A health status monitoring method and system based on big data

Abstract

The present invention relates to the field of health monitoring technology, and in particular to a health status monitoring method and system based on big data, including: an information acquisition module for real-time acquisition of the vital sign data and motion data of a user during a monitoring period, and also for acquiring the historical health data of the user, a level classification module for calculating the abnormal exercise intensity index of the user and classifying the user's physical fitness level, a metabolic analysis module for analyzing the aerobic state and load state, and analyzing the metabolic state of the user during the monitoring period, an adjustment and optimization module for adjusting the metabolic state analysis process of the user, an intensity analysis module for analyzing the abnormal state of the user's exercise intensity, and a health status evaluation module for analyzing the health status of the user and evaluating the user's exercise status. The present invention effectively improves the efficiency of monitoring the health status of the user.

Description

Technical Field

[0001] The present invention relates to the field of health monitoring technology, and in particular to a health status monitoring method and system based on big data. Background Art

[0002] With the improvement of people's living standards and the enhancement of health awareness, health monitoring has become an indispensable part of daily life. Traditional health monitoring methods mostly rely on manual records and regular physical examinations, which have problems such as discontinuous data and delayed analysis, making it difficult to detect health risks in a timely manner. In recent years, with the development of the Internet of Things, big data and artificial intelligence technologies, health monitoring methods based on big data have gradually become a research hotspot. They can collect and analyze data such as users' living habits and physiological indicators to evaluate and warn of health conditions in real time.

[0003] Chinese patent publication number CN114496253A discloses a big data physical health monitoring system, including: campus building classification module, student location acquisition module, location color marking module, behavior image drawing module, influence index determination module and physical behavior opinion module. The present invention combines the daily activity dynamic trajectory of college students to obtain the influence index of college students' behavior on physical fitness, and combines the physical fitness test report of college students, so as to realize daily supervision of the physical fitness of college students; the present invention records the activity trajectory of college students in a periodic time, and updates the pixel points on the behavior image according to the corresponding position, so that the activity trajectory of college students can be recorded at any time, and the physical fitness influence index of students can be obtained according to the behavior image, so that the real physical fitness influence data of college students can be obtained in real time according to the real activities of college students, and at the same time, the influence data can be fully understood by the data manager through the distinction between positive and negative; it can be seen that the present invention only monitors the physical fitness of users according to the physical fitness monitoring report and the activity dynamics, and there are problems of inaccurate and low efficiency in monitoring the health status of users. Summary of the invention

[0004] To this end, the present invention provides a health status monitoring method and system based on big data, so as to overcome the problems of inaccurate and low efficiency in user health status monitoring in the prior art.

[0005] To achieve the above objectives, the present invention provides a health status monitoring method and system based on big data, comprising:

[0006] The information acquisition module is used to obtain the user's vital sign data and exercise data in real time during the monitoring period, and is also used to obtain the user's historical health data;

[0007] A level classification module is used to calculate the user's abnormal exercise intensity index based on the user's historical health data, and classify the user's physical fitness level according to the calculation result of the user's abnormal exercise intensity index;

[0008] The metabolic analysis module is used to analyze the aerobic state and load state according to the user's physical sign data during the monitoring period, and to analyze the user's metabolic state during the monitoring period according to the user's physical fitness level classification results, aerobic state and load state analysis results;

[0009] The intensity analysis module is used to analyze the abnormal state of the user's exercise intensity according to the user's physical fitness level classification results and exercise data during the monitoring period;

[0010] The health status assessment module is used to analyze the user's health status according to the user's metabolic status analysis results and exercise intensity abnormal status analysis results during the monitoring period, and to assess the user's exercise status according to the health status analysis results, and output the assessment results to the user.

[0011] Furthermore, the level classification module is provided with a history analysis unit, which analyzes the abnormal exercise intensity index of the user according to the historical health data of the user;

[0012] The history analysis unit compares each historical blood lactate concentration a(i) of the user with the blood lactate concentration threshold A, and analyzes the historical exercise state according to the comparison result, wherein:

[0013] When a(i)<A, the history analysis unit determines that the historical blood lactate concentration of the test is normal, and sets the historical exercise state of the test to aerobic exercise;

[0014] When a(i)≥A, the history analysis unit determines that the historical blood lactate concentration of the test is abnormal, and sets the historical exercise state of the test to anaerobic exercise;

[0015] Wherein, a(i) represents the historical blood lactate concentration of the i-th test, i is the historical test number and i=1,2...N, N is the number of historical tests;

[0016] The history analysis unit counts the exercise intensity of the historical exercise state detected as anaerobic exercise, and calculates the abnormal exercise intensity index B according to the statistical result, setting B=[b(1)+b(2)+...+b(J)] / J;

[0017] Among them, b(1) is the first exercise intensity index when the historical exercise state is anaerobic exercise, b(2) is the second exercise intensity index when the historical exercise state is anaerobic exercise, b(J) is the Jth exercise intensity index when the historical exercise state is anaerobic exercise, and J is the number of historical exercise states that are anaerobic exercise.

[0018] Furthermore, the metabolic analysis module is provided with a breath monitoring unit, and the breath monitoring unit is used to analyze the user's aerobic state according to the breath data;

[0019] The exhalation analysis unit analyzes the user's aerobic state according to the user's maximum oxygen uptake VO2max, minute ventilation VE and carbon dioxide emission VCO2, wherein:

[0020] When VO2max / (VCO2 / KG)<(VO2max / KG+VCO2) / (VE / KG), the breath analysis unit determines that the user's aerobic state is normal;

[0021] When VO2max / (VCO2 / KG)≥(VO2max / KG+VCO2) / (VE / KG), the breath analysis unit determines that the user's aerobic state is abnormal;

[0022] Where KG is the user's weight.

