of the object to be evaluated, δ 1 is the comprehensive coefficient of human function stored in the database, JyD is the human function movement score index of the object to be evaluated, δ 2 is the human body function motion coefficient stored in the database, δ 1 +δ 2 =1, π is the ratio of a circle to a circle, and in this embodiment, its value is 3.14.

[0049] In this embodiment, the formula for calculating the comprehensive scoring index of human function of the object to be evaluated combines the basic scoring index and the exercise scoring index of human function, dynamically integrates multiple key parameters, including the comprehensive coefficient and exercise coefficient in the database, and can comprehensively evaluate the functional level of the individual under different exercise states. The application of parameters in the formula, such as the natural constant e and the pi, ensures the scientificity and universality of the calculation process, thereby improving the accuracy and stability of the evaluation results. The formula uses the parameter human function comprehensive coefficient δ 1 and the human body function motion coefficient δ 2Weights are assigned to the comprehensive index and exercise index to flexibly adjust their impact on the overall evaluation results. This design can not only reflect the basic state of individual function, but also highlight the key role of functional changes after exercise, which helps to identify potential problems that may exist during exercise and provide more accurate decision-making basis for health management. The formula combines the core calculation with the coefficients stored in the database, so that the evaluation process can flexibly adjust the coefficient value according to different individuals or specific scenarios. This scalability ensures that the evaluation method can be applied to a variety of scenarios, such as health monitoring, exercise effect evaluation, etc., which improves the versatility and practical value of the system.

[0050] Specifically, the specific steps for obtaining the basic human function score index of the subject to be evaluated are as follows: comprehensively analyze the family genetic risk score, bone density score, and body fat status score of the subject to be evaluated to obtain the basic human function score index of the subject to be evaluated.

[0051] The specific formula for calculating the basic scoring index of human function of the subject to be evaluated is as follows: Among them, JcP is the basic scoring index of human function of the subject to be evaluated, YcF is the family genetic risk score of the subject to be evaluated, ξ 1 is the genetic scoring coefficient stored in the database, GmD is the bone density score of the subject to be evaluated, ξ 2 is the bone density scoring coefficient stored in the database, TzZ is the body fat status score of the subject to be evaluated, ξ 3 is the body fat score coefficient stored in the database, ξ 1 +ξ 2 +ξ 3 =1, e is a natural constant, and in this embodiment, its value is 2.718.

[0052] In this embodiment, the formula for calculating the basic score index of human function of the subject to be evaluated comprehensively analyzes the family genetic risk score, bone density score and body fat status score, and uses the weight parameter (genetic score coefficient ξ 1 , bone density scoring coefficient ξ 2 , body fat score coefficient ξ 3 ) adjusts the importance of each indicator. This multi-dimensional integration approach can fully reflect the overall status of an individual in terms of genetic risk, bone health and fat status, significantly improving the comprehensiveness and scientificity of the evaluation results. By introducing an adjustable weight parameter genetic score coefficient ξ 1 , bone density scoring coefficient ξ 2 , body fat score coefficient ξ 3The formula can highlight the impact of a certain indicator according to specific evaluation needs. For example, for evaluation subjects with more serious bone density problems, the weight of the bone density score can be increased. This flexibility enables the evaluation method to adapt to the specific needs of different individuals or application scenarios. The inverse tangent function and the natural constant e are introduced in the formula to effectively control the growth trend of the score, so that the calculation result has a good interval limit and mathematical stability. In addition, the introduction of e-1 avoids the situation where the denominator is zero, and improves the robustness and applicability of the formula. This optimized design ensures that the scoring results can reflect data differences and avoid the influence of extreme values, thereby enhancing the accuracy and credibility of the evaluation. The weights and scoring coefficients involved in the formula are all from the standard values ​​stored in the database, ensuring the reliability and authority of the evaluation results. This design method based on big data provides more scientific theoretical support for individualized health assessment and lays the foundation for further optimization of the evaluation method.

[0053] Specifically, Figure 2 As shown, the specific steps for obtaining the pre-exercise scoring index of the human function of the object to be evaluated are as follows: the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated are respectively standardized (i.e., de-unitized); the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, and static skin temperature value of the object to be evaluated after the standardized processing are comprehensively analyzed to obtain the static health index of the human function of the object to be evaluated; the static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated after the standardized processing are comprehensively analyzed to obtain the static burden index of the human function of the object to be evaluated; the static health index and the static burden index of the human function of the object to be evaluated are comprehensively analyzed to obtain the pre-exercise scoring index of the human function of the object to be evaluated.

