Data processing method for intelligent health system

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method for an intelligent health system, which comprises the following steps: acquiring health monitoring data; acquiring a normal period and an abnormal period in the health monitoring data curve according to the health monitoring data; according to the abnormal period in the health monitoring data curve, the abnormal period with the long-term persistence characteristic in the health monitoring data curve and the abnormal period without the long-term persistence characteristic in the health monitoring data curve are obtained, and a new health monitoring data curve is obtained in combination with the normal period in the health monitoring data curve; and performing compression processing on the new health monitoring data curve. According to the method, the redundancy of the health monitoring data with low importance degree is increased, and the health monitoring data with high importance degree is reserved, so that the final compression result can ensure that the compressed data can reflect the health condition of a user while improving the health monitoring data compression effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a data processing method for a smart big health system. Background Art

[0002] The smart big health system can help people detect potential health problems at an early stage. Through regular health monitoring, individuals can better understand their physical conditions and thus take timely intervention measures. Currently, health monitoring devices are often used in the smart big health system for human health monitoring; they can provide real-time health data. For example, by measuring and monitoring the heart rate, symptoms of arrhythmia can be detected in a timely manner, the body's stress level can be evaluated, and corresponding measures can be taken in time to relieve stress and reduce anxiety, thereby promoting physical health. Since heart rate data is a very important indicator in health monitoring, more health monitoring devices use HRV (Heart Rate Variability), that is, heart rate variability index data, to achieve health monitoring; HRV refers to the change amplitude and frequency of the spontaneous beating rhythm of the heart, which can reflect the functional state of the human autonomic nervous system; by monitoring HRV, the relative activity levels of these two nervous systems can be understood, thereby evaluating one's own stress level, emotional state, and overall physical condition. HRV is closely related to cardiovascular health. Abnormal HRV monitoring may be correlated with an increased risk of heart disease, so it can also be used to evaluate cardiovascular health.

[0003] However, since HRV monitoring collects high-frequency data in real time through devices, a large amount of data information points will be generated during this collection process. Usually, data compression methods are used to process this type of data during monitoring and processing. Although the HRV curve has a certain periodic similarity, due to various factors such as individual differences, physiological conditions, and environmental factors, there are certain differences in the fluctuation characteristics of each cycle; therefore, when using traditional run-length encoding to compress this type of data, problems such as low compression efficiency and large storage space occupation will still occur; and if lossy compression is performed periodically, it will lead to the situation where abnormal HRV monitoring data cannot be effectively identified; resulting in an unsatisfactory final monitoring and storage effect. Summary of the Invention

[0004] The present invention provides a data processing method for a smart big health system to solve the existing problems: it cannot take into account both improving the compression effect of health monitoring data and ensuring that the compressed data can reflect the user's health condition.

[0005] The data processing method for a smart big health system of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a data processing method for a smart big health system, and the method includes the following steps:

[0007] Collect health monitoring data; obtain each cycle in the health monitoring data curve, the data within each cycle in the health monitoring data, and the time span of each cycle of the health monitoring data according to the health monitoring data; obtain the first abnormal degree of each cycle in the health monitoring data curve according to the data within each cycle in the health monitoring data and the time span of each cycle of the health monitoring data; obtain the second abnormal degree of each cycle in the health monitoring data curve according to the inflection points within each cycle in the health monitoring data curve; obtain the abnormal degree of each cycle in the health monitoring data curve according to the first abnormal degree and the second abnormal degree of each cycle in the health monitoring data curve; divide all cycles in the health monitoring data curve into two categories: abnormal cycles and normal cycles according to the abnormal degree of each cycle in the health monitoring data curve;

[0008] Obtain the time nodes of all abnormal cycles in the health monitoring data curve; obtain the abnormal cycles with long-term persistence characteristics and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve according to the time nodes of all abnormal cycles in the health monitoring data curve;

[0009] Obtain a new health monitoring data curve according to the normal cycles in the health monitoring data curve, the abnormal cycles with long-term persistence characteristics in the health monitoring data curve, and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve, and perform compression processing on the new health monitoring data curve.

