A TCM syndrome analysis system and electronic device for health status

Abstract

The present invention provides a TCM syndrome type analysis system and electronic device for health status, and relates to the technical field of data processing. The data processing module in the system determines the pulse wave of the user according to the acquired heart rate data; the characteristic analysis module determines the multiple pulse characteristic values ​​in each pulse wave cycle according to the pulse wave, and also determines the pulse characteristic mean according to all the pulse characteristic values ​​in the preset time length; the health judgment module determines whether the multiple pulse characteristic mean values ​​are all within the corresponding health standard range, and when the multiple pulse characteristic mean values ​​are all within the corresponding health standard range, the user is judged to be healthy; when there is a pulse characteristic mean value that is not within the corresponding health standard range, the significant analysis module determines the significance of the multiple pulse characteristic values; the syndrome type analysis module then determines the TCM syndrome type to which the user's physical health status belongs. Real-time monitoring of the user is achieved, which is convenient for popularization and use in daily life.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a TCM syndrome analysis system and electronic equipment for health status. Background Art

[0002] Sub-health refers to a physiological state in which the body, although without a clear disease, shows reduced vitality and varying degrees of reduced adaptability. It is caused by the low physiological functions and metabolic processes of various systems of the body, and is a state of low physiological function between health and disease. Health is a perfect state of body, mind and communication, not just the absence of physical disease. According to statistics, the total number of truly healthy and sick people accounts for less than 1 / 3, and more than 2 / 3 of the population is in a transitional state between health and disease. Generally, when the human body feels the symptoms of a disease, it can determine the physical condition through medical examinations. If the human body is sub-healthy, the various indicators obtained through medical examinations are still within the normal range, and other detection methods such as psychological examinations or inquiries are still needed for auxiliary analysis. Such detection and analysis methods are relatively complicated, and the test results have a certain lag, making it difficult to promote and popularize in daily life. Summary of the invention

[0003] The problem to be solved by the present invention is that the existing health status analysis method is not suitable for use in daily life, the detection results have a certain lag, and it is difficult to achieve real-time monitoring of individuals.

[0004] To solve the above problems, in a first aspect, the present invention provides a TCM syndrome analysis system for health status, comprising:

[0005] A data processing module, used to determine the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user;

[0006] A feature analysis module, for determining, based on the pulse wave, a plurality of pulse feature values ​​of the user in each pulse wave cycle, wherein the plurality of pulse feature values ​​include a main wave amplitude, a dicrotic wave amplitude, a beat time ratio, a frequency concentration value, and a concentrated frequency amplitude value; and for determining, based on all the pulse feature values ​​within a preset time period, a pulse feature mean value corresponding to each pulse feature value;

[0007] a health judgment module, used to judge whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range; and also used to judge that the user is in good health when the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range;

[0008] A significance analysis module, for determining the significance of a plurality of pulse characteristic values ​​according to the age of the user, all the pulse characteristic values ​​of the user within a preset time period, and all the pulse characteristic values ​​corresponding to pre-stored health data samples when there is a pulse characteristic mean value that is not within the corresponding health standard range;

[0009] a syndrome analysis module, for determining the TCM syndrome to which the user's physical health status belongs according to the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndromes, and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome;

[0010] The significant analysis module includes:

[0011] A health data calling unit, configured to call the pre-stored health data samples corresponding to the age range according to the age range of the user, wherein the age is divided into a plurality of age ranges, and each age range corresponds to a set of pre-stored health data samples;

[0012] A significant value analysis unit, for obtaining, based on all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the called health data sample, a variance difference significant value, a mean difference first significant value and a mean difference second significant value corresponding to each of the pulse characteristic values ​​by using an independent sample t-test method, wherein the mean difference first significant value is a significant value obtained by analyzing under the assumption of equal variances, and the mean difference second significant value is a significant value obtained by analyzing under the assumption of not assuming equal variances;

[0013] A significance confirmation unit is used to determine the significance of each pulse characteristic value according to the variance difference significance value and the first significance value of the mean difference corresponding to each pulse characteristic value, or according to the variance difference significance value and the second significance value of the mean difference corresponding to each pulse characteristic value.

[0014] In a second aspect, the present invention further provides an electronic device, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the following steps when executing the computer program:

[0015] Determining the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user;

[0016] Determine multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave, wherein the multiple pulse characteristic values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value;

[0017] According to all the pulse characteristic values ​​within a preset time period, determining a pulse characteristic mean corresponding to each pulse characteristic value;

[0018] Determine whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range;

[0019] When the average values ​​of the plurality of pulse characteristics are within the corresponding health standard range, it is determined that the user is in good health;

[0020] When there is a pulse characteristic mean value that is not within the corresponding health standard range, calling the pre-stored health data sample corresponding to the age range according to the age range of the user, wherein the age is divided into a plurality of age ranges, and each age range corresponds to a set of pre-stored health data samples;

[0021] According to all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the called health data sample, an independent sample t-test method is used to obtain a variance difference significance value, a mean difference first significance value and a mean difference second significance value corresponding to each pulse characteristic value, wherein the mean difference first significance value is a significance value obtained by analyzing under the assumption of equal variance, and the mean difference second significance value is a significance value obtained by analyzing under the assumption of not assuming equal variance;

[0022] Determine the significance of each of the pulse characteristic values ​​according to the variance difference significance value and the mean difference first significance value corresponding to each of the pulse characteristic values, or according to the variance difference significance value and the mean difference second significance value corresponding to each of the pulse characteristic values;