[0054] The specific formulas for calculating the static health index of human function, the static burden index of human function, and the pre-exercise scoring index of human function of the subject to be evaluated are as follows: Among them, QjK is the static health index of the human body function of the object to be evaluated, QxF is the static blood flow distribution index of the object to be evaluated after standardization, θ 1 is the static blood flow distribution coefficient stored in the database, QjB is the static muscle oxygen saturation value of the object to be evaluated after standardization, θ 2 is the static muscle oxygen saturation coefficient stored in the database, QrS is the static lactate value of the subject to be evaluated after standardization, θ 3 is the static lactic acid coefficient stored in the database, QpW is the static skin temperature value of the object to be evaluated after standardization, θ 4is the static skin temperature coefficient stored in the database, θ 1 +θ 2 +θ 3 +θ 4 =1, QjF is the static burden index of human body function of the subject to be evaluated, QxL is the static heart rate value of the subject to be evaluated after standardization, ψ 1 is the static heart rate coefficient stored in the database, QxY is the static blood pressure value of the subject to be evaluated after standardization, ψ 2 is the static blood pressure coefficient stored in the database, QxP is the static respiratory rate value of the subject to be evaluated after standardization, ψ 3 is the static respiratory rate coefficient stored in the database, QjY is the static muscle hardness value of the object to be evaluated after standardization, ψ 4 is the static muscle stiffness coefficient stored in the database, ψ 1 +ψ 2 +ψ 3 +ψ 4 =1, QyD is the pre-exercise scoring index of the human function of the subject to be evaluated.

[0055] In this implementation scheme, the static physiological indicators of the object to be evaluated (such as blood flow distribution, muscle oxygen saturation, lactate value, etc.) are divided into health-related indicators and burden-related indicators for analysis, and the static health index and static burden index are calculated respectively. This hierarchical analysis method can more scientifically reflect the individual's physiological state. The static health index focuses on evaluating the positive factors of function, while the static burden index focuses on the body's stress and burden. The combination of the two can more comprehensively reflect the overall state of the human body under static conditions. All static physiological indicators are standardized to eliminate the influence of units and dimensions between different indicators, so that various data are comparable under the same evaluation system. This processing method not only improves the fairness and consistency of the calculation results, but also ensures the accuracy and reliability of subsequent comprehensive analysis, providing multidimensional indicators. The fusion analysis provides a solid foundation. The standardized coefficients stored in the database (such as static blood flow distribution coefficient, muscle oxygen saturation coefficient, etc.) are introduced into the formula to ensure the scientificity and authority of the evaluation results. These coefficients can objectively reflect the influence weight of physiological indicators on health status after statistics and analysis of a large number of samples, making the calculation results more in line with reality. In addition, this big data-based evaluation method improves the accuracy and universality of the results. The static health index and the static burden index are comprehensively analyzed, and the human body function pre-exercise score index is further calculated. This method not only focuses on positive health factors (such as blood flow distribution and muscle oxygen saturation), but also takes into account the burden and pressure of the body (such as heart rate, blood pressure, etc.), making the evaluation results more comprehensive and balanced, and can provide multi-faceted references for individualized health management and intervention plans.

[0056] Specifically, the specific steps for obtaining the post-exercise scoring index of human function of the subject to be evaluated are as follows: standardize the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory frequency value, and exercise muscle hardness value of the subject to be evaluated (i.e., remove the unit); conduct a comprehensive analysis of the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the subject to be evaluated after the standardization to obtain the human function exercise health index of the subject to be evaluated; conduct a comprehensive analysis of the exercise heart rate value, exercise blood pressure value, exercise respiratory frequency value, and exercise muscle hardness value of the subject to be evaluated after the standardization to obtain the human function exercise burden index of the subject to be evaluated; conduct a comprehensive analysis of the human function exercise health index and the human function exercise burden index of the subject to be evaluated to obtain the human function pre-exercise scoring index of the subject to be evaluated.