[0010] Preferably, the steps of collecting health monitoring data; obtaining each cycle in the health monitoring data curve, the data within each cycle in the health monitoring data, and the time span of each cycle of the health monitoring data include the following specific methods:

[0011] Let the user collect health monitoring data through a smart bracelet in a state of keeping the body relaxed, and import the collected health monitoring data into Kubios HRV software through the health monitoring data to obtain the health monitoring data curve, each cycle in the health monitoring data curve, the data within each cycle of the health monitoring data, and the time span of each cycle of the health monitoring data.

[0012] Preferably, the specific calculation formula for obtaining the first abnormal degree of each cycle in the health monitoring data curve is as follows:

[0013]

[0014] In the formula, Represents the first degree of abnormality in the \(i\)-th cycle of the health monitoring data curve; \(F\) i Represents the difference between the maximum and minimum values in the \(i\)-th cycle of the health monitoring data curve; \(N\) represents the number of cycles in the health monitoring data curve; \(F\) j Represents the difference between the maximum and minimum values in the \(j\)-th cycle of the health monitoring data curve; \(T\) i Represents the time span of the \(i\)-th cycle of the health monitoring data curve; \(T\) j Represents the time span of the \(j\)-th cycle of the health monitoring data curve; \(Norm()\) represents the linear normalization function; \(exp()\) represents the exponential function with the natural constant as the base; \(||\) represents the absolute value operation.

[0015] Preferably, the method for obtaining the second degree of abnormality in each cycle of the health monitoring data curve according to the inflection points in each cycle of the health monitoring data curve includes the following specific steps:

[0016] Divide each cycle of the health monitoring data curve into several segments according to the inflection points in each cycle of the health monitoring data curve to obtain the sub-segments of each cycle of the health monitoring data curve. Then, according to the number of inflection points in each cycle of the health monitoring data curve and the cycle sub-segments of the health monitoring data curve, obtain the second degree of abnormality in each cycle of the health monitoring data curve. The specific calculation formula is:

[0017]

[0018] In the formula, Represents the second degree of abnormality in the \(i\)-th cycle of the health monitoring data curve; \(M\) i Represents the number of inflection points in the \(i\)-th cycle of the health monitoring data curve; \(N\) represents the number of cycles in the health monitoring data curve; \(M\) j Represents the number of inflection points in the \(j\)-th cycle of the health monitoring data curve; \(K\) di,i Represents the mean value of the slopes of all data on the \(d_i\)-th sub-segment in the \(i\)-th cycle of the health monitoring data curve; \(D\) i Represents the number of sub-segments in the \(i\)-th cycle of the health monitoring data curve; \(K\) dj,j Represents the mean value of the slopes of all data on the \(d_j\)-th sub-segment in the \(j\)-th cycle of the health monitoring data curve; \(D\) j Represents the number of sub-segments in the \(j\)-th cycle of the health monitoring data curve; \(exp()\) represents the exponential function with the natural constant as the base; \(A\) 1 And \(A\) 2 Are preset weights; \(||\) represents the absolute value operation.

[0019] Preferably, the specific calculation formula for obtaining the degree of abnormality in each cycle of the health monitoring data curve is:

[0020]

[0021] In the formula, σ i represents the degree of abnormality of the i-th cycle in the health monitoring data curve; represents the first degree of abnormality of the i-th cycle in the health monitoring data curve; represents the second degree of abnormality of the i-th cycle in the health monitoring data curve; μ 1 and μ 2 are preset weights.

[0022] Preferably, according to the degree of abnormality of each cycle in the health monitoring data curve, all cycles in the health monitoring data curve are divided into two categories: abnormal cycles and normal cycles. The specific method includes:

[0023] Preset an abnormality degree threshold τ. When the degree of abnormality of a cycle in the health monitoring data curve is greater than or equal to τ, the cycle in the health monitoring data curve is an abnormal cycle. When the degree of abnormality of a cycle in the health monitoring data curve is less than τ, the cycle in the health monitoring data curve is a normal cycle.