[0023] The TCM syndrome type to which the user's physical health status belongs is determined based on the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndrome types and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome type.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention provides a TCM syndrome analysis system and electronic device for health status. The data processing module determines the pulse wave of the user according to the acquired heart rate data of the user. The heart rate data is collected by the smart wearable device worn by the user, and the heart rate data of the user can be monitored and collected in real time. The feature analysis module determines multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave, wherein the multiple pulse characteristic values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value. The heart rate status of the user can be reflected by these five characteristic values. The feature analysis module also determines the pulse characteristic mean corresponding to each pulse characteristic value according to all pulse characteristic values ​​within a preset time length. Then the health judgment module judges whether the multiple pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic mean value is equal to a The health standard range corresponds to; when multiple pulse feature mean values ​​are within the corresponding health standard range, the user is judged to be healthy, and the judgment result can be sent to the smart wearable device worn by the user, so that the user can grasp the personal physical condition in time; however, when there is a pulse feature mean value that is not within the corresponding health standard range, the significant analysis module determines the significance of multiple pulse feature values ​​according to the user's age, all pulse feature values ​​of the user within the preset time and all pulse feature values ​​corresponding to the pre-stored health data sample; and then the syndrome analysis module determines the TCM syndrome to which the user's physical health state belongs according to the user's age, the significance of multiple pulse feature values, the significance rules corresponding to multiple TCM syndromes and the pulse feature mean values ​​corresponding to multiple pulse feature values, wherein the significance rules are the types of pulse feature values ​​corresponding to each TCM syndrome. The user's physical condition is refined into a specific TCM syndrome, and the final analysis result is fed back and presented on the smart wearable device to realize real-time monitoring of the user, which is convenient for promotion and use in daily life. The refined analysis results facilitate users to accurately grasp their own health status, remind users to adjust their living habits or eating habits in time, etc., to avoid deterioration of their physical condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the structure of a TCM syndrome analysis system for health status provided by an embodiment of the present invention;

[0027] Figure 2 A waveform diagram of a pulse wave provided by an embodiment of the present invention;

[0028] Figure 3 A health standard range table corresponding to different pulse characteristic values ​​in different age ranges provided by an embodiment of the present invention;

[0029] Figure 4 A table of significant rules corresponding to some TCM syndrome types in different age ranges provided in an embodiment of the present invention;

[0030] Figure 5 A table of significant rules corresponding to another part of TCM syndrome types in different age ranges provided in an embodiment of the present invention;

[0031] Figure 6 An analysis result table of the independent sample t-test method provided in the embodiment of the present invention;

[0032] Figure 7 A table of characteristic mean ranges of different TCM syndrome types when the age is 39 or below provided in an embodiment of the present invention;

[0033] Figure 8 A questionnaire provided for an embodiment of the present invention;

[0034] Fig. 9 A table of some experimental verification data provided for the embodiments of the present invention;

[0035] Fig.10 A schematic diagram of the structure of an electronic device application environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0037] It should be understood that the various modules or units described in the system implementation of the present invention may be executed in different orders and / or in parallel. In addition, the system implementation may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0038] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0039] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0040] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0041] In view of the problems existing in the above-mentioned related technologies, this embodiment provides a TCM syndrome analysis system and electronic equipment for health status.

[0042] like Figure 1 As shown, a TCM syndrome analysis system for health status provided by an embodiment of the present invention includes:

[0043] The data processing module 100 is used to determine the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user.

[0044] Specifically, the user wears a smart wearable device in daily life, and the smart wearable device collects the user's heart rate data. The smart wearable device is a wristwatch, and the sensor on the wristwatch monitors the user's pulse signal. The PPG technology (Photoplethysmography) is used to pre-process the collected heart rate data to obtain the overall pulse wave waveform, wherein the PPG technology is a technology that measures blood volume changes by light absorption. The Butterworth filter is used to reduce the noise of the PPG signal waveform, and then the motion artifact correction algorithm based on the Gaussian function is used to remove the motion artifact waveform to obtain the real-time pulse wave. Among them, the Butterworth filter is a type of electronic filter, which is also called the maximum flat filter. The characteristics of the Butterworth filter are that the frequency response curve in the passband is maximally flat and has no ripples, and gradually drops to zero in the stopband; the motion artifact correction algorithm based on the Gaussian function compensates the image by using the Gaussian function to reduce the influence of motion artifacts. Under normal circumstances, the motion artifact correction algorithm based on the Gaussian function will smooth the image to blur the artifacts caused by the movement, making the image look smoother and clearer. The clutter in the waveform and the formed waveform image are processed to form a clear real-time pulse wave, which is convenient for accurately extracting the pulse characteristic value of the monitored object.

[0045] The feature analysis module 200 is used to determine multiple pulse feature values ​​of the user in each pulse wave cycle based on the pulse wave, wherein the multiple pulse feature values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value. These five pulse feature values ​​are referred to as five-dimensional time-frequency feature values.

[0046] Specifically, Figure 2 The pulse wave waveform in a pulse wave cycle is shown in FIG. There is a similar waveform in each pulse wave cycle. By analyzing the pulse wave, the above five pulse characteristic values ​​can be obtained in each pulse wave cycle. These five pulse characteristic values ​​can be obtained through time domain analysis and frequency domain analysis. The main wave amplitude is Figure 2 The height value represented by h1 is the amplitude of the main wave. This characteristic value is used to characterize the cardiac ejection condition and ability. The main wave amplitude is hereinafter referred to as α; the dicrotic wave amplitude is Figure 2 The height value represented by h3+h4 is the dicrotic wave amplitude value, which is used to characterize vascular resistance and vascular conditions. The dicrotic wave amplitude is referred to as β in the following text; the beat time ratio is Figure 2 The ratio of the mid-beat trough t3 / pulse wave period T, the beat time ratio represents the fluctuation time of the heart cycle, and the beat time ratio is hereinafter referred to as γ. The PPG signal is converted into a frequency signal using continuous Fourier transform. The formula for continuous Fourier transform is as follows:

[0047] ,

[0048] in, is the angular frequency; f(t) is the response function or real-time pulse wave function; is the frequency response function, which is the Fourier transform of f(t); j is the imaginary unit and t is the time.

[0049] After continuous Fourier transform, the relationship between the frequency domain phase and the frequency domain amplitude and the frequency is obtained, and then two eigenvalues ​​related to the frequency are obtained: the frequency concentration value represents the frequency value corresponding to the most PPG signals, hereinafter referred to as δ; the concentrated frequency amplitude value represents the frequency value corresponding to the largest number of frequency domain signals, hereinafter referred to as ε.

[0050] The feature analysis module 200 is further used to determine the pulse feature mean value corresponding to each pulse feature value according to all the pulse feature values ​​within a preset time period.