[0023] Furthermore, the metabolic analysis module is also provided with a load monitoring unit, and the load monitoring unit is used to analyze the user's load status according to the user's vital sign data within the monitoring period;

[0024] The load monitoring unit calculates the abnormal physical sign index η according to the physical sign data of the user during the monitoring period, and sets η=exp{3×(r / XL)-3};

[0025] Where r is the user's real-time breathing rate, and XL is the user's real-time heart rate;

[0026] The load monitoring unit compares the abnormal physical sign index η with the preset abnormal physical sign indexes, and analyzes the load state of the user according to the comparison result, wherein:

[0027] When η<N1, the load monitoring unit determines that the user's load state is normal;

[0028] When η≥N1, the load monitoring unit determines that the user's load state is abnormal;

[0029] Among them, N1 is the preset abnormal physical sign index.

[0030] Furthermore, the metabolic analysis module is further provided with a metabolic analysis unit, which is used to analyze the metabolic state of the user during the monitoring period according to the aerobic state analysis result of the user during the monitoring period and the load state analysis result of the user, wherein:

[0031] When the user's physical fitness level is level three, if α<A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0032] When the user's physical fitness level is level 2, if α<A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0033] When the user's physical fitness level is level one, if α<A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0034] Among them, α is the metabolic abnormality index during the monitoring period, and α is set to be c1×E / T+c2×R / T, where c1 is the aerobic abnormality weight, c2 is the load abnormality weight, E is the duration of the aerobic state being abnormal during the monitoring period, R is the duration of the load state being abnormal during the monitoring period, T is the duration of the monitoring period, A1 is the first preset metabolic abnormality index, A2 is the second preset metabolic abnormality index, A3 is the third preset vital sign metabolic index, and A1<A2<A3.

[0035] Furthermore, it also includes an adjustment and optimization module, which is used to adjust the user's metabolic state analysis process according to the average temperature in the monitoring period, analyze the environmental heat dissipation abnormality according to the environmental parameters in the monitoring period, and optimize the user's metabolic state adjustment process according to the environmental heat dissipation abnormality analysis result;

[0036] The adjustment and optimization module is provided with an environment adjustment unit, and the environment adjustment unit is used to adjust the analysis process of the user's metabolic state according to the average temperature in the monitoring period;

[0037] The environment adjustment unit compares the average temperature t of the monitoring period with each preset temperature, and adjusts the analysis process of the user's metabolic state according to the comparison result, wherein:

[0038] When t<T1, the environment adjustment unit determines that the environment temperature is low during the monitoring period, and adjusts each preset abnormal vital sign index to Az', setting Az'=Az×cos[(T1-t) / T1], and setting z=1,2,3;

[0039] When T1≤t<T2, the environment adjustment unit determines that the environment temperature is normal within the monitoring period and does not make any adjustment;

[0040] When t≥T2, the environment adjustment unit determines that the environment temperature is high during the monitoring period, and adjusts each preset abnormal vital sign index to Az", and sets Az"=Az×1+sin[(T2-t) / T2];

[0041] Wherein, T1 is the left value of the preset temperature range of the movement, T2 is the right value of the preset temperature range of the movement, and T1<T2.

[0042] Furthermore, the adjustment and optimization module is further provided with a heat dissipation optimization unit, which analyzes the abnormality of environmental heat dissipation according to environmental parameters within the monitoring period, and optimizes the adjustment process of the user's metabolic state according to the analysis results;

[0043] The heat dissipation optimization module compares the environmental heat dissipation coefficient β with the environmental heat dissipation threshold D, and analyzes the environmental heat dissipation abnormality according to the comparison result. The heat dissipation optimization unit also optimizes the adjustment process of the user's metabolic state according to the environmental heat dissipation abnormality analysis result, wherein:

[0044] When β≥D, the heat dissipation optimization unit determines that the abnormality of environmental heat dissipation during the monitoring period is normal and does not perform optimization;

[0045] When β<D, the heat dissipation optimization unit determines that the environmental heat dissipation abnormality within the monitoring period is abnormal, and adjusts the right value of the motion preset temperature range to T2', setting T2'=T2×{1-arctan[(D-β) / D]}.

[0046] Furthermore, the health status assessment module is provided with a health status analysis unit, which analyzes the health status of the user during the monitoring period according to the metabolic status analysis results and the abnormal exercise intensity status analysis results of the user during the monitoring period, wherein:

[0047] When the user's metabolic state is normal and the abnormal exercise intensity state is normal, the health state analysis unit determines that the user's health state is normal and does not issue an alarm to the user;

[0048] When the user's metabolic state is normal and the abnormal exercise intensity state is abnormal, the health state analysis unit determines that the user's health state is a load exercise state and does not issue an alarm to the user;

[0049] When the user's metabolic state is abnormal and the abnormal state of exercise intensity is normal, the health state analysis unit determines that the user's health state is a fatigue exercise state and issues a fatigue exercise alarm to the user;

[0050] When the user's metabolic state is abnormal and the exercise intensity abnormal state is abnormal, the health state analysis unit determines that the user's health state is an abnormal physical state and issues a high-intensity alarm to the user.

[0051] Furthermore, the health status assessment module is further provided with a motion assessment unit, which assesses the user's motion status according to the analysis result of the health status within the monitoring period, wherein:

[0052] When the user's health status is normal, the exercise evaluation unit determines that the user's exercise status is low-load exercise, and adjusts the user's exercise intensity index to K', setting K'=K×γ;

[0053] When the user's health status is a load exercise status, the exercise evaluation unit determines that the user's exercise status is a normal high-load exercise, and does not adjust the user's exercise intensity index;

[0054] When the user's health status is a fatigued exercise state, the exercise evaluation unit determines that the user's exercise state is a long-term exercise and does not adjust the user's exercise intensity index;

[0055] When the user's health status is an abnormal physical state, the exercise evaluation unit determines that the user's exercise status is an abnormally high-load exercise, and adjusts the user's exercise intensity index to K", setting K" = K × cos [(α-Az) / Az] × γ;

[0056] The exercise evaluation unit is further configured to output the adjusted exercise intensity index to the user.