[0024] Preferably, the method for obtaining the time nodes of all abnormal cycles in the health monitoring data curve includes the following specific method:

[0025] For the o-th abnormal cycle in the health monitoring data curve, select the moment when the first data point in the o-th abnormal cycle is recorded as the time node of the o-th abnormal cycle, and obtain the time nodes of all abnormal cycles in the health monitoring data curve.

[0026] Preferably, the method for obtaining the abnormal cycles with long-term persistence characteristics and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve includes the following specific method:

[0027] For the o-th abnormal cycle in the health monitoring data curve, combine the o-th abnormal cycle with each other abnormal cycle to obtain a number of abnormal cycle pairs of the o-th abnormal cycle. Combine each abnormal cycle pair with each other abnormal cycle pair to obtain a number of abnormal cycle pair groups of the o-th abnormal cycle; take the absolute value of the time node difference between the two abnormal cycles in each abnormal cycle pair as the time difference value of each abnormal cycle pair; take the ratio of the smaller time difference value to the larger time difference value in the two abnormal cycle pairs of the abnormal cycle pair group as the time difference similarity of each abnormal cycle pair group; finally, preset a threshold ξ. When the ratio of the time difference similarity of any abnormal cycle pair group is greater than or equal to ξ, the o-th abnormal cycle has long-term persistence characteristics. When the ratio of the time difference similarity of all abnormal cycle pair groups is less than ξ, the o-th abnormal cycle does not have long-term persistence characteristics.

[0028] Preferably, the method for obtaining a new health monitoring data curve and compressing the new health monitoring data curve specifically includes the following steps:

[0029] Perform mean fitting on all normal cycles in the health monitoring data curve to obtain the fitting results of all normal cycles in the health monitoring data curve; replace all normal cycles and all abnormal cycles without long-term persistence characteristics in the health monitoring data curve with the fitting results of all normal cycles in the health monitoring data curve to obtain a new health monitoring data curve, and finally compress the data corresponding to the new health monitoring data curve using run-length encoding.

[0030] The beneficial effect of the technical solution of the present invention is that when traditional data compression methods compress health monitoring data, they cannot balance improving the compression effect of health monitoring data while ensuring that the compressed data can reflect the user's health condition; while the present invention classifies health monitoring data according to its importance level, obtaining health monitoring data with a high importance level and health monitoring data with a low importance level, increasing the redundancy of health monitoring data with a low importance level and retaining health monitoring data with a high importance level to obtain new health monitoring data, and then compressing the new health monitoring data to achieve the final compression result that can balance improving the compression effect of health monitoring data while ensuring that the compressed data can reflect the user's health condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a flowchart of the steps of the data processing method for the intelligent big health system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the data processing method for the intelligent big health system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0035] The following specifically describes the specific solution of the data processing method provided by the present invention for the intelligent big health system in conjunction with the accompanying drawings.

[0036] Please refer to Figure 1 , which shows the flowchart of the steps of the data processing method provided by an embodiment of the present invention for the intelligent big health system. The method includes the following steps:

[0037] Step S001: Collect health monitoring data.

[0038] It should be noted that the health monitoring data in this embodiment is HRV data. Since HRV data is high-frequency data collected in real time by devices, a large amount of data information points will be generated during this collection process. Therefore, data compression is usually used to process the health monitoring data. Although the health monitoring data has a certain degree of periodic similarity, due to factors such as the user's health condition and psychological condition, there are certain differences in the data of different periods. Therefore, if traditional run-length encoding is directly used to compress the health monitoring data, a good compression effect cannot be obtained. And if the health monitoring data is simply lossily compressed, some information will be lost in the lossily compressed health monitoring data, resulting in the lossily compressed health monitoring data being unable to accurately reflect the user's health condition. Therefore, this embodiment proposes a data compression method that can improve the compression effect of health monitoring data while ensuring that the compressed data can reflect the user's health condition. Therefore, it is first necessary to collect health monitoring data.

[0039] Specifically, let the user collect health monitoring data through a smart bracelet while keeping the body relaxed.