[0051] Specifically, in order to ensure the accuracy of the analysis results, there are certain usage requirements for the acquired heart rate data. It is necessary to continuously obtain a certain amount of raw data within a certain period of time. For example, the user is required to wear the smart wearable device for 14 days, and within 14 days, 40 groups or more of five-dimensional time-frequency feature values ​​are collected. The feature analysis module 200 determines multiple groups of five-dimensional time-frequency feature values ​​based on the heart rate data obtained within the preset time, and the number of groups of five-dimensional time-frequency feature values ​​is greater than the preset number of groups; if the number of groups of five-dimensional time-frequency feature values ​​is not greater than the preset number of groups, the heart rate data of the preset time is collected again. For example, if the valid data collected within 14 days is insufficient, the data needs to be cleared and the collection is restarted. The purpose of stipulating data collection within the preset time is to ensure that the data is uniformly collected in a short period of time, so that the user's true health status can be reflected through valid data within a period of time. If the span of data collection time is large, the user's own health status may be greatly different before and after, and the collected data cannot truly reflect the user's physical condition at the end of the data collection period. Since there are five kinds of pulse characteristic values, it is necessary to add the data of each pulse characteristic value within the preset time length to find the average value. For example, all the main wave amplitude values ​​within the preset time length are added and then divided by the number of all main wave amplitude values ​​to obtain the main wave amplitude mean corresponding to the main wave amplitude, that is, the pulse characteristic mean corresponding to the main wave amplitude. The calculation formulas for the mean of five-dimensional time-frequency characteristics are: α mean = (the sum of all valid α values ​​within 14 days) / number of valid data groups; β mean = (the sum of all valid β values ​​within 14 days) / number of valid data groups; γ mean = (the sum of all valid γ values ​​within 14 days) / number of valid data groups; δ mean = (the sum of all valid δ values ​​within 14 days) / number of valid data groups; ε mean = (the sum of all valid ε values ​​within 14 days) / number of valid data groups.

[0052] The health judgment module 300 is used to judge whether the multiple pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to a health standard range; and is also used to judge that the user is in good health when the multiple pulse characteristic mean values ​​are all within the corresponding health standard range.

[0053] Specifically, Figure 3 As shown, each pulse characteristic value corresponds to a healthy standard range, and the pulse characteristic mean of each pulse characteristic value corresponds to a healthy standard range. For example, the healthy standard range corresponding to the α mean is Figure 3 The health standard range corresponding to the mean of [α2, α1] and β in the table is Figure 3 The health standard range corresponding to the mean of [β2, β1] and γ in the table is Figure 3 The health standard range corresponding to the mean value of [γ2, γ1] and δ in the table is Figure 3 The health standard range corresponding to the mean value of [δ2, δ1] and ε in the table is Figure 3[ε2, ε1] in the table shown. When the pulse characteristic mean corresponding to each pulse characteristic value obtained by analysis is within the corresponding health standard range, it can be determined that the user's body is healthy at this time. As long as the pulse characteristic mean corresponding to one pulse characteristic value is not within the corresponding health standard range, the user's body state is not healthy, that is, the user's body is in a sub-healthy state at this time.

[0054] The significance analysis module 400 is used to determine the significance of multiple pulse characteristic values ​​according to the age of the user, all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the pre-stored health data samples when there is a pulse characteristic mean value that is not within the corresponding health standard range.

[0055] Specifically, health data samples of healthy people are pre-stored in the database or system memory, and multiple groups of five-dimensional time-frequency feature values ​​of healthy people can be calculated in advance through health data sample analysis. Then, with the multiple groups of five-dimensional time-frequency feature values ​​of healthy people as a reference, it is analyzed whether the multiple groups of five-dimensional time-frequency feature values ​​of the current user are significant relative to the data of the healthy people, where significant means that the pulse feature values ​​of the current user are quite different from the data of the healthy people, indicating that the current user's physical condition may be in a sub-healthy state; insignificant means that the pulse feature values ​​of the current user are similar or close to the data of the healthy people.

[0056] The syndrome analysis module 500 is used to determine the TCM syndrome to which the user's physical health status belongs based on the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndromes and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome.

[0057] Specifically, the "pre-illness" state in traditional Chinese medicine (mainly refers to the human body beginning to have some minor information) is consistent with the sub-health state in modern medicine. Traditional Chinese medicine proposes ten sub-health syndromes, integrating ten common sub-health states of modern people. The traditional Chinese medicine syndromes include 1. liver qi stagnation, 2. liver qi stagnation and spleen deficiency, 3. heart and spleen deficiency, 4. liver and kidney yin deficiency, 5. lung and spleen qi deficiency, 6. spleen deficiency and dampness obstruction, 7. phlegm and heat internal disturbance, 8. heart and kidney disharmony, 9. qi and blood deficiency and 10. dampness and heat accumulation, a total of 10 syndromes. Figure 4 and Figure 5 As shown in the table, the ”, “ " The pulse characteristic value in the row where the pulse characteristic value is located is significant. Therefore, for users aged 39 and below, the syndrome type of liver qi stagnation shows a significant rule that the three characteristic values ​​of β, γ and δ are significant among the five pulse characteristic values ​​(for example, the variance difference significant value corresponding to the β characteristic value is significant). For users aged 50-69, the syndrome type of liver qi stagnation shows a significant rule that the β characteristic value is significant among the five pulse characteristic values ​​(the variance difference significant value and the mean difference significant value corresponding to the β characteristic value are both significant). When the significance of multiple pulse characteristic values ​​is the same as the significance rule corresponding to liver qi stagnation, liver qi stagnation is marked as a pending TCM syndrome type; further judging the pulse characteristic Whether the pulse characteristic mean of the characteristic value is within the characteristic mean range corresponding to each pulse characteristic value corresponding to liver qi stagnation, when it is within the characteristic mean range, it is considered that the user's physical health status presents the TCM syndrome type of liver qi stagnation; if it is not within the characteristic mean range, it is considered that the user's physical health status does not present the TCM syndrome type of liver qi stagnation. After analysis, the user's physical health status can be accurately determined to one or several TCM syndrome types, and the analysis results are refined. Compared with only knowing whether the body is healthy or sub-healthy, the refined analysis results are convenient for users to accurately grasp their own health status, remind users to adjust their living habits or eating habits in time, etc., to avoid deterioration of physical condition.