[0057] Furthermore, the health status monitoring system based on big data is characterized by comprising:

[0058] Step S1, obtaining the user's physical sign data and exercise data in real time during the monitoring period, and obtaining the user's historical health data;

[0059] Step S2, calculating the abnormal exercise intensity index of the user according to the historical health data of the user, and classifying the physical fitness level of the user according to the calculation result of the abnormal exercise intensity index of the user;

[0060] Step S3, analyzing the aerobic state and load state according to the user's physical sign data during the monitoring period, and analyzing the user's metabolic state during the monitoring period according to the user's physical fitness level classification result, the aerobic state and load state analysis results;

[0061] Step S4, adjusting the user's metabolic state analysis process according to the average temperature during the monitoring period, analyzing the abnormality of environmental heat dissipation according to the environmental parameters during the monitoring period, and optimizing the user's metabolic state adjustment process according to the results of the abnormality analysis of environmental heat dissipation;

[0062] Step S5, analyzing the abnormal state of the user's exercise intensity index according to the user's physical fitness level classification result and exercise data during the monitoring period;

[0063] Step S6, analyzing the user's health status according to the analysis results of the user's metabolic status and the abnormal exercise intensity status during the monitoring period, and evaluating the user's exercise status according to the analysis results of the health status.

[0064] Compared with the prior art, the beneficial effects of the present invention are that the required information is acquired by the information acquisition module, thereby improving the completeness and accuracy of information acquisition; the user's physical fitness level is divided by the level division module, thereby improving the accuracy of the user's physical fitness level analysis; the user's metabolic state is analyzed by the metabolic analysis module, thereby improving the accuracy of the user's metabolic state analysis; the user's metabolic state is adjusted and optimized by the adjustment and optimization module, thereby improving the accuracy of the user's metabolic state analysis; the abnormal state of the user's exercise intensity index during the monitoring period is analyzed by the intensity analysis module, thereby improving the accuracy of the abnormal state of the user's exercise intensity analysis; the user's health status is analyzed by the health status evaluation module, and the user's exercise status is evaluated according to the analysis result, thereby improving the efficiency of user health status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a schematic diagram of the structure of the health status monitoring system based on big data in this embodiment.

[0066] Figure 2 It is a structural diagram of the level division module of this embodiment.

[0067] Figure 3 Schematic diagram of the structure of the metabolic analysis module of this embodiment.

[0068] Figure 4 This is a schematic diagram of the structure of the adjustment and optimization module in this embodiment.

[0069] Figure 5 Schematic diagram of the structure of the health status assessment module of this embodiment.

[0070] Figure 6 Schematic diagram of the structure of the health status monitoring method based on big data in this embodiment. DETAILED DESCRIPTION

[0071] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0072] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0073] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] See also Figure 1 As shown, it is a structural diagram of the health status monitoring method and system based on big data in this embodiment, including:

[0075] The information acquisition module is used to obtain the user's vital sign data and exercise data during the monitoring period, and is also used to obtain the user's historical health data; the vital sign data includes: exhalation data, real-time respiratory rate and the user's real-time heart rate; the exhalation data includes maximum oxygen uptake, minute ventilation and carbon dioxide emissions; the exercise data includes: environmental parameters and the user's exercise intensity index during the monitoring period; the environmental parameters include average temperature, ambient wind speed, ambient absolute humidity and altitude; the historical health data is a data set of historical detections, and each data set detected includes historical blood lactate concentration, historical exercise intensity and historical heart rate. This embodiment does not specifically limit the number of historical detections in the historical health data, and those skilled in the art can set it freely, as long as the number of historical detections in the historical health data is met. For example, the data set of the 20 most recent historical detections can be selected as the historical health data; the exercise intensity index described in this embodiment is The intensity index of the sports item performed by the user. In this embodiment, running is taken as an example. The sports intensity index is the running pace, that is, the time taken for running 1 kilometer. This embodiment does not make a specific limitation on the sports intensity index, and those skilled in the art can set it freely. For example, in cycling, the exercise intensity index can be the time taken for every 5 kilometers, and in rope skipping, the exercise intensity index can be the number of uninterrupted rope skipping. This embodiment does not make a specific limitation on the value of the monitoring period, and those skilled in the art can set it freely, and only need to meet the value requirements of the monitoring period, such as setting the value of the monitoring period to 10 minutes. This implementation does not make a specific limitation on the method of obtaining the user's vital sign data and motion data, and those skilled in the art can set it freely, and only need to meet the user's vital sign data and motion data acquisition requirements. This embodiment obtains the user's vital sign data and motion data through a wearable electronic device, and the user's historical health data is obtained by user interactive input;

[0076] A level classification module, used to classify the user's physical fitness level according to the user's historical health data, and the level classification module is connected to the information acquisition module;

[0077] A metabolic analysis module, used to analyze the user's metabolic state according to the user's physical fitness grade classification result and the user's physical sign data during the monitoring period, and the metabolic analysis module is connected to the grade classification module;

[0078] An adjustment and optimization module, used to adjust the user's metabolic state analysis process according to the environmental parameters within the monitoring period, and the adjustment and optimization module is connected to the metabolic analysis module;

[0079] An intensity analysis module, used to analyze the abnormal state of the user's exercise intensity according to the user's physical fitness level classification result and exercise data during the monitoring period, and the intensity analysis module is connected to the registration classification module;

[0080] The health status assessment module is used to analyze the user's health status according to the user's metabolic status analysis results and the abnormal exercise intensity status analysis results during the monitoring period, and to evaluate the user's exercise plan for the next monitoring period according to the health status analysis results. The health status assessment module is connected to the metabolic analysis module and the intensity analysis module.