[0057] The specific formulas for calculating the human function exercise health index, human function exercise burden index, and human function pre-exercise score index of the subject to be evaluated are as follows: Among them, HjK is the human functional sports health index of the subject to be evaluated, HxF is the sports blood flow distribution index of the subject to be evaluated after standardized processing, α 1 is the exercise blood flow distribution coefficient stored in the database, HjB is the exercise muscle oxygen saturation value of the object to be evaluated after standardization, α 2 is the sports muscle oxygen saturation coefficient stored in the database, HrS is the sports lactate value of the subject to be evaluated after standardized processing, α 3 is the sports lactate coefficient stored in the database, HpW is the sports skin temperature value of the subject to be evaluated after standardization, α 4 is the motion skin temperature coefficient stored in the database, α 1 +α 2 +α 3 +α 4 =1, HjF is the human body function exercise burden index of the subject to be evaluated, HxL is the exercise heart rate value of the subject to be evaluated after standardization, β 1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the subject to be evaluated after standardization, β 2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise respiratory rate value of the subject to be evaluated after standardization, β 3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the object to be evaluated after standardization, β 4 is the sports muscle stiffness coefficient stored in the database, β 1 +β 2 +β 3 +β4 =1, HyD is the pre-exercise scoring index of the human function of the subject to be evaluated.

[0058] In this implementation scheme, by standardizing key indicators after exercise (such as blood flow distribution, muscle oxygen saturation, lactate value, skin temperature, etc.), and calculating the sports health index and sports burden index respectively, this hierarchical evaluation method can dynamically reflect the health status and physical burden of the human body after exercise. The health index highlights positive indicators, and the burden index captures possible excessive stress reactions. The combination of the two can comprehensively and accurately evaluate the impact of exercise on the body. The sports health index and the sports burden index are comprehensively analyzed to obtain the human function post-exercise scoring index. This method takes into account both positive and negative factors, so that the evaluation not only focuses on the positive effects of exercise (such as increased blood flow and improved muscle oxygen supply), but also can identify potential stress or risks during exercise (such as Heart rate is too fast, blood pressure is too high, etc.), thereby providing more comprehensive guidance for scientific exercise assessment and intervention. By standardizing (de-unitizing) all exercise indicators, the unfairness caused by differences in units and magnitudes between different indicators is eliminated. This processing method allows multi-dimensional indicators to be integrated in the same evaluation system, thereby ensuring the fairness and consistency of the calculation results, and providing a reliable basis for subsequent comprehensive analysis and scoring. The introduction of exercise-related coefficients stored in the database (such as blood flow distribution coefficient, oxygen saturation coefficient, lactate coefficient, etc.) makes the weight distribution of the formula based on a large amount of sample data, which is highly authoritative. This data-driven method can adjust the evaluation criteria according to different populations or scenarios, thereby improving the applicability and practicality of the method.

[0059] Specifically, the specific steps for obtaining the human function movement change scoring index of the object to be evaluated are as follows: the static blood flow distribution index, static muscle oxygen saturation value, static lactate value, and static skin temperature value of the object to be evaluated after standardization are respectively combined with the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the object to be evaluated after standardization for comprehensive analysis to obtain the human function movement health change index of the object to be evaluated; the static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the object to be evaluated after standardization are respectively combined with the exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value of the object to be evaluated after standardization for comprehensive analysis to obtain the human function movement burden change index of the object to be evaluated; the human function movement health change index and the human function movement burden change index of the object to be evaluated are comprehensively analyzed to obtain the human function movement change scoring index of the object to be evaluated.

[0060] The specific formulas for calculating the human function movement health change index, human function movement burden change index, and human function movement change score index of the subject to be evaluated are as follows:

[0061]

