A health status intelligent monitoring system

Abstract

The present invention relates to the technical field of health data monitoring, and in particular to a health status intelligent monitoring system, comprising: an information acquisition module for acquiring a user's vital sign information, work information and daily information; an analysis number module for analyzing an analysis cycle number; a work analysis module for analyzing a work node number and a rest duration; a motion analysis module for analyzing a motion cycle number and a motion duration; a history analysis module for storing a user's vital sign information, daily information and motion duration, and analyzing a vital sign state parameter, a motion parameter and a sleep parameter; a user analysis module for analyzing a first user type and a second user type; a health analysis module for analyzing a health state parameter; and a state output module for analyzing and outputting a user's health state. The present invention realizes accurate monitoring of a user's health state.

Description

Technical Field

[0001] The present invention relates to the technical field of health data monitoring, and in particular to an intelligent health status monitoring system. Background Art

[0002] With the rapid development of science and technology, especially the integration of artificial intelligence and Internet of Things technology, user sports health status monitoring technology has come into being. This technology uses precise sensors to monitor users' sports data, heart rate changes, sleep quality and other key health indicators in real time, providing users with comprehensive and accurate health information.

[0003] Chinese Patent Publication No.: CN108492890A discloses a human health status monitoring system and method, including: a monitoring sensor, which is installed and fixed on a sitting or sleeping furniture, collects various parameter information of a person's physical state through the sitting or sleeping furniture, and sends it to a data acquisition module; a data conversion module, which receives the information sent by the monitoring sensor, performs amplification, filtering and analog-to-digital conversion, and then forwards it to a control processing module; a clock module, which generates real-time time and provides it to the control processing module; the control processing module is connected to the data acquisition module, the storage module, the operation comparison module, the clock module, and the communication interface module, receives various human state parameter information data sent by the data acquisition module, and processes it; the storage module receives the instructions and data sent by the control processor module for storage and reading; the operation comparison module receives the data and instructions sent by the control processor module, performs operation comparison according to the set model algorithm, and returns the result of the operation comparison to the control processing module. The invention realizes the analysis of the monitoring of the user's health status, but does not realize the comprehensive analysis of the user's daily work and life data and exercise data. There are problems of low efficiency in analyzing the user's health status and inaccurate monitoring of the user's health status. Summary of the invention

[0004] To this end, the present invention provides a health status intelligent monitoring system to overcome the problems of low efficiency in analyzing the health status of users and inaccurate monitoring of the health status of users in the prior art.

[0005] To achieve the above object, the present invention provides a health status intelligent monitoring system, comprising:

[0006] Information acquisition module, used to periodically obtain the user's vital information, work information and daily information;

[0007] An analysis number module is used to analyze the analysis cycle number;

[0008] Work analysis module, used to analyze work node number and rest time according to work information and analysis cycle number;

[0009] A motion analysis module is used to analyze the motion cycle number and the motion duration according to the physical sign information, daily information and the analysis cycle number;

[0010] A history analysis module, which is used to store the user's physical sign information, daily information and exercise duration, and analyze the physical sign state parameters, exercise parameters and sleep parameters based on the stored physical sign information, daily information and exercise duration of the user;

[0011] A user analysis module, for analyzing the first user type and the second user type according to daily information and rest duration;

[0012] A health analysis module, for analyzing health status parameters according to physical state parameters, exercise parameters, sleep parameters, work node number, rest duration, exercise cycle number, first user type and second user type;

[0013] The status output module is used to analyze the user's health status according to the health status parameters and output the user's health status.

[0014] Furthermore, the analysis numbering module sets each preset analysis period in a day as an analysis time period, and numbers each analysis time period in chronological order to obtain an analysis period number, and sets the analysis period number to i, i∈N + .

[0015] Furthermore, the work analysis module is provided with a work analysis unit, which is used to analyze the work node number according to the node parameter and the analysis cycle number. The work analysis unit counts the analysis cycle number between the first node parameter and the second node parameter corresponding to each work number as the work node number, and sets the work node number to u, u∈U, U represents the set of work node numbers, U={u|u∈N + ,K1(j)≤u≤K2(j)}, K1(j) represents the first node parameter, K2(j) represents the second node parameter;

[0016] The work analysis module is provided with a rest analysis unit, which is used to analyze the rest duration according to the work node number, and set the rest duration to Q=1440-NU×T, wherein Q represents the rest duration, NU represents the number of work node numbers in the set of work node numbers, and T represents the duration of the preset analysis cycle.