[0081] See also Figure 2 As shown, it is a schematic diagram of the structure of the level division module of this embodiment, including:

[0082] A history analysis unit, used to analyze the user's abnormal exercise intensity index based on the user's historical health data;

[0083] The level division unit is used to divide the user's physical fitness level according to the analysis result of the abnormal exercise intensity index, and the level division unit is connected to the history analysis unit.

[0084] See also Figure 3 As shown, it is a schematic diagram of the structure of the metabolic analysis module of this embodiment, including:

[0085] An exhalation monitoring unit, used to analyze the user's aerobic status based on exhalation data;

[0086] A load monitoring unit is used to analyze the user's load status according to the user's vital sign data during the monitoring period;

[0087] The metabolic analysis unit is used to analyze the metabolic state of the user during the monitoring period according to the analysis results of the user's aerobic state and the user's load state during the monitoring period. The metabolic analysis unit is connected to the exhalation monitoring unit and the load monitoring unit.

[0088] See also Figure 4 As shown, it is a schematic diagram of the structure of the adjustment and optimization module of this embodiment, including:

[0089] An environmental adjustment unit, for adjusting the analysis process of the user's metabolic state according to environmental parameters during the monitoring period;

[0090] The heat dissipation optimization unit is used to analyze the abnormality of environmental heat dissipation according to the environmental parameters within the monitoring period, and optimize the adjustment process of the user's metabolic state according to the analysis results. The heat dissipation optimization unit is connected to the environmental adjustment unit.

[0091] See also Figure 5 As shown, it is a schematic diagram of the structure of the adjustment and optimization module of this embodiment, including:

[0092] A health status analysis unit, used to analyze the health status of the user during the monitoring period according to the metabolic status analysis results and abnormal exercise intensity status analysis results of the user during the monitoring period;

[0093] The motion evaluation unit is used to evaluate the user's motion state according to the analysis results of the health state during the monitoring period, and output the evaluation results to the user.

[0094] Specifically, the health status monitoring method and system based on big data described in this embodiment are applied to the health status monitoring of sports people in high altitude areas during exercise; the health status monitoring method and system based on big data described in this embodiment act on the health data processing platform in the server;

[0095] Specifically, the level classification module is provided with a history analysis unit, which analyzes the abnormal exercise intensity index of the user according to the historical health data of the user;

[0096] The history analysis unit compares each historical blood lactate concentration a(i) of the user with the blood lactate concentration threshold A, and analyzes the historical exercise state according to the comparison result, wherein:

[0097] When a(i)<A, the history analysis unit determines that the historical blood lactate concentration of the test is normal, and sets the historical exercise state of the test to aerobic exercise;

[0098] When a(i)≥A, the history analysis unit determines that the historical blood lactate concentration of the test is abnormal, and sets the historical exercise state of the test to anaerobic exercise;

[0099] Wherein, a(i) represents the historical blood lactate concentration of the i-th test, i is the historical test number and i=1,2...N, N is the number of historical tests;

[0100] The history analysis unit counts the exercise intensity of the historical exercise state detected as anaerobic exercise, and calculates the abnormal exercise intensity index B according to the statistical result, setting B=[b(1)+b(2)+...+b(J)] / J;

[0101] Among them, b(1) is the first exercise intensity index when the historical exercise state is anaerobic exercise, b(2) is the second exercise intensity index when the historical exercise state is anaerobic exercise, b(J) is the Jth exercise intensity index when the historical exercise state is anaerobic exercise, and J is the number of historical exercise states that are anaerobic exercise.

[0102] Specifically, the historical analysis unit improves the accuracy of the user's historical exercise intensity analysis by analyzing the user's abnormal exercise intensity index according to the user's historical health data, thereby improving the accuracy of the user's physical fitness level classification, thereby improving the accuracy of the user's health status analysis, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make a specific limitation on the value of the blood lactate concentration threshold A, and technical personnel in this field can set it freely, as long as the value requirements of the blood lactate concentration threshold A are met, such as the blood lactate concentration threshold A can be set to 4mmo l / L.

[0103] Specifically, the level classification module is further provided with a level classification unit, and the level classification unit classifies the user's physical fitness level according to the calculation result of the abnormal exercise intensity index B, wherein:

[0104] When B<B1, the level classification unit determines that the user's physical fitness level is level three;

[0105] When B1≤B<B2, the level classification unit determines that the user's physical fitness level is level 2;

[0106] When B≥B2, the level classification unit determines that the user's physical fitness level is level one;

[0107] Wherein, B1 is the first preset exercise intensity, B2 is the second preset exercise intensity, and B1<B2.

[0108] Specifically, the level classification unit improves the accuracy of the user's physical fitness level classification by classifying the user's physical fitness level according to the calculation result of the abnormal exercise intensity index, thereby improving the accuracy of the user's health status analysis, thereby improving the accuracy of the user's exercise status assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make specific limitations on the values ​​of the first preset exercise intensity B1 and the second preset exercise intensity B2. Technical personnel in this field can set them freely, and only need to meet the value requirements of the first preset exercise intensity B1 and the second preset exercise intensity B2. For example, when the user's sports event is long-distance running, the first preset exercise intensity B1 can be set to 4min, and the second preset exercise intensity B2 can be set to 3min.

[0109] It is worth noting that in this embodiment, the physical fitness level of level three indicates that the user's physical fitness level is weak, the physical fitness level of level two indicates that the user's physical fitness level is normal, and the physical fitness level of level one indicates that the user's physical fitness level is good.

[0110] Specifically, the metabolic analysis module is provided with a breath monitoring unit, which analyzes the user's aerobic status based on the breath data;

[0111] The exhalation analysis unit analyzes the user's aerobic state according to the user's maximum oxygen uptake VO2max, minute ventilation VE and carbon dioxide emission VCO2, wherein:

[0112] When VO2max / (VCO2 / KG)<(VO2max / KG+VCO2) / (VE / KG), the breath analysis unit determines that the user's aerobic state is normal;

[0113] When VO2max / (VCO2 / KG)≥(VO2max / KG+VCO2) / (VE / KG), the breath analysis unit determines that the user's aerobic state is abnormal;

[0114] Where KG is the user's weight.