[0062] Among them, YjB is the human body function movement health change index of the object to be evaluated, HxF is the movement blood flow distribution index of the object to be evaluated after standardization, and QxF is the static blood flow distribution index of the object to be evaluated after standardization. is the blood flow distribution variation coefficient stored in the database, HjB is the exercise muscle oxygen saturation value of the subject to be evaluated after standardization, QjB is the static muscle oxygen saturation value of the subject to be evaluated after standardization, is the muscle oxygen saturation variation coefficient stored in the database, HrS is the sports lactate value of the subject to be evaluated after standardization, QrS is the static lactate value of the subject to be evaluated after standardization, is the lactate variation coefficient stored in the database, HpW is the exercise skin temperature value of the subject to be evaluated after standardization, QpW is the static skin temperature value of the subject to be evaluated after standardization, is the skin temperature variation coefficient stored in the database, YjF is the human body function exercise burden change index of the subject to be evaluated, HxL is the exercise heart rate value of the subject to be evaluated after standardization, QxL is the static heart rate value of the subject to be evaluated after standardization, η 1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the subject to be evaluated after standardization, QxY is the static blood pressure value of the subject to be evaluated after standardization, η 2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise respiratory rate value of the subject to be evaluated after standardization, QxP is the static respiratory rate value of the subject to be evaluated after standardization, η 3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the object to be evaluated after standardization, QjY is the static muscle hardness value of the object to be evaluated after standardization, η 4 is the sports muscle stiffness coefficient stored in the database, η 1 +η 2 +η 3 +η 4 =1, YdB is the human body function movement change score index of the object to be evaluated.

[0063] In this implementation plan, by comparing and comprehensively analyzing static indicators with post-exercise indicators, this part of the design can accurately capture the changes in human function before and after exercise, including sports health changes and burden changes. This dynamic change analysis method can reflect the overall positive effects of exercise on the body (such as health improvement) and potential risks (such as increased burden), and provide a scientific basis for a comprehensive evaluation of exercise effects. The design divides exercise changes into a health change index and a burden change index, and independently evaluates them from two dimensions: positive indicators (such as blood flow distribution, oxygen saturation) and burden indicators (such as heart rate, blood pressure, etc.). This detailed evaluation method helps to quantify the positive effects and sources of stress of exercise, which not only avoids the one-sidedness of a single indicator, but also can more accurately locate potential problems in exercise, standardize static and post-exercise physiological indicators, and calculate the degree of change by difference. , eliminating the problem of data incomparability caused by different units or dimensions. This combination of standardization and difference not only ensures the fairness and consistency of the data, but also can more intuitively quantify the changes caused by exercise, and improve the accuracy of the analysis results. The formula uses the variation coefficients stored in the database (such as blood flow distribution variation coefficient, lactate variation coefficient, etc.) to weight the degree of change, ensuring the scientificity and authority of the evaluation results. These variation coefficients are based on a large amount of sample data, which can objectively reflect the actual impact of changes in different indicators on health status, making the evaluation more universal. Through a comprehensive analysis of the exercise health change index and the exercise burden change index, the human body function exercise change score index is finally obtained. This method can take into account both positive and negative factors, provide a scientific basis for comprehensive judgment of the overall effect of exercise, and help to formulate more reasonable exercise plans or intervention measures.

[0064] Specifically, the specific formula for calculating the human body function movement score index of the subject to be evaluated is as follows: Among them, JyD is the human function movement score index of the subject to be evaluated, QyD is the human function pre-movement score index of the subject to be evaluated, μ 1 is the pre-exercise scoring coefficient stored in the database, HyD is the post-exercise scoring index of the human function of the subject to be evaluated, μ 2 is the post-exercise scoring coefficient stored in the database, YdB is the human body function movement change scoring index of the object to be evaluated, μ 3 is the motion change scoring coefficient stored in the database, μ 1 +μ 2 +μ 3 =1.

[0065] It needs to be explained that the tanh function is a hyperbolic tangent function, whose domain is all real numbers, whose range is (-1, 1), and the mathematical expression of the tanh function is: Wherein, e is a natural constant and its value is 2.718 in this embodiment.