[0017] Furthermore, the motion analysis module analyzes the motion cycle number according to the heart rate, the number of motion steps and the analysis cycle number, wherein:

[0018] When R(i)-R(i-1)≥r1 and W(i)≥w, the motion analysis module determines that the current analysis cycle number is the motion cycle number, and sets the motion cycle number v=i;

[0019] When R(i)-R(i-1)<r1 or W(i)<w, the motion analysis module does not analyze the motion cycle number;

[0020] The motion analysis module analyzes the motion duration according to the motion cycle number and sets the motion duration to A=NV×T, wherein A represents the motion duration, NV represents the number of motion cycle numbers, v represents the motion cycle number, v∈V, V represents the set of motion cycle numbers, R(i) represents the number of motion steps, R(i-1) represents the number of motion steps in the previous analysis cycle, r1 represents the first step threshold, W(i) represents the heart rate, and w represents the heart rate threshold.

[0021] Furthermore, the history analysis module calculates the vital sign state parameter according to the stored blood oxygen saturation through a first history analysis formula, and the history analysis module is provided with a first history analysis formula as follows:

[0022]

[0023] Where P1 represents the physical state parameter, C(d,i) represents the stored blood oxygen saturation, d represents the storage day number, d∈N + , d max Indicates the maximum value of the stored day number.

[0024] Furthermore, the history analysis module calculates the motion parameters according to the stored motion duration and motion steps through a second analysis formula, and the history analysis module is provided with a second analysis formula as follows:

[0025]

[0026] Among them, P2 represents the motion parameter, R(d,i max ) represents the number of stored motion steps, i max represents the maximum value of the analysis cycle number, r2 represents the second step number threshold, and A(d) represents the stored exercise duration.

[0027] Furthermore, the user analysis module is provided with a first classification unit, which is used to compare the body mass index of the user with the index threshold, and analyze the first user type of the user according to the comparison result, wherein:

[0028] When B<b1, the first classification unit determines that the first user type of the user is thin;

[0029] When b1≤B≤b2, the first classification unit determines that the first user type of the user is normal;

[0030] When B>b2, the first classification unit determines that the first user type of the user is overweight;

[0031] Wherein, b1 represents the first index threshold, and b2 represents the second index threshold;

[0032] The user analysis module is further provided with a second classification unit, which is used to analyze the second user type of the user according to the rest time and the sleep time, wherein:

[0033] When Q / (1440-S)≥q, the second classification unit determines that the second user type of the user has ample time;

[0034] When Q / (1440-S)<q, the second classification unit determines that the second user type of the user is short of time;

[0035] Among them, S represents the sleep duration, and q represents the rest comparison threshold.

[0036] Furthermore, the health analysis module is provided with a health analysis unit, which is used to calculate the health status parameter through a health analysis formula according to the exercise parameter, rest time and sleep parameter. The health analysis unit is provided with a health analysis formula as follows:

[0037] H=P2×ln[1+(P3+Q) / 1440]

[0038] Wherein, H represents the health status parameter.

[0039] Furthermore, the health analysis module is further provided with a health adjustment unit, which is used to adjust the analysis process of the health status parameters according to the physical sign status parameters, the first user type and the second user type, wherein:

[0040] When the first user type is normal, the health adjustment unit does not adjust the analysis process of the health status parameter;

[0041] When the first user type is thin and the second user type is full, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1×α] is set;

[0042] When the first user type is thin and the second user type is short, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1] is set;

[0043] When the first user type is overweight and the second user type is full, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)-P1×α] is set;

[0044] When the first user type is overweight and the second user type is underweight, the health adjustment unit adjusts the health status parameter, and the adjusted health status parameter is H1, and H1=H×lg[C(i)-P1] is set;

[0045] Where C(i) represents blood oxygen saturation, and α represents the adjustment parameter;

[0046] The health analysis module is also provided with a health optimization unit, which is used to optimize the adjustment process of the health status parameters according to the coincidence cycle number, wherein:

[0047] when When the health optimization unit does not optimize the adjustment process of the health status parameter;

[0048] when When the health optimization unit optimizes the adjustment process of the health state parameter, the optimized health state parameter is H2, and H2=H1 / e NY×60 / 1440 ;

[0049] Wherein, NY represents the number of coincidence cycle numbers in the set of coincidence cycle numbers.