[0115] Specifically, the exhalation monitoring unit improves the accuracy of the user's aerobic state analysis by analyzing the user's aerobic state based on the exhalation data, thereby improving the accuracy of the user's metabolic state analysis, thereby improving the accuracy of the user's exercise state assessment, and ultimately improving the efficiency of monitoring the user's health status.

[0116] The metabolic analysis module is also provided with a load monitoring unit, which analyzes the user's load status according to the user's vital sign data within the monitoring period;

[0117] The load monitoring unit calculates the abnormal physical sign index η according to the physical sign data of the user during the monitoring period, and sets η=exp{3×(r / XL)-3};

[0118] Where r is the user's real-time breathing rate, and XL is the user's real-time heart rate;

[0119] The load monitoring unit compares the abnormal physical sign index η with the preset abnormal physical sign indexes, and analyzes the load state of the user according to the comparison result, wherein:

[0120] When η<N1, the load monitoring unit determines that the user's load state is normal;

[0121] When η≥N1, the load monitoring unit determines that the user's load state is abnormal;

[0122] Among them, N1 is the preset abnormal physical sign index.

[0123] Specifically, the load monitoring unit analyzes the user's load status according to the user's vital sign data during the monitoring period, thereby improving the accuracy of the user's load status analysis, and further improving the accuracy of the user's metabolic status analysis, thereby improving the accuracy of the user's exercise status assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make specific limitations on the value of the preset vital sign abnormality index N1, and technical personnel in this field can set it freely, as long as the value requirements of the preset vital sign abnormality index N1 are met, such as the preset vital sign abnormality index N1 can be set to 0.4.

[0124] Specifically, the metabolic analysis unit analyzes the metabolic state of the user during the monitoring period according to the aerobic state analysis result of the user during the monitoring period and the load state analysis result of the user, wherein:

[0125] When the user's physical fitness level is level three, if α<A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0126] When the user's physical fitness level is level 2, if α<A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0127] When the user's physical fitness level is level one, if α<A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal;

[0128] Among them, α is the metabolic abnormality index during the monitoring period, and α is set to be c1×E / T+c2×R / T, where c1 is the aerobic abnormality weight, c2 is the load abnormality weight, E is the duration of the aerobic state being abnormal during the monitoring period, R is the duration of the load state being abnormal during the monitoring period, T is the duration of the monitoring period, A1 is the first preset metabolic abnormality index, A2 is the second preset metabolic abnormality index, A3 is the third preset vital sign metabolic index, and A1<A2<A3.

[0129] Specifically, the metabolic analysis unit analyzes the metabolic state of the user during the monitoring period according to the aerobic state analysis results of the user and the load state analysis results of the user during the monitoring period, thereby improving the accuracy of the metabolic state analysis of the user, thereby improving the accuracy of the user's exercise state assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not specifically limit the values ​​of the first preset metabolic abnormality index A1, the second preset metabolic abnormality index A2, and the third preset vital sign metabolic index A3. Those skilled in the art can set them freely, and only need to meet the value requirements of the first preset metabolic abnormality index A1, the second preset metabolic abnormality index A2, and the third preset vital sign metabolic index A3. For example, the first preset metabolic abnormality index A1 can be set to 0.1, the second preset metabolic abnormality index A2 can be set to 0.15, and the third preset vital sign metabolic index A3 can be set to 0.3.

[0130] Specifically, the adjustment and optimization module is provided with an environment adjustment unit, which adjusts the analysis process of the user's metabolic state according to the average temperature in the monitoring period;

[0131] The environment adjustment unit compares the average temperature t of the monitoring period with each preset temperature, and adjusts the analysis process of the user's metabolic state according to the comparison result, wherein:

[0132] When t<T1, the environment adjustment unit determines that the environment temperature is low during the monitoring period, and adjusts each preset abnormal vital sign index to Az', setting Az'=Az×cos[(T1-t) / T1], and setting z=1,2,3;

[0133] When T1≤t<T2, the environment adjustment unit determines that the environment temperature is normal within the monitoring period and does not make any adjustment;

[0134] When t≥T2, the environment adjustment unit determines that the environment temperature is high during the monitoring period, and adjusts each preset abnormal vital sign index to Az", and sets Az"=Az×1+sin[(T2-t) / T2];

[0135] Wherein, T1 is the left value of the preset temperature range of the movement, T2 is the right value of the preset temperature range of the movement, and T1<T2.

[0136] Specifically, the environment adjustment unit improves the accuracy of the user's metabolic state analysis by adjusting the analysis process of the user's metabolic state according to the environmental parameters within the monitoring period, thereby improving the accuracy of the user's exercise state assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make specific limitations on the values ​​of the left value T1 of the exercise preset temperature range and the right value T2 of the exercise preset temperature range. Those skilled in the art can set them freely, and only need to meet the value requirements of the left value T1 of the exercise preset temperature range and the right value T2 of the exercise preset temperature range. For example, the left value T1 of the exercise preset temperature range can be set to 18°C, and the right value T2 of the exercise preset temperature range can be set to 25°C.