[0066] In this implementation, a hyperbolic tangent function is introduced into the formula, and its output range is between (-1, 1), which can perform smooth nonlinear mapping on the input value, avoiding the situation where the scoring index has extreme values. This nonlinear characteristic makes the scoring result smoother and more controllable, especially when the motor function index is in a higher or lower range, which can effectively avoid the problem of evaluation distortion and improve the credibility and stability of the result. The formula combines the pre-exercise scoring index, post-exercise scoring index and exercise change scoring index of human body function, and uses the weight coefficient stored in the database (pre-exercise scoring coefficient μ 1 , post-exercise scoring coefficient μ 2 , motion change scoring coefficient μ 3 ) are weighted. This design can balance the contribution of each indicator to the overall score, ensure the comprehensiveness and scientificity of the evaluation results, and provide flexibility to adapt to the weight adjustment needs in different scenarios. The formula comprehensively considers the impact of the pre-exercise state, post-exercise state and exercise changes on human function, and organically combines the three to fully reflect the overall impact of exercise on body function. This design can help more clearly identify the effectiveness of exercise improvement and potential health risks, and provide more guiding results for exercise effect evaluation. The mathematical form of the tanh function is both concise and highly expressive, and it is easy to achieve fast calculation through computer programs. Its standardized input and output characteristics also make the comparison of the scoring index under different individuals and scenarios more intuitive and convenient, and easy to promote and apply. The hyperbolic tangent function can effectively reduce the impact of extreme input values ​​on the scoring results, thereby enhancing the robustness of the model and avoiding excessive fluctuations in the scoring results due to extreme data. This stability is of great significance to the practical application of the human function exercise scoring index, especially in the evaluation scenarios of high-intensity exercise or special populations.

[0067] See also Figure 3The embodiment of the present invention provides a technical solution: a MALS human function evaluation system based on big data, comprising: a data acquisition module, a data analysis module, a comprehensive analysis module, and a judgment analysis module; the data acquisition module is used to acquire the human function data of the object to be evaluated, the human function data including the basic human function data, the initial human function data before the setting of the exercise mode, and the human function movement data after the setting of the exercise mode; the data analysis module is used to perform a comprehensive analysis on the human function data of the object to be evaluated, and obtain the basic human function scoring index, the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be evaluated; the comprehensive analysis module is used to perform a comprehensive analysis on the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function movement change scoring index of the object to be evaluated, and obtain the human function movement scoring index of the object to be evaluated, and perform a comprehensive analysis in combination with the basic human function scoring index of the object to be evaluated, and obtain the comprehensive human function scoring index of the object to be evaluated; the judgment analysis module is used to perform a judgment analysis on the comprehensive human function scoring index of the object to be evaluated and the preset human function evaluation interval, and obtain the human function evaluation level of the object to be evaluated.

[0068] In summary, this application has at least the following effects:

[0069] By introducing comprehensive analysis of pre- and post-exercise and static data, it breaks through the limitations of traditional reliance on static health data and can dynamically evaluate the functional state of the human body, especially in terms of functional changes after exercise. It can effectively reflect the health status of individuals in different activity states, making the evaluation results more comprehensive and scientific. This method can provide more accurate data support for personalized health management, exercise program design, and disease prevention, and improve the practicality and timeliness of the evaluation.

[0070] By standardizing and comprehensively analyzing the static data and motion data before and after exercise, we can not only evaluate the individual's health status before and after exercise, but also accurately calculate the changes in the burden of exercise on the body, and further reveal the individual's adaptability and potential health problems during exercise. This detailed assessment of dynamic changes can help to discover potential health risks in advance and guide individuals to develop reasonable exercise and health intervention plans, thereby effectively improving exercise effectiveness and safety.

[0071] By combining multi-dimensional data of the human body, such as blood flow distribution, muscle oxygen saturation, lactate value, etc., and comprehensively analyzing static and motion data, a more comprehensive reference is provided for human function scoring. By calculating changes in multiple dimensions and conducting comprehensive analysis, the accuracy and reliability of the evaluation results are ensured, avoiding evaluation biases that are easily caused by a single data indicator. This multi-dimensional data fusion can not only provide a comprehensive scientific basis for health assessment, but also help to formulate more accurate health intervention and exercise recommendation plans.