[0058] In this embodiment, the data processing module 100 determines the user's pulse wave based on the acquired heart rate data of the user. The heart rate data is collected by the smart wearable device worn by the user, and the user's heart rate data can be monitored and collected in real time; the feature analysis module 200 determines a plurality of pulse characteristic values ​​of the user in each pulse wave cycle based on the pulse wave, wherein the plurality of pulse characteristic values ​​include the main wave amplitude, the dicrotic wave amplitude, the beat time ratio, the frequency concentration value and the concentrated frequency amplitude value. Through these five characteristic values, the user's heart rate state can be reflected; the feature analysis module 200 also determines the pulse characteristic mean corresponding to each pulse characteristic value based on all the pulse characteristic values ​​within a preset time length; then the health judgment module 300 judges whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic mean value corresponds to a health standard range. When multiple pulse feature mean values ​​are within the corresponding health standard range, the user is judged to be healthy, and the judgment result can be sent to the smart wearable device worn by the user, so that the user can grasp the personal physical condition in time; however, when there is a pulse feature mean value that is not within the corresponding health standard range, the significant analysis module 400 determines the significance of multiple pulse feature values ​​according to the user's age, all pulse feature values ​​of the user within the preset time and all pulse feature values ​​corresponding to the pre-stored health data sample; and then the syndrome analysis module 500 determines the TCM syndrome to which the user's physical health state belongs according to the user's age, the significance of multiple pulse feature values, the significance rules corresponding to multiple TCM syndromes and the pulse feature mean values ​​corresponding to multiple pulse feature values, wherein the significance rules are the types of pulse feature values ​​corresponding to each TCM syndrome. The user's physical condition is refined into a specific TCM syndrome, and the final analysis result is fed back and presented on the smart wearable device to realize real-time monitoring of the user, which is convenient for promotion and use in daily life. The refined analysis results facilitate the user to accurately grasp his or her own health status, remind the user to adjust his or her living habits or eating habits in time, etc., to avoid deterioration of physical condition.

[0059] Optionally, before the health judgment module judges whether the mean values ​​of the plurality of pulse characteristics are all within the corresponding health standard range, the TCM syndrome type analysis system for health status further includes:

[0060] The health standard determination module 600 is used to determine the health standard range corresponding to the multiple pulse characteristic values ​​according to the age range of the age, wherein in different age ranges, the multiple pulse characteristic values ​​correspond to different health standard ranges.

[0061] Specifically, Figure 3As shown, different age ranges and different pulse characteristic values ​​correspond to different health standard ranges. For example, for the age range of 40-49 years old, the health standard range corresponding to α is Figure 3 The health standard range corresponding to [α4, α3] and β in the table is Figure 3 The health standard range corresponding to [β4, β3] and γ in the table is Figure 3 The health standard range corresponding to [γ4, γ3] and δ in the table is Figure 3 The health standard range corresponding to [δ4, δ3] and ε in the table is Figure 3 [ε4, ε3] in the table shown. Similarly, in other age ranges, the health standard range corresponding to different pulse characteristic values ​​can be obtained by Figure 3 The table shown in .

[0062] Optionally, the significant analysis module 400 includes:

[0063] The health data calling unit is used to call the pre-stored health data samples corresponding to the age range according to the age range of the user, wherein the age is divided into multiple age ranges, and each age range corresponds to a set of pre-stored health data samples. The multiple age ranges are 39 years old and below, 40-49 years old, 50-69 years old, and 70 years old and above. Health data samples can be pre-stored in the system.

[0064] The significant value analysis unit is used to obtain the variance difference significance value, the mean difference first significance value and the mean difference second significance value corresponding to each pulse characteristic value according to all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the pre-stored health data samples, using the independent sample t-test method, wherein the mean difference first significance value is a significant value obtained by analyzing under the assumption of equal variances, and the mean difference second significance value is a significant value obtained by analyzing under the assumption of equal variances.

[0065] Specifically, the independent sample t-test is a statistical method for comparing the difference between the means of two independent sample groups. This test is usually used to determine whether the means of the two data sets have a statistically significant difference. In this embodiment, the user's data is compared with the health data, and the independent sample t-test method is used to obtain the following results: Figure 6In the table shown, the value in the column where significance (P) is located is the significance of the variance difference test, referred to as the variance difference significance value; the value in the column where Sig. (two-tailed) is located is the significance of the mean difference test, referred to as the mean difference significance value, but because in the column where Sig. (two-tailed) is located, each pulse characteristic value corresponds to two values, for example, the two mean difference significance values ​​corresponding to α, one is the first significant value of the mean difference obtained when equal variance is assumed (that is, the variance of the user's data is equal to the variance of the health data); the other is the second significant value of the mean difference obtained when equal variance is not assumed (that is, the variance of the user's data is not equal to the variance of the health data).

[0066] A significance confirmation unit is used to determine the significance of each pulse characteristic value according to the variance difference significance value and the first significance value of the mean difference corresponding to each pulse characteristic value, or according to the variance difference significance value and the second significance value of the mean difference corresponding to each pulse characteristic value.

[0067] In this embodiment, age is segmented because people in different age ranges have different pulse characteristic values ​​due to their physical conditions. Therefore, all the pulse characteristic values ​​of the user within a preset time period are compared with all the pulse characteristic values ​​corresponding to the pre-stored health data samples to determine the significance of each pulse characteristic value. If a pulse characteristic value is significant, it means that the characteristic value of the user has a large deviation from the same characteristic value of a healthy person, which means that the user's body is in a sub-healthy state, and the user can be reminded in time to pay attention to his or her own health.

[0068] Optionally, in the significance confirmation unit, determining the significance of each of the pulse characteristic values ​​according to the variance difference significance value and the mean difference first significance value corresponding to each of the pulse characteristic values, or according to the variance difference significance value and the mean difference second significance value corresponding to each of the pulse characteristic values, comprises:

[0069] It is determined whether the variance difference significance value corresponding to each pulse characteristic value is less than a first significance value. The first significance value may be set to 0.05.