[0137] Specifically, the adjustment and optimization module is further provided with a heat dissipation optimization unit, which analyzes the abnormality of environmental heat dissipation according to environmental parameters within the monitoring period, and optimizes the adjustment process of the user's metabolic state according to the analysis results;

[0138] The heat dissipation optimization unit calculates the environmental heat dissipation coefficient β according to the environmental parameters in the monitoring period, and sets β=ln{1+(fF) / F×s+(hH) / H};

[0139] Where f is the ambient wind speed during the monitoring period, F is the preset ambient wind speed, s is the ambient absolute humidity during the monitoring period, h is the altitude of the user's sports area, and H is the preset altitude;

[0140] The heat dissipation optimization module compares the environmental heat dissipation coefficient β with the environmental heat dissipation threshold D, and analyzes the environmental heat dissipation abnormality according to the comparison result. The heat dissipation optimization unit also optimizes the adjustment process of the user's metabolic state according to the environmental heat dissipation abnormality analysis result, wherein:

[0141] When β≥D, the heat dissipation optimization unit determines that the abnormality of environmental heat dissipation during the monitoring period is normal and does not perform optimization;

[0142] When β<D, the heat dissipation optimization unit determines that the environmental heat dissipation abnormality within the monitoring period is abnormal, and adjusts the right value of the motion preset temperature range to T2', setting T2'=T2×{1-arctan[(D-β) / D]}.

[0143] Specifically, the heat dissipation optimization unit analyzes the environmental heat dissipation abnormality according to the environmental parameters within the monitoring period, and optimizes the adjustment process of the user's metabolic state according to the analysis results, thereby improving the accuracy of the heat dissipation abnormality analysis within the monitoring period, and then improving the accuracy of the user's metabolic state analysis, thereby improving the accuracy of the user's exercise state assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make specific limitations on the value of the preset environmental heat dissipation coefficient D, and technical personnel in this field can set it freely, and only need to meet the value requirements of the preset environmental heat dissipation coefficient D, such as the preset environmental heat dissipation coefficient D can be set to 0.2; at the same time, the preset environmental wind speed F in this embodiment is 5m / s, and the preset altitude H is not specifically limited in this embodiment, and technical personnel in this field can set it freely, such as the preset altitude H can be set to 1800m.

[0144] Specifically, the intensity analysis module analyzes the abnormal state of the user's exercise intensity according to the user's physical fitness level classification result and the exercise data in the monitoring period, wherein:

[0145] When the user's physical fitness level is level three, if γ<Y1, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is normal; when γ≥Y1, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is abnormal;

[0146] When the user's physical fitness level is level 2, if γ<Y2, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is normal; when γ≥Y2, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is abnormal;

[0147] When the user's physical fitness level is level one, if γ<Y3, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is normal; when γ≥Y3, the intensity analysis module determines that the abnormal state of the user's exercise intensity during the monitoring period is abnormal;

[0148] Among them, γ is the exercise intensity index, and γ=K / B is set. K is the user's exercise intensity index during the monitoring period, Y1 is the first preset exercise intensity index, Y2 is the second preset exercise intensity index, and Y3 is the third preset exercise intensity index.

[0149] Specifically, the intensity analysis unit analyzes the abnormal state of the user's exercise intensity according to the user's physical fitness level classification result and the exercise data in the monitoring period, thereby improving the accuracy of the analysis of the abnormal state of the user's exercise intensity in the monitoring period, thereby improving the accuracy of the user's exercise state assessment, and ultimately improving the efficiency of monitoring the user's health status; it can be understood that this embodiment does not make specific limitations on the values ​​of the first preset exercise intensity index Y1, the second preset exercise intensity index Y2 and the third preset exercise intensity index Y3. Technical personnel in this field can set them freely, and only need to meet the value requirements of the first preset exercise intensity index Y1, the second preset exercise intensity index Y2 and the third preset exercise intensity index Y3. For example, the first preset exercise intensity index Y1 can be set to 1.05, the second preset exercise intensity index Y2 can be set to 1.1, and the third preset exercise intensity index Y3 can be set to 1.15.

[0150] Specifically, the health status assessment module is provided with a health status analysis unit, which analyzes the health status of the user during the monitoring period according to the metabolic status analysis results and the abnormal exercise intensity status analysis results of the user during the monitoring period, wherein:

[0151] When the user's metabolic state is normal and the abnormal exercise intensity state is normal, the health state analysis unit determines that the user's health state is normal and does not issue an alarm to the user;

[0152] When the user's metabolic state is normal and the abnormal exercise intensity state is abnormal, the health state analysis unit determines that the user's health state is a load exercise state and does not issue an alarm to the user;

[0153] When the user's metabolic state is abnormal and the abnormal state of exercise intensity is normal, the health state analysis unit determines that the user's health state is a fatigue exercise state and issues a fatigue exercise alarm to the user;

[0154] When the user's metabolic state is abnormal and the exercise intensity abnormal state is abnormal, the health state analysis unit determines that the user's health state is an abnormal physical state and issues a high-intensity alarm to the user.

[0155] Specifically, the health status analysis unit analyzes the health status of the user during the monitoring period based on the metabolic status analysis results and the abnormal exercise intensity status analysis results of the user during the monitoring period, thereby improving the accuracy of the health status analysis of the user during the monitoring period, and then improving the accuracy of the user's exercise status assessment, and ultimately improving the efficiency of monitoring the user's health status.

[0156] Specifically, the health status assessment module is further provided with a motion assessment unit, which assesses the user's motion status according to the analysis results of the health status within the monitoring period, wherein:

[0157] When the user's health status is normal, the exercise evaluation unit determines that the user's exercise status is low-load exercise, and adjusts the user's exercise intensity index to K', setting K'=K×γ;

[0158] When the user's health status is a load exercise status, the exercise evaluation unit determines that the user's exercise status is a normal high-load exercise, and does not adjust the user's exercise intensity index;

[0159] When the user's health status is a fatigued exercise state, the exercise evaluation unit determines that the user's exercise state is a long-term exercise and does not adjust the user's exercise intensity index;

[0160] When the user's health status is an abnormal physical state, the exercise evaluation unit determines that the user's exercise status is an abnormally high-load exercise, and adjusts the user's exercise intensity index to K", setting K" = K × cos [(α-Az) / Az] × γ;

[0161] The exercise evaluation unit is further configured to output the adjusted exercise intensity index to the user.