[0050] Furthermore, the status output module analyzes the user's health status according to the health status parameters, wherein:

[0051] When H

[0052] When h1≤H≤h2, the status output module determines that the user's health status is good;

[0053] When h>h2, the status output module determines that the user's health status is excellent;

[0054] Among them, h1 represents the first health threshold, and h2 represents the second health threshold.

[0055] ​Compared with the prior art, the beneficial effect of the present invention is that the information acquisition module acquires the user's vital signs information, work information and daily information to improve the accuracy of information acquisition, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The analysis number module analyzes the analysis cycle number to correspond the data acquired every day to the number of each time period one by one to ensure data uniformity, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The work analysis module analyzes the work information and the analysis cycle number to analyze the work node number. and rest duration, the work node number is used to represent the number of the user's work period, and the rest duration is used to represent the duration of the user's non-working time every day, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The motion analysis module analyzes the vital sign information, daily information and analysis cycle number to analyze the motion cycle number and exercise duration, and uses the motion cycle number to represent the analysis cycle number corresponding to the user's exercise, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The history analysis module stores and analyzes the vital sign information, daily information and exercise duration to Analyze the physical sign state parameters, exercise parameters and sleep parameters, use the physical sign state parameters to represent the fluctuation of blood oxygen saturation in the user's historical data, use the exercise parameters to represent the relationship between the user's daily exercise volume and exercise duration, and use the sleep parameters to represent the user's daily sleep conditions, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The user analysis module analyzes daily information and rest duration to analyze the first user type and the second user type, and classifies the users according to their weight status and free time length, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. Accuracy, through the analysis of the vital sign status parameters, motion parameters, sleep parameters, work node number, rest time, motion cycle number, the first user type and the second user type by the health analysis module to analyze the health status parameters, and use the health status parameters to represent the relationship between the user's daily work data and the motion data, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. Through the analysis of the health status parameters by the status output module, the user's health status is analyzed to realize the monitoring of the user's health status, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the structure of the intelligent health status monitoring system of this embodiment.

[0057] Figure 2Schematic diagram of the structure of the work analysis module of this embodiment.

[0058] Figure 3 Schematic diagram of the structure of the user analysis module of this embodiment.

[0059] Figure 4 This is a schematic diagram of the structure of the health analysis module of this embodiment. DETAILED DESCRIPTION

[0060] 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.

[0061] 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.

[0062] In addition, 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 it can be indirectly connected 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.

[0063] See also Figure 1 As shown, it is a health status intelligent monitoring system of this embodiment, including:

[0064] The information acquisition module is used to periodically acquire the user's vital information, work information and daily information. The user is an employee working in the enterprise. The vital information includes heart rate and blood oxygen saturation. The work information includes working hours, off-get off work hours and the number of working periods. The work information is the user's daily work information. The number of working periods is the number of working periods of the user in the enterprise every day. For example, the user's working hours are 9:00-11:00 and 13:00-18:00 every day, and the number of working periods is 2. The on-work hours and off-get off work hours are the on-work hours and off-get off work hours of each work period. The on-work hours and off-get off work hours are 24-hour time format data. The daily information includes the user's height, weight, exercise steps and sleep duration. The exercise steps are the daily exercise steps, which are used to count each The number of exercise steps from 0:00 to 23:59 during the day, the sleep time is the daily sleep time, which is the sleep time from 18:00 of the previous day to 17:59 of the current day, the vital sign information and daily information are obtained through wearable smart devices such as smart bracelets, smart watches, etc., and the work information is obtained through user interactive input. In this embodiment, the frequency of obtaining the user's vital sign information and daily information is once per preset analysis period. In this embodiment, the preset analysis period is set to 15 minutes. In this embodiment, the preset analysis period is not specifically limited, and technicians in this field can freely set it, such as 10 minutes, 30 minutes and 60 minutes, etc. The setting of the preset analysis period should meet the requirement of being less than or equal to 60 minutes. In this embodiment, the frequency of obtaining the user's work information is once a day;