[0070] When the variance difference significant value is less than the first significant value, it means that the variance of the user's data is significantly different from that of the healthy person, and the pulse characteristic value is determined to be significant in terms of variance; it can also be further determined whether the first significant value of the mean difference corresponding to the same pulse characteristic value is less than the second significant value. The second significant value can be set to 0.05.

[0071] When the first significant value of the mean difference is smaller than the second significant value, it means that the mean of the user's data is significantly different from the mean of the healthy person's data, and in terms of the mean, the pulse characteristic value is determined to be significant.

[0072] When the first significant value of the mean difference is greater than or equal to the second significant value, it means that the mean of the user's data is slightly different from the mean of the healthy person's data, and in terms of the mean, it is determined that the pulse characteristic value is not significant.

[0073] When the variance difference significance value is greater than or equal to the first significance value, it means that the user's data variance is slightly different from the healthy person's data variance, and in terms of variance, the pulse characteristic value is determined to be insignificant; further determine whether the second significance value of the mean difference corresponding to the same pulse characteristic value is less than the second significance value.

[0074] When the second significant value of the mean difference is smaller than the second significant value, it means that the mean of the user's data is significantly different from the mean of the healthy person's data, and in terms of the mean, the pulse characteristic value is determined to be significant.

[0075] When the second significant value of the mean difference is greater than or equal to the second significant value, it means that the mean of the user's data is slightly different from the mean of the healthy person's data, and in terms of the mean, it is determined that the pulse characteristic value is not significant.

[0076] In this embodiment, the significance of the pulse characteristic value can be analyzed from the perspective of variance or mean. Some pulse characteristic values ​​only need to show significance in terms of variance to be determined as significant, and some pulse characteristic values ​​only need to show significance in terms of mean to be determined as significant. However, some pulse characteristic values ​​need to show significance in terms of both variance and mean to be determined as significant. Therefore, specific TCM syndrome analysis is required, for example Figure 4 and Figure 5 In the context of data, variability refers to the degree of dispersion or fluctuation of data, which is mainly measured by standard deviation, variance or mean. Figure 6 Sig. (two-tailed) value in, when the variability column has " ", indicating that the mean needs to be significant; mean P refers to the variance, that is, Figure 6 The significance (P) value in the column where the mean P is located has " ", indicating that the variance needs to be significant. For example, Figure 4 and Figure 5In the analysis of whether users aged 50-69 years old have the TCM syndrome of liver qi stagnation, both the mean P and the variability need to be significant, that is, both the variance and the mean are significant, in order to determine whether β is significant; similarly, by Figure 4 and Figure 5 By looking up the table, it can be known whether each pulse characteristic value in each age range needs to satisfy one aspect or both aspects to be significant, thereby knowing the significance of each pulse characteristic value of the user.

[0077] Optionally, the syndrome analysis module 500 includes:

[0078] The rule calling unit is used to call the significance rules of multiple TCM syndrome types corresponding to the age range according to the age range of the user.

[0079] Specifically, by querying Figure 4 and Figure 5 From the table in , we can know the significance rules of each TCM syndrome type corresponding to each age range, that is, the types of pulse characteristic values ​​that show significance corresponding to each TCM syndrome type and whether the pulse characteristic values ​​need to be significant in terms of variance or significant in terms of mean.

[0080] The rule comparison unit is used to determine whether the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance rules of the multiple TCM syndromes called; when the significance of the multiple pulse characteristic values ​​of the user is inconsistent with the significance rules of the multiple TCM syndromes called, it is determined that the user's body is in a sub-healthy state.

[0081] Specifically, the significance law of TCM syndrome types sometimes requires more than one pulse characteristic value to be significant, or requires the pulse characteristic value to be significant in more than one aspect. Therefore, even if a certain pulse characteristic value of the user is significant, when the significance of multiple pulse characteristic values ​​of the user does not match the significance law of the multiple TCM syndrome types called, it means that the user does not show a specific TCM syndrome type, or does not belong to the existing preset multiple TCM syndrome types, but the user's physical condition is indeed in a sub-healthy state.

[0082] The syndrome confirmation unit is used to record the consistent TCM syndrome as a pending TCM syndrome when the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance law of at least one of the multiple TCM syndromes called, and determine the TCM syndrome to which the user's physical health status belongs based on the age of the user, the characteristic mean range of the multiple pulse characteristic values ​​corresponding to the pending TCM syndrome and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values.

[0083] Specifically, when the significance of the user's pulse characteristics is analyzed to be consistent with the significance law of a certain TCM syndrome type, it can be temporarily determined that the user's physical condition may belong to this TCM syndrome type, and further analysis and judgment based on the pulse characteristic mean is required. After analyzing the health and sub-health status, in order to improve the accuracy of the analysis results, further analysis is performed in combination with the pulse characteristic mean to determine the TCM syndrome type to which the user's physical condition belongs.

[0084] Optionally, in the syndrome confirmation unit, determining the TCM syndrome to which the user's physical health status belongs based on the user's age, the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the pending TCM syndrome, and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values ​​comprises:

[0085] According to the age range of the user, the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the plurality of TCM syndrome types corresponding to the age range is called.

[0086] Specifically, Figure 7 The characteristic mean ranges of multiple pulse characteristic values ​​corresponding to two TCM syndromes for the age group of 39 years and below are shown. For example, for the age group of 39 years and below, the characteristic mean range of α corresponding to liver qi stagnation is [A2, A1]. Similarly, the characteristic mean range of each pulse characteristic value corresponding to each TCM syndrome corresponding to other age ranges can also be pre-stored, and the characteristic mean range can be obtained by calling.

[0087] Determine whether the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values ​​are within the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the undetermined TCM syndrome type.

[0088] When the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values ​​are all within the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the pending TCM syndrome type, the pending TCM syndrome type is determined to be the TCM syndrome type to which the user's physical health status belongs.

[0089] When there is a pulse characteristic value corresponding to the pulse characteristic mean that is not within the characteristic mean range of the same pulse characteristic value corresponding to the pending TCM syndrome type, it is determined that the user's physical health status does not belong to the pending TCM syndrome type.