[0162] Specifically, the motion evaluation unit improves the accuracy of the user's motion status analysis by evaluating the user's motion status according to the analysis results of the health status during the monitoring period, thereby improving the accuracy of the adjustment of the motion intensity index, and ultimately improving the efficiency of monitoring the user's health status.

[0163] See also Figure 6 As shown, it is a structural diagram of the health status monitoring method based on big data in this embodiment, including:

[0164] Step S1, obtaining the user's physical sign data and exercise data in real time during the monitoring period, and obtaining the user's historical health data;

[0165] Step S2, calculating the abnormal exercise intensity index of the user according to the historical health data of the user, and classifying the physical fitness level of the user according to the calculation result of the abnormal exercise intensity index of the user;

[0166] Step S3, analyzing the aerobic state and load state according to the user's physical sign data during the monitoring period, and analyzing the user's metabolic state during the monitoring period according to the user's physical fitness level classification result, the aerobic state and load state analysis results;

[0167] Step S4, adjusting the user's metabolic state analysis process according to the average temperature during the monitoring period, analyzing the abnormality of environmental heat dissipation according to the environmental parameters during the monitoring period, and optimizing the user's metabolic state adjustment process according to the results of the abnormality analysis of environmental heat dissipation;

[0168] Step S5, analyzing the abnormal state of the user's exercise intensity according to the user's physical fitness level classification result and exercise data during the monitoring period;

[0169] Step S6, analyzing the user's health status according to the analysis results of the user's metabolic status and the abnormal exercise intensity status during the monitoring period, and evaluating the user's exercise status according to the analysis results of the health status.

[0170] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A health status monitoring system based on big data, characterized in that: include, The information acquisition module is used to obtain the user's vital sign data and exercise data in real time during the monitoring period, and is also used to obtain the user's historical health data; A level classification module is used to calculate the average abnormal exercise intensity of the user according to the historical health data of the user, and classify the user's physical fitness level according to the calculation result of the average abnormal exercise intensity of the user; The metabolic analysis module is used to analyze the aerobic state and load state according to the user's physical sign data during the monitoring period, and to analyze the user's metabolic state during the monitoring period according to the user's physical fitness level classification results, aerobic state and load state analysis results; The intensity analysis module is used to analyze the abnormal state of the user's exercise intensity according to the user's physical fitness level classification results and exercise data during the monitoring period; A health status assessment module is used to analyze the user's health status according to the user's metabolic status analysis results and exercise intensity abnormality status analysis results during the monitoring period, and to assess the user's exercise status according to the health status analysis results, and output the assessment results to the user; The level classification module is provided with a history analysis unit, which analyzes the average abnormal exercise intensity of the user according to the historical health data of the user; The history analysis unit compares each historical blood lactate concentration a(i) of the user with the blood lactate concentration threshold A, and analyzes the historical exercise state according to the comparison result, wherein: When a(i)<A, the history analysis unit determines that the historical blood lactate concentration of the test is normal, and sets the historical exercise state of the test to aerobic exercise; When a(i)≥A, the history analysis unit determines that the historical blood lactate concentration of the test is abnormal, and sets the historical exercise state of the test to anaerobic exercise; Where a(i) represents the historical blood lactate concentration of the i-th test, i is the historical test number and i=1,2...N, N is the number of historical tests; The history analysis unit counts the exercise intensity of the historical exercise state detected as anaerobic exercise, and calculates the average abnormal exercise intensity B according to the statistical result, and sets B=[b(1)+b(2)+...+b(J)] / J; Wherein, b(1) is the first exercise intensity of anaerobic exercise in the historical exercise state, b(2) is the second exercise intensity of anaerobic exercise in the historical exercise state, b(J) is the Jth exercise intensity of anaerobic exercise in the historical exercise state, and J is the number of anaerobic exercise in the historical exercise state; The metabolic analysis module is provided with a breath monitoring unit, and the breath monitoring unit is used to analyze the user's aerobic state according to the breath data; The exhalation monitoring unit analyzes the user's aerobic state according to the user's maximum oxygen uptake VO2max, minute ventilation VE and carbon dioxide emission VCO2, wherein: When VO2max / (VCO2 / KG)<(VO2max / KG+VCO2) / (VE / KG), the exhalation monitoring unit determines that the user's aerobic state is normal; When VO2max / (VCO2 / KG)≥(VO2max / KG+VCO2) / (VE / KG), the exhalation monitoring unit determines that the user's aerobic state is abnormal; Where KG is the user's weight; The metabolic analysis module is also provided with a load monitoring unit, which is used to analyze the user's load status according to the user's vital sign data within the monitoring period; The load monitoring unit calculates the abnormal physical sign index η according to the physical sign data of the user during the monitoring period, and sets η=exp{3×(r / XL)-3}; Where r is the user's real-time breathing rate, and XL is the user's real-time heart rate; The load monitoring unit compares the abnormal physical sign index η with the preset abnormal physical sign indexes, and analyzes the load state of the user according to the comparison result, wherein: When η<N1, the load monitoring unit determines that the user's load state is normal; When η≥N1, the load monitoring unit determines that the user's load state is abnormal; Among them, N1 is the preset abnormal physical sign index; The metabolic analysis module is further provided with a metabolic analysis unit, which is used to analyze the metabolic state of the user during the monitoring period according to the aerobic state analysis result of the user during the monitoring period and the load state analysis result of the user, wherein: When the user's physical fitness level is level three, if α<A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A1, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal; When the user's physical fitness level is level 2, if α<A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A2, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal; When the user's physical fitness level is level one, if α<A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is normal; if α≥A3, the metabolic analysis unit determines that the user's metabolic state during the monitoring period is abnormal; Among them, α is the metabolic abnormality index during the monitoring period, and α=c1×E / T+c2×R / T is set, c1 is the aerobic abnormality weight, c2 is the load abnormality weight, E is the duration of the aerobic state being abnormal during the monitoring period, R is the duration of the load state being abnormal during the monitoring period, T is the duration of the monitoring period, A1 is the first preset metabolic abnormality index, A2 is the second preset metabolic abnormality index, A3 is the third preset vital sign metabolic index, and A1<A2<A3.