[0040] Thus, the health monitoring data is obtained.

[0041] Step S002: Obtain a health monitoring data curve based on the health monitoring data, classify the periods in the health monitoring data curve, and obtain normal periods and abnormal periods in the health monitoring data curve.

[0042] It should be noted that since run - length encoding data compression only has a good compression effect on continuous and exactly the same data segments, and the data within each cycle in health monitoring data is not exactly the same, run - length encoding data compression cannot compress health monitoring data well. However, the importance of normal data in health monitoring data is much lower than that of abnormal data. Therefore, lossy compression can be performed on the normal data in health monitoring data, while the abnormal data is completely retained, so as to improve the compression effect of health monitoring data and ensure that the compressed data can reflect the user's health condition. Therefore, it is first necessary to obtain the degree of abnormality of each cycle in the health monitoring data curve.

[0043] Specifically, first import the collected health monitoring data into Kubios HRV software to obtain the health monitoring data curve, each cycle in the health monitoring data curve, the data within each cycle of the health monitoring data, and the time span of each cycle of the health monitoring data.

[0044] It should be noted that the abscissa of the health monitoring data curve is the time when the health monitoring data is collected, and the ordinate is the collected health monitoring data.

[0045] Then, according to the data within each cycle in the health monitoring data curve, obtain the first degree of abnormality of each cycle in the health monitoring data curve. Its specific calculation formula is:

[0046]

[0047] In the formula, represents the first degree of abnormality of the i - th cycle in the health monitoring data curve; F i represents the difference between the maximum value and the minimum value in the i - th cycle of the health monitoring data curve; N represents the number of cycles in the health monitoring data curve; F j represents the difference between the maximum value and the minimum value in the j - th cycle of the health monitoring data curve; T i represents the time span of the i - th cycle in the health monitoring data curve; T j represents the time span of the j - th cycle in the health monitoring data curve; Norm() represents the linear normalization function; exp() represents the exponential function with the natural constant as the base; || represents the absolute value operation.

[0048] It should be further noted that represents the degree of difference between the i - th cycle in the health monitoring data curve and all cycles in the health monitoring data curve in terms of health data. Therefore, the larger the value of, the more abnormal the i - th cycle in the health monitoring data curve; It represents the degree of difference in the time span between the $i$-th cycle in the health monitoring data curve and all cycles in the health monitoring data curve; The larger the value of , the more abnormal the $i$-th cycle in the health monitoring data curve; therefore, the obtained

[0049] Thus, the first degree of abnormality of each cycle in the health monitoring data curve is obtained.

[0050] Then, according to the inflection points in each cycle of the health monitoring data curve, each cycle in the health monitoring data curve is divided into several segments to obtain the sub-segments of each cycle in the health monitoring data curve. According to the number of inflection points in each cycle of the health monitoring data curve and the cycle sub-segments in the health monitoring data curve, the second degree of abnormality of each cycle in the health monitoring data curve is obtained. The specific calculation formula is as follows:

[0051]

[0052] In the formula, represents the second degree of abnormality of the $i$-th cycle in the health monitoring data curve; $M$ i represents the number of inflection points in the $i$-th cycle in the health monitoring data curve; $N$ represents the number of cycles in the health monitoring data curve; $M$ j represents the number of inflection points in the $j$-th cycle in the health monitoring data curve; $K$ di,i represents the mean value of the slopes of all data on the $d_i$-th sub-segment within the $i$-th cycle in the health monitoring data curve; $D$ i represents the number of sub-segments within the $i$-th cycle in the health monitoring data curve; $K$ dj,j represents the mean value of the slopes of all data on the $d_j$-th sub-segment within the $j$-th cycle in the health monitoring data curve; $D$ j represents the number of sub-segments within the $j$-th cycle in the health monitoring data curve; $\exp()$ represents the exponential function with the natural constant as the base; $A$ 1 and $A$ 2 are preset weights. The magnitudes of $A$ 1 and $A$ 2 can be set according to the actual situation. There is no rigid requirement in this embodiment. In this embodiment, $A$ 1 and $A$ 2 are respectively equal to 0.6 and 0.4 for description; $||$ represents the absolute value operation.