[0065] An analysis number module, used for analyzing the analysis cycle number, the analysis number module is connected to the information acquisition module;

[0066] A work analysis module, used to analyze the work node number and the rest time according to the work information and the analysis cycle number, and the work analysis module is connected to the analysis number module;

[0067] A motion analysis module, used to analyze the motion cycle number and the motion duration according to the physical sign information, daily information and the analysis cycle number, and the motion analysis module is connected to the analysis number module;

[0068] A history analysis module, used to store the user's physical sign information, daily information and exercise duration, and analyze the physical sign state parameters, exercise parameters and sleep parameters according to the stored physical sign information, daily information and exercise duration of the user, the history analysis module is connected to the exercise analysis module;

[0069] A user analysis module, used to analyze the first user type and the second user type according to daily information and rest duration, the user analysis module being connected to the work analysis module;

[0070] A health analysis module, used to analyze the health status parameters according to the physical state parameters, exercise parameters, sleep parameters, work node number, rest time, exercise cycle number, first user type and second user type, and the health analysis module is connected with the user analysis module and the history analysis module;

[0071] The status output module is used to analyze the user's health status according to the health status parameters and output the user's health status. The status output module is connected to the health analysis module.

[0072] See also Figure 2 As shown, the work analysis module includes:

[0073] A node analysis unit, used to analyze node parameters according to working information;

[0074] A work analysis unit, used to analyze the work node number according to the node parameters, the work analysis unit is connected to the node analysis unit;

[0075] The rest analysis unit is used to analyze the rest duration according to the work node number, and the rest analysis unit is connected to the work analysis unit.

[0076] See also Figure 3 As shown, the user analysis module includes:

[0077] A mass analysis unit, used to analyze the body mass index of the user according to the height and weight of the user;

[0078] a first classification unit, configured to analyze the first user type according to the user's body mass index, the first classification unit being connected to the mass analysis unit;

[0079] The second classification unit is used to analyze the second user type according to the rest time and the sleep time, and the second classification unit is connected to the first classification unit.

[0080] See also Figure 4 As shown, the health analysis module includes:

[0081] A health analysis unit for analyzing health status parameters based on exercise parameters, rest duration, and sleep parameters;

[0082] A health adjustment unit, used for adjusting the analysis process of the health status parameter according to the physical sign status parameter, the first user type and the second user type, the health adjustment unit being connected to the health analysis unit;

[0083] The health optimization unit is used to optimize the adjustment process of the health status parameter according to the working node number and the motion cycle number, and the health optimization unit is connected to the health adjustment unit.

[0084] Specifically, in this embodiment, the information acquisition module acquires the user's vital signs information, work information and daily information to improve the accuracy of information acquisition, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The analysis number module analyzes the analysis cycle number to match the data acquired every day with the number of each time period one by one to ensure data uniformity, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The work analysis module analyzes the work information and the analysis cycle number to analyze the work node number and the rest time. The working node number indicates the number of the user's working period, and the rest time indicates the length of the user's non-working time every day, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The motion analysis module analyzes the vital sign information, daily information and analysis cycle number to analyze the motion cycle number and the motion duration, and the motion cycle number indicates the analysis cycle number corresponding to the user's exercise, thereby improving the system's analysis efficiency of the user's health status and the accuracy of monitoring the user's health status. The history analysis module stores and analyzes the vital sign information, daily information and exercise duration to analyze the physical sign information. The physical state parameters, exercise parameters and sleep parameters are used to represent the fluctuation of blood oxygen saturation in the user's historical data, the exercise parameters are used to represent the relationship between the user's daily exercise volume and exercise duration, and the sleep parameters are used to represent the user's daily sleep conditions, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The user analysis module analyzes daily information and rest duration to analyze the first user type and the second user type, and classifies users according to their weight status and free time length, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The health analysis module analyzes the vital sign status parameters, motion parameters, sleep parameters, work node number, rest duration, motion cycle number, the first user type and the second user type to analyze the health status parameters, and uses the health status parameters to represent the relationship between the user's daily work data and the motion data, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status. The status output module analyzes the health status parameters to analyze the user's health status and realizes the monitoring of the user's health status, thereby improving the system's analysis efficiency of the user's health status and improving the accuracy of monitoring the user's health status.