2. The health status monitoring system based on big data according to claim 1 is characterized in that: It also includes an adjustment and optimization module, which is used to adjust the user's metabolic state analysis process according to the average temperature during the monitoring period, analyze the abnormality of environmental heat dissipation according to the environmental parameters during the monitoring period, and optimize the user's metabolic state adjustment process according to the abnormality analysis result of environmental heat dissipation; The adjustment and optimization module is provided with an environment adjustment unit, and the environment adjustment unit is used to adjust the analysis process of the user's metabolic state according to the average temperature in the monitoring period; The environment adjustment unit compares the average temperature t of the monitoring period with each preset temperature, and adjusts the analysis process of the user's metabolic state according to the comparison result, wherein: When t<T1, the environment adjustment unit determines that the environment temperature is low during the monitoring period, and adjusts each preset abnormal physical sign index to Az', setting Az'=Az×cos[(T1-t) / T1], and setting z=1,2,3; When T1≤t<T2, the environment adjustment unit determines that the environment temperature is normal within the monitoring period and does not make any adjustment; When t≥T2, the environment adjustment unit determines that the environment temperature is high during the monitoring period, and adjusts each preset abnormal vital sign index to Az", and sets Az"=Az×1+sin[(T2-t) / T2]; Wherein, T1 is the left value of the preset temperature range of the movement, T2 is the right value of the preset temperature range of the movement, and T1<T2.

3. The health status monitoring system based on big data according to claim 2 is characterized in that: The adjustment and optimization module is further provided with a heat dissipation optimization unit, which analyzes the abnormality of environmental heat dissipation according to environmental parameters within the monitoring period, and optimizes the adjustment process of the user's metabolic state according to the analysis results; The heat dissipation optimization unit compares the environmental heat dissipation coefficient β with the environmental heat dissipation threshold D, and analyzes the environmental heat dissipation abnormality according to the comparison result. The heat dissipation optimization unit also optimizes the adjustment process of the user's metabolic state according to the environmental heat dissipation abnormality analysis result, wherein: When β≥D, the heat dissipation optimization unit determines that the abnormality of environmental heat dissipation during the monitoring period is normal and does not perform optimization; When β<D, the heat dissipation optimization unit determines that the environmental heat dissipation abnormality within the monitoring period is abnormal, and adjusts the right value of the motion preset temperature range to T2', setting T2'=T2×{1-arctan[(D-β) / D]}.

4. The health status monitoring system based on big data according to claim 3 is characterized in that: The health status assessment module is provided with a health status analysis unit, which analyzes the health status of the user during the monitoring period according to the metabolic status analysis results and the abnormal exercise intensity status analysis results of the user during the monitoring period, wherein: When the user's metabolic state is normal and the exercise intensity state is normal, the health state analysis unit determines that the user's health state is normal and does not issue an alarm to the user; When the user's metabolic state is normal and the exercise intensity state is abnormal, the health state analysis unit determines that the user's health state is a load exercise state and does not issue an alarm to the user; When the user's metabolic state is abnormal and the exercise intensity is normal, the health state analysis unit determines that the user's health state is a fatigue exercise state and issues a fatigue exercise alarm to the user; When the user's metabolic state is abnormal and the exercise state is abnormal, the health state analysis unit determines that the user's health state is an abnormal physical state and issues a high-intensity alarm to the user.

5. The health status monitoring system based on big data according to claim 4 is characterized in that: The health status assessment module is further provided with a motion assessment unit, which assesses the user's motion status according to the analysis result of the health status within the monitoring period, wherein: When the user's health status is normal, the exercise evaluation unit determines that the user's exercise status is low-load exercise, and adjusts the user's exercise intensity to K', setting K'=K×γ; When the user's health status is a load exercise state, the exercise evaluation unit determines that the user's exercise state is a normal high-load exercise and does not adjust the user's exercise intensity; When the user's health status is a fatigued exercise state, the exercise evaluation unit determines that the user's exercise state is a long-term exercise and does not adjust the user's exercise intensity; When the user's health status is an abnormal physical state, the exercise evaluation unit determines that the user's exercise status is an abnormally high-load exercise, and adjusts the user's exercise intensity to K", setting K"=K×cos[(α-Az) / Az]×γ; Among them, γ is the exercise intensity index, and K is the user's exercise intensity during the monitoring period; The exercise evaluation unit is further configured to output the adjusted exercise intensity to the user.

6. A health status monitoring method based on big data, applied to the health status monitoring system based on big data as claimed in any one of claims 1 to 5, characterized in that: include: Step S1, obtaining the user's physical sign data and exercise data in real time during the monitoring period, and obtaining the user's historical health data; Step S2, calculating the average abnormal exercise intensity of the user according to the historical health data of the user, and classifying the user's physical fitness level according to the calculation result of the average abnormal exercise intensity of the user; Step S3, analyzing the aerobic state and load state according to the user's physical sign data during the monitoring period, and analyzing the user's metabolic state during the monitoring period according to the user's physical fitness level classification result, the aerobic state and load state analysis results; Step S4, adjusting the user's metabolic state analysis process according to the average temperature during the monitoring period, analyzing the abnormality of environmental heat dissipation according to the environmental parameters during the monitoring period, and optimizing the user's metabolic state adjustment process according to the results of the abnormality analysis of environmental heat dissipation; Step S5, analyzing the abnormal state of the user's exercise intensity according to the user's physical fitness level classification result and exercise data during the monitoring period; Step S6, analyzing the user's health status according to the analysis results of the user's metabolic status and the abnormal exercise intensity status during the monitoring period, and evaluating the user's exercise status according to the analysis results of the health status.

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