[0072] It adopts four modular designs, namely data acquisition, data analysis, comprehensive analysis and judgment analysis, which can effectively share the tasks of each module and ensure the efficiency and accuracy of the evaluation process. The data acquisition module focuses on accurately acquiring human function data, while the data analysis module performs comprehensive data processing. The comprehensive analysis module integrates various data through multi-dimensional evaluation, and the judgment analysis module derives the final evaluation level based on the comprehensive score. The modular design not only simplifies the entire evaluation process, but also can be flexibly adjusted and optimized according to different needs to meet the health management needs of different user groups. At the same time, the system has a clear structure and is easy to operate, which is convenient for health managers or doctors to operate efficiently, improving the overall evaluation efficiency and real-time response capabilities.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The MALS human function assessment method based on big data is characterized by: The following steps are involved: Acquiring human function data of the subject to be evaluated, wherein the human function data includes basic human function data, initial human function data before setting the exercise mode, and human function exercise data after setting the exercise mode; Comprehensively analyze the human function data of the subject to be evaluated to obtain the basic human function score index, the human function pre-exercise score index, the human function post-exercise score index, and the human function exercise change score index of the subject to be evaluated; Comprehensively analyze the pre-exercise scoring index, post-exercise scoring index, and exercise change scoring index of the human function of the subject to be evaluated to obtain the human function exercise scoring index of the subject to be evaluated, and conduct a comprehensive analysis in combination with the basic scoring index of the human function of the subject to be evaluated to obtain the comprehensive scoring index of the human function of the subject to be evaluated; The comprehensive scoring index of the human function of the subject to be evaluated is judged and analyzed with the preset human function evaluation interval to obtain the human function evaluation level of the subject to be evaluated.

2. The MALS human function assessment method based on big data according to claim 1 is characterized in that: The basic human function data include family genetic risk score, bone density score, and body fat status score; the initial human function data include static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value; the human function exercise data include exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value.

3. The MALS human function assessment method based on big data according to claim 1 is characterized in that: The specific formula for calculating the comprehensive scoring index of human function of the object to be evaluated is as follows: Among them, JnZ is the comprehensive scoring index of human function of the object to be evaluated, e is a natural constant, JcP is the basic scoring index of human function of the object to be evaluated, δ1 is the comprehensive coefficient of human function stored in the database, JyD is the human function movement scoring index of the object to be evaluated, δ2 is the human function movement coefficient stored in the database, δ1+δ2=1, and π is pi.

4. The MALS human function assessment method based on big data according to claim 2 is characterized in that: The specific steps for obtaining the basic scoring index of human function of the object to be evaluated are as follows: Comprehensively analyze the family genetic risk score, bone density score, and body fat status score of the subject to be evaluated to obtain the basic score index of human function of the subject to be evaluated; The specific formula for calculating the basic scoring index of human function of the subject to be evaluated is as follows: Among them, JcP is the basic human function scoring index of the object to be evaluated, YcF is the family genetic risk score of the object to be evaluated, ξ1 is the genetic scoring coefficient stored in the database, GmD is the bone density score of the object to be evaluated, ξ2 is the bone density scoring coefficient stored in the database, TzZ is the body fat status score of the object to be evaluated, ξ3 is the body fat scoring coefficient stored in the database, ξ1+ξ2+ξ3=1, and e is a natural constant.

5. The MALS human function assessment method based on big data according to claim 2 is characterized in that: The specific steps for obtaining the pre-exercise scoring index of the human function of the subject to be evaluated are as follows: The static blood flow distribution index, static muscle oxygen saturation value, static lactate value, static skin temperature value, static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the evaluated object were standardized respectively; Comprehensively analyze the static blood flow distribution index, static muscle oxygen saturation value, static lactic acid value, and static skin temperature value of the subject to be evaluated after standardization to obtain the static health index of human body function of the subject to be evaluated; Comprehensively analyze the static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the subject to be evaluated after the standardized processing to obtain the static burden index of human body function of the subject to be evaluated; Comprehensively analyze the static health index and static burden index of human function of the subject to be evaluated to obtain the pre-exercise scoring index of human function of the subject to be evaluated; The specific formulas for calculating the static health index of human function, the static burden index of human function, and the pre-exercise scoring index of human function of the subject to be evaluated are as follows: Among them, QjK is the static health index of the human body function of the object to be evaluated, QxF is the static blood flow distribution index of the object to be evaluated after standardization, θ1 is the static blood flow distribution coefficient stored in the database, QjB is the static muscle oxygen saturation value of the object to be evaluated after standardization, θ2 is the static muscle oxygen saturation coefficient stored in the database, QrS is the static lactate value of the object to be evaluated after standardization, θ3 is the static lactate coefficient stored in the database, QpW is the static skin temperature value of the object to be evaluated after standardization, θ4 is the static skin temperature coefficient stored in the database, θ1+θ2+θ3+θ4=1, QjF ...rS is the static lactate value of the object to be evaluated after standardization, θ4 is the static skin temperature coefficient stored in the database, θ1+θ2+θ3+θ4=1, QjF is the static muscle oxygen saturation value of the object to be evaluated after standardization, θ2 is the static muscle oxygen saturation coefficient stored in the database, QrS is the static lactate value of the object to be evaluated after standardization, θ3 is the static lactate coefficient stored in the database, QrS is the static muscle oxygen saturation coefficient The static burden index of human function of the evaluated object, QxL is the static heart rate value of the evaluated object after standardization, ψ1 is the static heart rate coefficient stored in the database, QxY is the static blood pressure value of the evaluated object after standardization, ψ2 is the static blood pressure coefficient stored in the database, QxP is the static respiratory rate value of the evaluated object after standardization, ψ3 is the static respiratory rate coefficient stored in the database, QjY is the static muscle hardness value of the evaluated object after standardization, ψ4 is the static muscle hardness coefficient stored in the database, ψ1+ψ2+ψ3+ψ4=1, QyD is the pre-exercise score index of human function of the evaluated object.