[0085] Specifically, the analysis number module in this embodiment sets each preset analysis period in a day as an analysis time period, and numbers each analysis time period in chronological order to obtain an analysis period number, and sets the analysis period number to i, i∈N + .

[0086] Specifically, the analysis numbering module described in this embodiment divides the analysis time periods starting from 0:00 every day and numbers the analysis time periods, using different analysis cycle numbers to correspond to different analysis time periods in a day. For example, when i=1, the analysis time period it represents is from 0:00 to 0:15.

[0087] Specifically, the node analysis unit in this embodiment analyzes the node parameters according to the working information through the node analysis formula, and the node analysis unit is provided with the node analysis formula as follows:

[0088] K1(j)=[t1(j)×60+t2(j)] / T;

[0089] K2(j)=[t3(j)×60+t4(j)] / T;

[0090] Where K1(j) represents the first node parameter, K1(j) is rounded down, j represents the work number, j∈N + And j≤J, J represents the number of working hours, t1(j) represents the hour part of working time, t2(j) represents the minute part of working time, T represents the duration of the preset analysis cycle, K2(j) represents the second node parameter, K2(j) is rounded to the nearest integer, t3(j) represents the hour part of off-get off work time, and t4(j) represents the minute part of off-get off work time.

[0091] Specifically, in this embodiment, the working hours are t1(j):t2(j), and the off-get off work hours are t3(j):t4(j).

[0092] Specifically, the work analysis unit in this embodiment analyzes the work node number according to the node parameter and the analysis cycle number, and the work analysis unit counts the analysis cycle number between the first node parameter and the second node parameter corresponding to each work number as the work node number, and sets the work node number to u, u∈U, U represents the set of work node numbers, U={u|u∈N + ,K1(j)≤u≤K2(j)}.

[0093] Specifically, the rest analysis unit in this embodiment analyzes the rest duration according to the working node number, and sets the rest duration to Q=1440-NU×T, where Q represents the rest duration, and NU represents the number of working node numbers in the set of working node numbers.

[0094] Specifically, the motion analysis module in this embodiment analyzes the motion cycle number according to the heart rate, the number of motion steps and the analysis cycle number, wherein:

[0095] When R(i)-R(i-1)≥r1 and W(i)≥w, the motion analysis module determines that the current analysis cycle number is the motion cycle number, and sets the motion cycle number v=i;

[0096] When R(i)-R(i-1)<r1 or W(i)<w, the motion analysis module does not analyze the motion cycle number;

[0097] The motion analysis module analyzes the motion duration according to the motion cycle number, and sets the motion duration to A=NV×T, wherein A represents the motion duration, NV represents the number of motion cycle numbers, v represents the motion cycle number, v∈V, V represents the set of motion cycle numbers, R(i) represents the number of motion steps, R(i-1) represents the number of motion steps in the previous analysis cycle, r1 represents the first step threshold, 500≤r1≤700, W(i) represents the heart rate, w represents the heart rate threshold, 140≤w≤180. It can be understood that in this embodiment, the values ​​of the first step threshold and the heart rate threshold are not specifically limited, and those skilled in the art can freely set them as long as they meet the analysis of the motion cycle. The optimal values ​​of the first step threshold and the heart rate threshold are: r1=600, r2=150.

[0098] Specifically, the historical analysis module in this embodiment calculates the vital sign state parameter according to the stored blood oxygen saturation through the first historical analysis formula, and the historical analysis module is provided with the first historical analysis formula as follows:

[0099]

[0100] Where P1 represents the physical state parameter, C(d,i) represents the stored blood oxygen saturation, d represents the storage day number, d∈N + , d max Indicates the maximum value of the stored day number.