[0053] It should be further noted that represents the difference between the number of inflection points in the $i$-th cycle in the health monitoring data curve and the number of inflection points in all cycles in the health monitoring data curve. The larger the value, the more abnormal the i-th cycle in the health monitoring data curve; represents the difference between the sub-segment in the i-th cycle of the health monitoring data curve and the sub-segments in all cycles of the health monitoring data curve, The larger the value, so the obtained is larger, indicating that the i-th cycle in the health monitoring data curve is more abnormal.

[0054] Thus far, the second abnormal degree of each cycle in the health monitoring data curve is obtained.

[0055] Next, according to the first abnormal degree of each cycle in the health monitoring data curve and the second abnormal degree of each cycle in the health monitoring data curve, obtain the abnormal degree of each cycle in the health monitoring data curve. The specific calculation formula is:

[0056]

[0057] In the formula, σ i represents the abnormal degree of the i-th cycle in the health monitoring data curve; represents the first abnormal degree of the i-th cycle in the health monitoring data curve; represents the second abnormal degree of the i-th cycle in the health monitoring data curve; μ 1 and μ 2 are preset weights. The magnitudes of μ 1 and μ 2 can be set according to the actual situation. There is no strict requirement in this embodiment. In this embodiment, μ 1 and μ 2 are both equal to 0.5 for calculation.

[0058] It should be further noted that the larger the value of σ i , the more abnormal the i-th cycle in the health monitoring data curve. Finally, according to the abnormal degree of each cycle in the health monitoring data curve, each cycle in the health monitoring data curve is divided into two categories: abnormal cycles and normal cycles.

[0059] Specifically, preset an abnormal degree threshold τ. The magnitude of τ can be set according to the actual situation. There is no strict requirement in this embodiment. In this embodiment, τ = 0.7 is described. When the abnormal degree of the cycle in the health monitoring data curve is greater than or equal to τ, the cycle in the health monitoring data curve is an abnormal cycle. When the abnormal degree of the cycle in the health monitoring data curve is less than τ, the cycle in the health monitoring data curve is a normal cycle.

[0060] Thus far, the normal cycles and abnormal cycles in the health monitoring data curve are obtained.

[0061] Step S003: Obtain the time nodes of all abnormal cycles in the health monitoring data curve; according to the time nodes of all abnormal cycles in the health monitoring data curve, obtain the abnormal cycles with long-term persistence characteristics and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve.

[0062] It should be noted that there are various factors that cause abnormalities in the cycles of the health monitoring data curve. If the abnormal cycles in the health monitoring data curve do not have long-term persistence characteristics, then the abnormal cycles in the health monitoring data curve are very likely to be cycle abnormalities caused by transient factors such as exercise consumption or emotional changes; and the importance of cycle abnormalities caused by transient factors such as exercise consumption or emotional changes is low, and they can be regarded as normal cycles and subjected to lossy compression together with normal cycles. If the abnormal cycles in the health monitoring data curve have long-term persistence characteristics, then it is very likely to be cycle abnormalities caused by factors such as heart diseases, and the importance of cycle abnormalities caused by factors such as heart diseases is high, and they cannot be regarded as normal cycles, and all characteristics of these abnormal cycles need to be retained.

[0063] Specifically, for the o-th abnormal cycle in the health monitoring data curve, combine the o-th abnormal cycle with each other abnormal cycle to obtain several abnormal cycle pairs of the o-th abnormal cycle, and combine each abnormal cycle pair with each other abnormal cycle pair to obtain several abnormal cycle pair groups of the o-th abnormal cycle; take the absolute value of the time node difference between the two abnormal cycles in each abnormal cycle pair as the time difference value of each abnormal cycle pair; take the ratio of the smaller time difference value to the larger time difference value of the two abnormal cycle pairs in the abnormal cycle pair group as the time difference similarity of each abnormal cycle pair group; finally, preset a threshold ξ. When the ratio of the time difference similarity of any abnormal cycle pair group is greater than or equal to ξ, the o-th abnormal cycle has long-term persistence characteristics. When the ratio of the time difference similarity of all abnormal cycle pair groups is less than ξ, the o-th abnormal cycle does not have long-term persistence characteristics.