6. The MALS human function assessment method based on big data according to claim 2 is characterized in that: The specific steps for obtaining the post-exercise scoring index of the human body function of the subject to be evaluated are as follows: The exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactic acid value, exercise skin temperature value, exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value of the evaluated object are standardized respectively; Comprehensively analyze the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the subject to be evaluated after standardization to obtain the human function exercise health index of the subject to be evaluated; Comprehensively analyze the standardized exercise heart rate value, exercise blood pressure value, exercise breathing frequency value, and exercise muscle hardness value of the subject to be evaluated to obtain the human body function exercise burden index of the subject to be evaluated; Comprehensively analyze the human function movement health index and human function movement burden index of the subject to be evaluated to obtain the human function pre-movement score index of the subject to be evaluated; The specific formulas for calculating the human function exercise health index, human function exercise burden index, and human function pre-exercise score index of the subject to be evaluated are as follows: Among them, HjK is the human function sports health index of the object to be evaluated, HxF is the sports blood flow distribution index of the object to be evaluated after standardization, α1 is the sports blood flow distribution coefficient stored in the database, HjB is the sports muscle oxygen saturation value of the object to be evaluated after standardization, α2 is the sports muscle oxygen saturation coefficient stored in the database, HrS is the sports lactate value of the object to be evaluated after standardization, α3 is the sports lactate coefficient stored in the database, HpW is the sports skin temperature value of the object to be evaluated after standardization, α4 ​​is the sports skin temperature coefficient stored in the database, α1+α2+α3+α4=1, HjF is the sports skin temperature value of the object to be evaluated after standardization, α4 ​​is the sports skin temperature coefficient stored in the database, α1+α2+α3+α4=1, HjB is the sports muscle oxygen saturation value of the object to be evaluated after standardization, α2 is the sports muscle oxygen saturation coefficient stored in the database, HrS is the sports lactate value of the object to be evaluated after standardization, α3 is the sports lactate coefficient stored in the database, HpW is the sports skin temperature value of the object to be evaluated after standardization, α4 ​​is the sports skin temperature coefficient stored in the database, α1+α2+α3+α4=1, HjB is the sports muscle oxygen saturation value of the object to be evaluated after standardization, α4 ​​is the sports skin temperature coefficient stored in the database, α1+α2+α3+α4=1, HjB is the sports muscle oxygen saturation coefficient stored in the database, HrS is the sports lactate ... The human function exercise burden index of the evaluated object, HxL is the exercise heart rate value of the evaluated object after standardization, β1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the evaluated object after standardization, β2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise breathing frequency value of the evaluated object after standardization, β3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the evaluated object after standardization, β4 is the exercise muscle hardness coefficient stored in the database, β1+β2+β3+β4=1, HyD is the human function pre-exercise score index of the evaluated object.

7. The MALS human function assessment method based on big data according to claim 2 is characterized in that: The specific steps for obtaining the human body function movement change score index of the subject to be evaluated are as follows: The static blood flow distribution index, static muscle oxygen saturation value, static lactate value, and static skin temperature value of the subject to be evaluated after the standardized processing are respectively combined with the exercise blood flow distribution index, exercise muscle oxygen saturation value, exercise lactate value, and exercise skin temperature value of the subject to be evaluated after the standardized processing to obtain the human body function exercise health change index of the subject to be evaluated; The static heart rate value, static blood pressure value, static respiratory rate value, and static muscle hardness value of the subject to be evaluated after the standardized processing are respectively combined with the exercise heart rate value, exercise blood pressure value, exercise respiratory rate value, and exercise muscle hardness value of the subject to be evaluated after the standardized processing to perform a comprehensive analysis to obtain the human body function exercise burden change index of the subject to be evaluated; A comprehensive analysis is performed on the human function movement health change index and the human function movement burden change index of the subject to be evaluated to obtain the human function movement change score index of the subject to be evaluated.