[0101] Specifically, the historical analysis module in this embodiment calculates the motion parameters according to the stored motion duration and motion steps through a second analysis formula, and the historical analysis module is provided with a second analysis formula as follows:

[0102]

[0103] Among them, P2 represents the motion parameter, R(d,i max ) represents the number of stored motion steps, i maxrepresents the maximum value of the analysis cycle number, r2 represents the second step threshold, 8000≤r2≤10000, and A(d) represents the stored exercise duration. It can be understood that the value of the second step threshold is not specifically limited in this embodiment, and can be freely set by those skilled in the art as long as the calculation of the exercise parameters is satisfied. The optimal value of the second step threshold is: r2=8000.

[0104] Specifically, the historical analysis module in this embodiment calculates the sleep parameter according to the stored sleep duration, and sets the sleep parameter to P3 = ∑S(d) / d max , where P3 represents the sleep parameter and S(d) represents the stored sleep duration.

[0105] Specifically, the mass analysis unit in this embodiment calculates the user's body mass index according to the user's height and weight through a body mass index analysis formula. The body mass index analysis formula of the mass analysis unit is as follows:

[0106] B=M / L 2 ;

[0107] Among them, B represents body mass index, M represents user weight, and L represents user height.

[0108] Specifically, in this embodiment, the first classification unit compares the body mass index of the user with the index threshold, and analyzes the first user type of the user according to the comparison result, wherein:

[0109] When B<b1, the first classification unit determines that the first user type of the user is thin;

[0110] When b1≤B≤b2, the first classification unit determines that the first user type of the user is normal;

[0111] When B>b2, the first classification unit determines that the first user type of the user is overweight;

[0112] Wherein, b1 represents the first index threshold, 18≤b1≤19, b2 represents the second index threshold, 23.5≤b2≤24.5. It can be understood that the values ​​of the index thresholds are not specifically limited in this embodiment, and those skilled in the art can freely set them as long as they meet the judgment of the first user type. The optimal values ​​of the index thresholds are: b1=18.5, b2=24.

[0113] Specifically, in this embodiment, the second classification unit analyzes the second user type of the user according to the rest time and the sleep time, wherein:

[0114] When Q / (1440-S)≥q, the second classification unit determines that the second user type of the user has ample time;

[0115] When Q / (1440-S)<q, the second classification unit determines that the second user type of the user is short of time;

[0116] Wherein, S represents the sleep duration, q represents the rest comparison threshold, and 0.3≤q≤0.5. It can be understood that the value of the rest comparison threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the judgment of the second user category. The optimal value of the rest comparison threshold is: q=0.4.

[0117] Specifically, the health analysis unit in this embodiment calculates the health status parameter according to the exercise parameter, rest time and sleep parameter through the health analysis formula. The health analysis unit is provided with the following health analysis formula:

[0118] H=P2×ln[1+(P3+Q) / 1440]

[0119] Wherein, H represents the health status parameter.

[0120] Specifically, the health adjustment unit in this embodiment adjusts the analysis process of the health status parameter according to the physical sign status parameter, the first user type and the second user type, wherein:

[0121] When the first user type is normal, the health adjustment unit does not adjust the analysis process of the health status parameter;

[0122] When the first user type is thin and the second user type is full, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1×α] is set;

[0123] When the first user type is thin and the second user type is short, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1] is set;

[0124] When the first user type is overweight and the second user type is full, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)-P1×α] is set;

[0125] When the first user type is overweight and the second user type is underweight, the health adjustment unit adjusts the health status parameter, and the adjusted health status parameter is H1, and H1=H×lg[C(i)-P1] is set;

[0126] Wherein, C(i) represents blood oxygen saturation, α represents adjustment parameter, and 0.6≤α<1. It can be understood that the value of the adjustment parameter is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the adjustment of the health status parameter is satisfied. The optimal value of the adjustment parameter is: α=0.8.

[0127] Specifically, the health optimization unit described in this embodiment analyzes the coincidence cycle number according to the working node number and the motion cycle number, and sets the coincidence cycle number to y, y∈Y, Y=U∩V, wherein y represents the coincidence cycle number, and Y represents the set of coincidence cycle numbers.