[0090] In this embodiment, further analysis is performed in combination with the pulse characteristic mean value, because if the pulse characteristic mean value is within the characteristic mean value range, it means that most pulse characteristic values ​​are within a range (that is, if the user shows a certain TCM syndrome, the range of fluctuation of the pulse characteristic value). This means that the user's physical condition is in the same state for a long time, or fluctuates around this state, which further confirms the user's TCM syndrome and improves the accuracy of the analysis.

[0091] Optionally, in the syndrome confirmation unit, after determining whether the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome, the syndrome confirmation unit is further configured to determine that the user is in a sub-healthy state when the user's health status does not belong to all of the pending TCM syndromes. When there are a plurality of TCM syndromes to which the user's health status belongs, all of the a plurality of TCM syndromes to which the health status belongs are output as analysis results.

[0092] Specifically, there are many types of TCM syndromes, and the proportion of people suffering from each TCM syndrome is different. The TCM syndromes with a larger proportion are taken as the focus of this analysis, while the TCM syndromes with a relatively smaller proportion are not over-analyzed and are uniformly classified as sub-healthy states to reduce the amount of analysis calculations and improve analysis efficiency. The same user may not only show one TCM syndrome, but may have multiple TCM syndromes at the same time. Therefore, through the significance analysis and pulse feature mean analysis, as long as the user meets the TCM syndrome, it will be output as the analysis result and sent to the smart wearable device to ensure the comprehensiveness of the analysis.

[0093] Optionally, in the data processing module 100, determining, based on the pulse wave, a plurality of pulse characteristic values ​​of the user in each pulse wave cycle comprises:

[0094] The pulse wave is analyzed in the time domain to determine the main wave amplitude, the dicrotic wave amplitude and the beat time ratio.

[0095] Perform frequency domain analysis on the pulse wave to determine the frequency concentration value and the concentration frequency amplitude value.

[0096] Specifically, time domain analysis refers to the feature extraction of pulse waves in time series. Through time domain analysis, the characteristics of pulse waves such as amplitude, period, rise time, fall time, main wave amplitude, dicrotic wave amplitude and beat time ratio can be obtained. These parameters can reflect the physiological state of the heart's contraction and relaxation function, blood vessel elasticity and resistance, etc. Frequency domain analysis refers to the feature extraction of pulse waves in the frequency domain. Through frequency domain analysis, the calculation of pulse wave spectrum, extraction of frequency components, frequency distribution of pulse waves, main frequency concentration value and concentrated frequency amplitude value, etc. can be obtained. These frequency distributions can reflect the physiological state of the heart and blood vessels.

[0097] In order to verify the feasibility of this system, 60 respondents of different ages were randomly selected. The respondents were asked to fill in the Ten Syndrome Types Scale (i.e., questionnaire) and then the results of the scale were confirmed by combining the TCM random inspection and consultation. Figure 8 As shown, the respondents filled in the corresponding scores according to the questions and option scoring instructions in the questionnaire. Since different TCM syndrome types are related to different questions, the following data statistical analysis is required.

[0098] Syndrome score = the sum of all the questions associated with the syndrome. Syndrome severity score = 2.5 Syndrome score / number of questions associated with syndrome, keep three decimal places. Syndrome severity level judgment: The relationship between syndrome severity score and severity level is: [0, 1) corresponds to level 1, [1, 2) corresponds to level 2, [2, 3) corresponds to level 3, [3, 4) corresponds to level 4, [4, 5) corresponds to level 5, [5, 6) corresponds to level 6, [6, 7) corresponds to level 7, [7, 8) corresponds to level 8, [8, 9) corresponds to level 9, [9, 10] corresponds to level 10. Sub-health status judgment: If all syndrome severity scores are less than 2, it is judged as a healthy state, otherwise it is judged as a sub-healthy state. Sub-health status main TCM syndrome judgment: The syndrome severity score is not less than 2 and the two syndromes with the highest and second highest scores are judged as the main syndrome. If the third highest value is equal to the second highest value, it is also judged as the main syndrome. In this way, the true health status of all respondents can be known.

[0099] By collecting the physiological data (heart rate data) of the respondents through smart wearable devices, the above system is used to analyze and process the heart rate data of the respondents, and the results of the analysis (i.e., the algorithm results) are compared with the results obtained through questionnaire analysis (i.e., scale measurement results), the accuracy of the analysis results of the above system can be obtained. Fig. 9 Part of the verification results are randomly displayed. Based on the comprehensive experimental verification results, the analysis confirmation rate (i.e. accuracy) is 86.11%, reaching more than 85%, indicating that the system has high feasibility in the analysis of individual TCM syndromes and has high practical application value.

[0100] In this embodiment, the system can be built in electronic equipment, processing equipment, computers or servers, etc. The system can be connected to the smart wearable device through wireless to achieve data transmission and interaction, or the system can be directly integrated into the smart wearable device. The smart wearable device can be a head-mounted wearable device or a wristwatch, etc., which is used to collect physiological data of the monitored object and transmit the collected physiological data to the analysis system through a wireless network. The analysis system can also feed back the execution results to the smart wearable device to remind the wearer to pay attention to his or her health status.

[0101] The TCM syndrome analysis system for health status of the present application can realize instant acquisition of one's own health status by having the user wear a smart wearable device and use PPG technology to collect relevant physiological data. Compared with the need to use a specific scale for testing, it is more convenient to reflect the user's immediate status and realize long-term monitoring at any time. The user's relevant physiological data is obtained by the user wearing a smart wearable device. The smart wearable device is easy to carry. Compared with the method that requires offline consultation with a doctor, the application scenario can be daily, with strong universality and a wider range of application groups.