8. The MALS human function assessment method based on big data according to claim 7 is characterized in that: The specific formulas for calculating the human function movement health change index, human function movement burden change index, and human function movement change score index of the subject to be evaluated are as follows: Among them, YjB is the human body function movement health change index of the object to be evaluated, HxF is the movement blood flow distribution index of the object to be evaluated after standardization, and QxF is the static blood flow distribution index of the object to be evaluated after standardization. is the blood flow distribution variation coefficient stored in the database, HjB is the exercise muscle oxygen saturation value of the subject to be evaluated after standardization, QjB is the static muscle oxygen saturation value of the subject to be evaluated after standardization, is the muscle oxygen saturation variation coefficient stored in the database, HrS is the sports lactate value of the subject to be evaluated after standardization, QrS is the static lactate value of the subject to be evaluated after standardization, is the lactate variation coefficient stored in the database, HpW is the exercise skin temperature value of the subject to be evaluated after standardization, QpW is the static skin temperature value of the subject to be evaluated after standardization, is the skin temperature variation coefficient stored in the database, YjF is the human body function movement burden change index of the object to be evaluated, HxL is the exercise heart rate value of the object to be evaluated after standardization, QxL is the static heart rate value of the object to be evaluated after standardization, η1 is the exercise heart rate coefficient stored in the database, HxY is the exercise blood pressure value of the object to be evaluated after standardization, QxY is the static blood pressure value of the object to be evaluated after standardization, η2 is the exercise blood pressure coefficient stored in the database, HxP is the exercise breathing frequency value of the object to be evaluated after standardization, QxP is the static breathing frequency value of the object to be evaluated after standardization, η3 is the exercise breathing frequency coefficient stored in the database, HjY is the exercise muscle hardness value of the object to be evaluated after standardization, QjY is the static muscle hardness value of the object to be evaluated after standardization, η4 is the exercise muscle hardness coefficient stored in the database, η1+η2+η3+η4=1, and YdB is the human body function movement change scoring index of the object to be evaluated.

9. The MALS human function assessment method based on big data according to claim 1, characterized in that: The specific formula for calculating the human function movement score index of the subject to be evaluated is as follows: Among them, JyD is the human function movement scoring index of the object to be evaluated, QyD is the human function pre-exercise scoring index of the object to be evaluated, μ1 is the pre-exercise scoring coefficient stored in the database, HyD is the human function post-exercise scoring index of the object to be evaluated, μ2 is the post-exercise scoring coefficient stored in the database, YdB is the human function movement change scoring index of the object to be evaluated, μ3 is the movement change scoring coefficient stored in the database, μ1+μ2+μ3=1.

10. A MALS human function assessment system based on big data, applying the MALS human function assessment method based on big data according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, data analysis module, comprehensive analysis module, judgment analysis module; The data acquisition module is used to acquire the human body function data of the subject to be evaluated, wherein the human body function data includes basic human body function data, initial human body function data before setting the exercise mode, and human body function exercise data after setting the exercise mode; The data analysis module is used to comprehensively analyze the human function data of the subject to be evaluated, and obtain the basic scoring index of the human function, the scoring index of the human function before exercise, the scoring index of the human function after exercise, and the scoring index of the human function exercise change of the subject to be evaluated; The comprehensive analysis module is used to comprehensively analyze the human function pre-exercise scoring index, the human function post-exercise scoring index, and the human function exercise change scoring index of the subject to be evaluated to obtain the human function exercise scoring index of the subject to be evaluated, and to comprehensively analyze the human function basic scoring index of the subject to be evaluated to obtain the human function comprehensive scoring index of the subject to be evaluated; The judgment and analysis module is used to judge and analyze the comprehensive scoring index of the human function of the object to be evaluated and the preset human function evaluation interval to obtain the human function evaluation level of the object to be evaluated.

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