[0128] Specifically, the health optimization unit in this embodiment optimizes the adjustment process of the health status parameter according to the coincidence cycle number, wherein:

[0129] when When the health optimization unit does not optimize the adjustment process of the health status parameter;

[0130] when When the health optimization unit optimizes the adjustment process of the health state parameter, the optimized health state parameter is H2, and H2=H1 / e NY×60 / 1440 ;

[0131] Wherein, NY represents the number of coincidence cycle numbers in the set of coincidence cycle numbers.

[0132] Specifically, the status output module in this embodiment analyzes the user's health status according to the health status parameters, wherein:

[0133] When H

[0134] When h1≤H≤h2, the status output module determines that the user's health status is good;

[0135] When h>h2, the status output module determines that the user's health status is excellent;

[0136] Among them, h1 represents the first health threshold, 0.6≤h1≤0.8, h2 represents the second health threshold, 1.4≤h2≤1.6. It can be understood that the value of the health threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it meets the judgment of the user's health status. The optimal value of the health threshold is: h1=0.7, h2=1.5.

[0137] ​Specifically, in this embodiment, when the health status is poor, it means that the user lacks exercise and should increase the exercise time to improve physical fitness. When the health status is good, it means that the user's exercise intensity is moderate and the exercise time can be increased according to their own situation. When the health status is excellent, it means that the user's exercise time is sufficient and he or she should continue to maintain a reasonable exercise time.

[0138] 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 intelligent monitoring system, characterized in that: include: An information acquisition module, used to periodically acquire the user's vital information, work information and daily information, wherein the vital information includes heart rate and blood oxygen saturation, the work information includes working hours, off-get off work hours and number of working hours, and the daily information includes user height, user weight, exercise steps and sleep duration; An analysis number module is used to analyze the analysis cycle number; The analysis number module sets each preset analysis period in a day as an analysis time period, and numbers each analysis time period in chronological order to obtain an analysis period number, and sets the analysis period number to i, i∈N + ; Work analysis module, used to analyze work node number and rest duration according to work information and analysis cycle number; The work analysis module is provided with a work analysis unit, which is used to analyze the work node number according to the node parameter and the analysis cycle number. The work analysis unit counts the analysis cycle number between the first node parameter and the second node parameter corresponding to each work number as the work node number, and sets the work node number to u, u∈U, U represents the set of work node numbers, U={u|u∈N + ,K1(j)≤u≤K2(j)}, K1(j) represents the first node parameter, K2(j) represents the second node parameter; The work analysis module is provided with a rest analysis unit, which is used to analyze the rest duration according to the work node number, and set the rest duration to Q=1440-NU×T, where Q represents the rest duration, NU represents the number of work node numbers in the set of work node numbers, and T represents the duration of a preset analysis cycle; A motion analysis module is used to analyze the motion cycle number and the motion duration according to the physical sign information, daily information and the analysis cycle number; The motion analysis module analyzes the motion cycle number according to the heart rate, the number of motion steps and the analysis cycle number, wherein: When R(i)-R(i-1)≥r1 and W(i)≥w, the motion analysis module determines that the current analysis cycle number is the motion cycle number, and sets the motion cycle number v=i; When R(i)-R(i-1)<r1 or W(i)<w, the motion analysis module does not analyze the motion cycle number; The motion analysis module analyzes the motion duration according to the motion cycle number, and sets the motion duration to A=NV×T, wherein A represents the motion duration, NV represents the number of motion cycle numbers, v represents the motion cycle number, v∈V, V represents the set of motion cycle numbers, R(i) represents the number of motion steps, R(i-1) represents the number of motion steps in the previous analysis cycle, r1 represents the first step number threshold, W(i) represents the heart rate, w represents the heart rate threshold, and T represents the duration of the preset analysis cycle; A history analysis module, which is used to store the user's physical sign information, daily information and exercise duration, and analyze the physical sign state parameters, exercise parameters and sleep parameters based on the stored physical sign information, daily information and exercise duration of the user; The history analysis module calculates the vital sign state parameter according to the stored blood oxygen saturation through the first history analysis formula. The history analysis module is provided with the first history analysis formula as follows: ； Where P1 represents the physical state parameter, C(d,i) represents the stored blood oxygen saturation, d represents the storage day number, d∈N + , d max Indicates the maximum value of the storage day number; The history analysis module calculates the motion parameters according to the stored motion duration and motion steps through a second analysis formula. The history analysis module is provided with a second analysis formula as follows: ； Among them, P2 represents the motion parameter, R(d,i max ) represents the number of stored motion steps, i max represents the maximum value of the analysis cycle number, r2 represents the second step number threshold, and A(d) represents the stored exercise duration; A user analysis module, for analyzing the first user type and the second user type according to daily information and rest duration; A health analysis module, for analyzing health status parameters according to physical state parameters, exercise parameters, sleep parameters, work node number, rest duration, exercise cycle number, first user type and second user type; The health analysis module is provided with a health analysis unit, which is used to calculate the health status parameter through a health analysis formula according to the exercise parameter, rest time and sleep parameter. The health analysis unit is provided with a health analysis formula as follows: H = P2 × ln [1 + (P3 + Q) / 1440] Where H represents the health status parameter, P3=∑S(d) / d max , P3 represents the sleep parameter, S(d) represents the stored sleep duration; The status output module is used to analyze the user's health status according to the health status parameters and output the user's health status.