[0102] Optionally, an electronic device provided by an embodiment of the present invention includes a memory and a processor; the memory is used to store a computer program; and the processor is used to implement the following steps when executing the computer program:

[0103] Determining the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user;

[0104] Determine multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave, wherein the multiple pulse characteristic values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value;

[0105] According to all the pulse characteristic values ​​within a preset time period, determining a pulse characteristic mean corresponding to each pulse characteristic value;

[0106] Determine whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range;

[0107] When the average values ​​of the plurality of pulse characteristics are within the corresponding health standard range, it is determined that the user is in good health;

[0108] When there is a pulse characteristic mean value that is not within the corresponding health standard range, determining the significance of multiple pulse characteristic values ​​according to the age of the user, all the pulse characteristic values ​​of the user within a preset time period, and all the pulse characteristic values ​​corresponding to the pre-stored health data samples;

[0109] The TCM syndrome type to which the user's physical health status belongs is determined based on the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndrome types and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome type.

[0110] In this embodiment, the electronic device may be a processing device, a computer, a server, etc. Fig.10 As shown, the electronic device can be wirelessly connected with the smart wearable device to achieve data transmission and interaction. The smart wearable device transmits the collected physiological data to the electronic device through a wireless network, and the electronic device can also feed back the execution results to the smart wearable device to remind the wearer to pay attention to his or her own health status. In addition, the electronic device can also be connected to a mobile terminal, which can be a mobile phone, tablet computer or other device of the monitored object or the manager of the monitored object. The smart wearable device and the mobile terminal can also be connected through a network. The mobile terminal is connected through a wireless network to receive the processing results of the electronic device, that is, to receive the five-dimensional time-frequency characteristic values ​​and the assessed health status obtained after the electronic device processes the physiological data. After receiving the feedback data, the corresponding prompt operation is performed so that the monitored object or its manager can grasp their own health status in real time.

[0111] An embodiment of the present invention provides a computer-readable storage medium, wherein a computer program is stored on the storage medium. When the computer program is executed by a processor, the following steps are implemented:

[0112] Determining the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user;

[0113] Determine multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave, wherein the multiple pulse characteristic values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value;

[0114] According to all the pulse characteristic values ​​within a preset time period, determining a pulse characteristic mean corresponding to each pulse characteristic value;

[0115] Determine whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range;

[0116] When the average values ​​of the plurality of pulse characteristics are within the corresponding health standard range, it is determined that the user is in good health;

[0117] When there is a pulse characteristic mean value that is not within the corresponding health standard range, determining the significance of multiple pulse characteristic values ​​according to the age of the user, all the pulse characteristic values ​​of the user within a preset time period, and all the pulse characteristic values ​​corresponding to the pre-stored health data samples;

[0118] The TCM syndrome type to which the user's physical health status belongs is determined based on the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndrome types and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome type.

[0119] An electronic device that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described and / or required herein.

[0120] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0121] A person of ordinary skill in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiment system can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0122] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A TCM syndrome analysis system for health status, characterized in that: include: A data processing module, used to determine the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user; A feature analysis module, for determining, based on the pulse wave, a plurality of pulse feature values ​​of the user in each pulse wave cycle, wherein the plurality of pulse feature values ​​include a main wave amplitude, a dicrotic wave amplitude, a beat time ratio, a frequency concentration value, and a concentrated frequency amplitude value; and for determining, based on all the pulse feature values ​​within a preset time period, a pulse feature mean value corresponding to each pulse feature value; a health judgment module, used to judge whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range; and also used to judge that the user is in good health when the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range; A significance analysis module, for determining the significance of a plurality of pulse characteristic values ​​according to the age of the user, all the pulse characteristic values ​​of the user within a preset time period, and all the pulse characteristic values ​​corresponding to pre-stored health data samples when there is a pulse characteristic mean value that is not within the corresponding health standard range; a syndrome analysis module, for determining the TCM syndrome to which the user's physical health status belongs according to the user's age, the significance of the multiple pulse characteristic values, the significance rules corresponding to the multiple TCM syndromes, and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values, wherein the significance rule is the type of the pulse characteristic value that presents significance corresponding to each TCM syndrome; The significant analysis module includes: A health data calling unit, configured to call the pre-stored health data samples corresponding to the age range according to the age range of the user, wherein the age is divided into a plurality of age ranges, and each age range corresponds to a set of pre-stored health data samples; A significant value analysis unit, for obtaining, based on all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the called health data sample, a variance difference significant value, a mean difference first significant value and a mean difference second significant value corresponding to each of the pulse characteristic values ​​by using an independent sample t-test method, wherein the mean difference first significant value is a significant value obtained by analyzing under the assumption of equal variances, and the mean difference second significant value is a significant value obtained by analyzing under the assumption of not assuming equal variances; A significance confirmation unit, used for determining the significance of each of the pulse characteristic values ​​according to the variance difference significance value and the mean difference first significance value corresponding to each of the pulse characteristic values, or according to the variance difference significance value and the mean difference second significance value corresponding to each of the pulse characteristic values; The syndrome analysis module comprises: A rule calling unit, used for calling, according to the age range of the user, the significant rules of the plurality of TCM syndrome types corresponding to the age range; a rule comparison unit, used to judge whether the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance rules of the multiple TCM syndromes called; when the significance of the multiple pulse characteristic values ​​of the user is inconsistent with the significance rules of the multiple TCM syndromes called, it is judged that the user's body is in a sub-healthy state; a syndrome confirmation unit, for recording the consistent TCM syndrome as a pending TCM syndrome when the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance rule of at least one of the multiple TCM syndromes called, and determining the TCM syndrome to which the user's physical health status belongs according to the age of the user, the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the pending TCM syndrome, and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values; Determining the TCM syndrome type to which the user's physical health status belongs based on the user's age, the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the undetermined TCM syndrome type, and the pulse characteristic mean values ​​corresponding to the multiple pulse characteristic values ​​includes: According to the age range of the user, calling the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the plurality of TCM syndrome types corresponding to the age range; Determine whether the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome type; When the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are all within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome type, determining that the pending TCM syndrome type is the TCM syndrome type to which the user's physical health status belongs; When there is a pulse characteristic value corresponding to the pulse characteristic mean that is not within the characteristic mean range of the same pulse characteristic value corresponding to the pending TCM syndrome type, it is determined that the user's physical health status does not belong to the pending TCM syndrome type.