[0064] So far, the abnormal cycles with long-term persistence characteristics and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve are obtained.

[0065] Step S004: According to the normal cycles in the health monitoring data curve, the abnormal cycles with long-term persistence characteristics and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve, obtain a new health monitoring data curve and compress the new health monitoring data curve.

[0066] It should be noted that since the normal cycles and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve have a low importance level, while the abnormal cycles with long-term persistence characteristics in the health monitoring data curve have a high importance level; therefore, when processing the health monitoring data, the data corresponding to the normal cycles and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve can be lossily compressed; the data corresponding to the abnormal cycles with long-term persistence characteristics in the health monitoring data curve are completely retained.

[0067] Specifically, perform mean fitting on all the normal cycles in the health monitoring data curve to obtain the fitting results of all the normal cycles in the health monitoring data curve. Since mean fitting is a well-known technology, it will not be elaborated in this embodiment; replace all the normal cycles and all the abnormal cycles without long-term persistence characteristics in the health monitoring data curve with the fitting results of all the normal cycles in the health monitoring data curve to obtain a new health monitoring data curve, and finally use run-length encoding to compress the data corresponding to the new health monitoring data curve.

[0068] It should be further noted that in this embodiment, by performing mean fitting on the cycles with low importance level in the health monitoring data curve to obtain a new health monitoring data curve, while increasing the redundancy of the new health monitoring data curve, the important cycles in the new health monitoring data curve are retained, achieving the improvement of the compression effect of the health monitoring data while ensuring that the data after compression can reflect the user's health condition.

[0069] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing method for a smart big health system, characterized in that: The method comprises the following steps: Collect health monitoring data; obtain each cycle in the health monitoring data curve, the data in each cycle in the health monitoring data, and the time span of each cycle in the health monitoring data according to the health monitoring data; obtain the first abnormality degree of each cycle in the health monitoring data curve according to the data in each cycle in the health monitoring data and the time span of each cycle in the health monitoring data; obtain the second abnormality degree of each cycle in the health monitoring data curve according to the inflection point in each cycle in the health monitoring data curve; obtain the abnormality degree of each cycle in the health monitoring data curve according to the first abnormality degree and the second abnormality degree of each cycle in the health monitoring data curve; divide all cycles in the health monitoring data curve into abnormal cycles and normal cycles according to the abnormality degree of each cycle in the health monitoring data curve; Obtaining the time nodes of all abnormal cycles in the health monitoring data curve; obtaining the abnormal cycles with long-term persistence characteristics in the health monitoring data curve and the abnormal cycles without long-term persistence characteristics in the health monitoring data curve according to the time nodes of all abnormal cycles in the health monitoring data curve; According to the normal cycle in the health monitoring data curve, the abnormal cycle with long-term persistence characteristics in the health monitoring data curve, and the abnormal cycle without long-term persistence characteristics in the health monitoring data curve, a new health monitoring data curve is obtained, and the new health monitoring data curve is compressed.

2. The data processing method for the smart big health system according to claim 1, characterized in that: The collecting of health monitoring data; obtaining each cycle in the health monitoring data curve, the data in each cycle in the health monitoring data, and the time span of each cycle in the health monitoring data according to the health monitoring data, includes the following specific methods: Allow users to collect health monitoring data through smart bracelets while keeping their bodies relaxed. Through the health monitoring data, the collected health monitoring data is imported into the Kubios HRV software to obtain the health monitoring data curve, each cycle in the health monitoring data curve, the data in each cycle of the health monitoring data, and the time span of each cycle of the health monitoring data.