2. The intelligent health status monitoring system according to claim 1, characterized in that: The user analysis module is provided with a first classification unit, which is used to compare the body mass index B of the user with the index threshold, and analyze the first user type of the user according to the comparison result, wherein: When B<b1, the first classification unit determines that the first user type of the user is thin; When b1≤B≤b2, the first classification unit determines that the first user type of the user is normal; When B>b2, the first classification unit determines that the first user type of the user is overweight; Wherein, b1 represents the first index threshold, and b2 represents the second index threshold; The user analysis module is further provided with a second classification unit, which is used to analyze the second user type of the user according to the rest time and the sleep time, wherein: When Q / (1440-S)≥q, the second classification unit determines that the second user type of the user has ample time; When Q / (1440-S)<q, the second classification unit determines that the second user type of the user is short of time; Among them, S represents the sleep duration, and q represents the rest comparison threshold.

3. The intelligent health status monitoring system according to claim 2, characterized in that: The health analysis module is further provided with a health adjustment unit, which is used to adjust the analysis process of the health status parameters according to the physical sign status parameters, the first user type and the second user type, wherein: When the first user type is normal, the health adjustment unit does not adjust the analysis process of the health status parameter; When the first user type is thin and the second user type is full, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1×α] is set; When the first user type is thin and the second user type is short, the health adjustment unit adjusts the health state parameter, and the adjusted health state parameter is H1, and H1=H×lg[C(i)+P1] is set; When the first user type is overweight and the second user type is adequate, the health adjustment unit adjusts the health state parameter, the adjusted health state parameter is H1, and H1=H×lg[C(i)-P1×α] is set; When the first user type is overweight and the second user type is underweight, the health adjustment unit adjusts the health status parameter, and the adjusted health status parameter is H1, and H1=H×lg[C(i)-P1] is set; Where C(i) represents blood oxygen saturation, and α represents the adjustment parameter; The health analysis module is also provided with a health optimization unit, which is used to optimize the adjustment process of the health status parameters according to the coincidence cycle number, wherein: When Y= When the health optimization unit does not optimize the adjustment process of the health status parameter; When Y≠ When the health optimization unit optimizes the adjustment process of the health state parameter, the optimized health state parameter is H2, and H2=H1 / e NY×60 / 1440 ; Wherein, NY represents the number of coincidence cycle numbers in the set of coincidence cycle numbers, and Y represents the set of coincidence cycle numbers.

4. The intelligent health status monitoring system according to claim 3, characterized in that: The status output module analyzes the user's health status according to the health status parameters, wherein: When H<h1, the status output module determines that the user's health status is poor; When h1≤H≤h2, the status output module determines that the user's health status is good; When H>h2, the status output module determines that the user's health status is excellent; Among them, h1 represents the first health threshold, and h2 represents the second health threshold.

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