2. The TCM syndrome analysis system for health status according to claim 1, characterized in that: Determining the significance of each of the pulse characteristic values ​​according to the variance difference significance value and the mean difference first significance value corresponding to each of the pulse characteristic values, or according to the variance difference significance value and the mean difference second significance value corresponding to each of the pulse characteristic values, comprises: Determine whether the variance difference significance value corresponding to each pulse characteristic value is less than the first significance value; When the variance difference significant value is smaller than the first significant value, the pulse characteristic value is determined to be significant in terms of variance, and whether the first significant value of the mean difference corresponding to the same pulse characteristic value is smaller than the second significant value; When the first significant value of the mean difference is smaller than the second significant value, determining that the pulse characteristic value is significant in terms of the mean; When the first significant value of the mean difference is greater than or equal to the second significant value, determining that the pulse characteristic value is not significant; When the variance difference significant value is greater than or equal to the first significant value, determining that the pulse characteristic value is not significant in terms of variance, and determining whether the mean difference second significant value corresponding to the same pulse characteristic value is less than the second significant value; When the second significant value of the mean difference is less than the second significant value, determining that the pulse characteristic value is significant in terms of the mean; When the second significant value of the mean difference is greater than or equal to the second significant value, it is determined that the pulse characteristic value is not significant.

3. The TCM syndrome analysis system for health status according to claim 1, characterized in that: After determining whether the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome type, the syndrome type confirmation unit is further used to: When the user's physical health condition does not belong to all of the pending TCM syndrome types, it is determined that the user's body is in a sub-healthy state; When there are multiple TCM syndrome types to which the user's physical health status belongs, all of the multiple TCM syndrome types to which the physical health status belongs are output as analysis results.

4. The TCM syndrome analysis system for health status according to claim 1, characterized in that: The feature analysis module is also used to determine multiple groups of five-dimensional time-frequency feature values ​​based on the heart rate data obtained within a preset time length, and the number of groups of five-dimensional time-frequency feature values ​​is greater than the preset number of groups; if the number of groups of five-dimensional time-frequency feature values ​​is not greater than the preset number of groups, the heart rate data of the preset time length is collected again.

5. The TCM syndrome analysis system for health status according to claim 1, characterized in that: Also includes: The health standard determination module is used to determine the health standard range corresponding to multiple pulse characteristic values ​​according to the age range of the age, wherein in different age ranges, multiple pulse characteristic values ​​correspond to different health standard ranges.

6. The TCM syndrome analysis system for health status according to claim 1, characterized in that: The TCM syndromes include liver qi stagnation, liver depression and spleen deficiency, heart and spleen deficiency, liver and kidney yin deficiency, lung and spleen qi deficiency, spleen deficiency and dampness obstruction, internal disturbance of phlegm and heat, heart and kidney disharmony, qi and blood deficiency and dampness and heat accumulation.

7. The TCM syndrome analysis system for health status according to claim 1, characterized in that: Determining the multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave includes: Performing time domain analysis on the pulse wave to determine the main wave amplitude, the dicrotic wave amplitude and the beat time ratio; Perform frequency domain analysis on the pulse wave to determine the frequency concentration value and the concentration frequency amplitude value.

8. An electronic device, characterized in that: The invention comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement the following steps when executing the computer program: Determining the pulse wave of the user according to the acquired heart rate data of the user, wherein the heart rate data is collected by a smart wearable device worn by the user; Determine multiple pulse characteristic values ​​of the user in each pulse wave cycle according to the pulse wave, wherein the multiple pulse characteristic values ​​include main wave amplitude, dicrotic wave amplitude, beat time ratio, frequency concentration value and concentrated frequency amplitude value; According to all the pulse characteristic values ​​within a preset time period, determining a pulse characteristic mean corresponding to each pulse characteristic value; Determine whether the plurality of pulse characteristic mean values ​​are all within the corresponding health standard range, wherein each pulse characteristic value corresponds to one health standard range; When the average values ​​of the plurality of pulse characteristics are within the corresponding health standard range, it is determined that the user is in good health; When there is a pulse characteristic mean value that is not within the corresponding health standard range, calling the pre-stored health data sample corresponding to the age range according to the age range of the user, wherein the age is divided into a plurality of age ranges, and each age range corresponds to a set of pre-stored health data samples; According to all the pulse characteristic values ​​of the user within a preset time period and all the pulse characteristic values ​​corresponding to the called health data sample, an independent sample t-test method is used to obtain a variance difference significance value, a mean difference first significance value and a mean difference second significance value corresponding to each pulse characteristic value, wherein the mean difference first significance value is a significance value obtained by analyzing under the assumption of equal variance, and the mean difference second significance value is a significance value obtained by analyzing under the assumption of not assuming equal variance; Determine the significance of each of the pulse characteristic values ​​according to the variance difference significance value and the mean difference first significance value corresponding to each of the pulse characteristic values, or according to the variance difference significance value and the mean difference second significance value corresponding to each of the pulse characteristic values; According to the age range of the user, calling the significance rules of multiple TCM syndromes corresponding to the age range, wherein the significance rules are the types of the pulse characteristic values ​​that are significant corresponding to each TCM syndrome; Determine whether the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance rules of the multiple TCM syndromes called; when the significance of the multiple pulse characteristic values ​​of the user is inconsistent with the significance rules of the multiple TCM syndromes called, determine that the user is in a sub-healthy state; When the significance of the multiple pulse characteristic values ​​of the user is consistent with the significance rule of at least one of the multiple TCM syndrome types called, the consistent TCM syndrome type is recorded as a pending TCM syndrome type, and according to the age range of the user, the characteristic mean value range of the multiple pulse characteristic values ​​corresponding to the multiple TCM syndrome types corresponding to the age range is called; Determine whether the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome type; When the pulse characteristic mean values ​​corresponding to the plurality of pulse characteristic values ​​are all within the characteristic mean value range of the plurality of pulse characteristic values ​​corresponding to the pending TCM syndrome type, determining that the pending TCM syndrome type is the TCM syndrome type to which the user's physical health status belongs; When there is a pulse characteristic value corresponding to the pulse characteristic mean that is not within the characteristic mean range of the same pulse characteristic value corresponding to the pending TCM syndrome type, it is determined that the user's physical health status does not belong to the pending TCM syndrome type.

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