3. The data processing method for the smart big health system according to claim 1, characterized in that: The specific calculation formula for obtaining the first abnormality degree of each cycle in the health monitoring data curve is as follows: In the formula, Indicates the first abnormality degree of the i-th cycle in the health monitoring data curve; F i Represents the difference between the maximum and minimum values ​​in the i-th cycle of the health monitoring data curve; N represents the number of cycles in the health monitoring data curve; F j Represents the difference between the maximum and minimum values ​​in the jth cycle of the health monitoring data curve; T i Represents the time span of the i-th cycle in the health monitoring data curve; T j It represents the time span of the jth cycle in the health monitoring data curve; Norm() represents the linear normalization function; exp() represents the exponential function with a natural constant as the base; || represents the absolute value operation.

4. The data processing method for the smart big health system according to claim 1, characterized in that: The method of obtaining the second abnormality degree of each cycle in the health monitoring data curve according to the inflection point in each cycle in the health monitoring data curve includes the following specific methods: According to the inflection points in each cycle of the health monitoring data curve, each cycle in the health monitoring data curve is divided into several segments to obtain sub-segments of each cycle in the health monitoring data curve. According to the number of inflection points in each cycle of the health monitoring data curve and the cycle sub-segments in the health monitoring data curve, the second abnormality degree of each cycle in the health monitoring data curve is obtained. The specific calculation formula is: In the formula, Indicates the second abnormality degree of the i-th cycle in the health monitoring data curve; M i represents the number of inflection points in the i-th cycle of the health monitoring data curve; N represents the number of cycles in the health monitoring data curve; M j represents the number of inflection points in the jth cycle of the health monitoring data curve; K di,i represents the mean value of the slope of all data on the d-th sub-segment in the ith cycle of the health monitoring data curve; D i represents the number of sub-segments in the i-th cycle of the health monitoring data curve; K dj,j represents the mean value of the slope of all data on the dj-th sub-segment in the j-th cycle of the health monitoring data curve; D j It represents the number of sub-segments in the j-th period in the health monitoring data curve; exp() represents an exponential function with a natural constant as the base; A1 and A2 are preset weights; || represents absolute value operation.

5. The data processing method for the smart big health system according to claim 1, characterized in that: The specific calculation formula for obtaining the abnormality degree of each cycle in the health monitoring data curve is as follows: In the formula, σ i Indicates the abnormality degree of the i-th cycle in the health monitoring data curve; Indicates the first abnormality degree of the i-th cycle in the health monitoring data curve; It represents the second abnormality degree of the i-th cycle in the health monitoring data curve; μ1 and μ2 are preset weights.

6. The data processing method for the smart big health system according to claim 1, characterized in that: According to the abnormal degree of each cycle in the health monitoring data curve, all cycles in the health monitoring data curve are divided into abnormal cycles and normal cycles, including the specific method of: An abnormality threshold τ is preset. When the abnormality of a period in the health monitoring data curve is greater than or equal to τ, the period in the health monitoring data curve is an abnormal period. When the abnormality of a period in the health monitoring data curve is less than τ, the period in the health monitoring data curve is a normal period.

7. The data processing method for the smart big health system according to claim 1, characterized in that: The specific method of obtaining the time nodes of all abnormal cycles in the health monitoring data curve includes: For the oth abnormal cycle in the health monitoring data curve, the moment when the first data point in the oth abnormal cycle is recorded is selected as the time node of the oth abnormal cycle, and the time nodes of all abnormal cycles in the health monitoring data curve are obtained.

8. The data processing method for the smart big health system according to claim 1, characterized in that: The specific method of obtaining the abnormal period with long-term persistence characteristics in the health monitoring data curve and the abnormal period without long-term persistence characteristics in the health monitoring data curve includes: For the oth abnormal cycle in the health monitoring data curve, the oth abnormal cycle is combined with each other abnormal cycle to obtain several abnormal cycle pairs of the oth abnormal cycle, and each abnormal cycle pair is combined with each other abnormal cycle pair to obtain several abnormal cycle pair groups of the oth abnormal cycle; the absolute value of the time node difference between the two abnormal cycles in each abnormal cycle pair is used as the time difference value of each abnormal cycle pair; the ratio of the small time difference value to the large time difference value in the two abnormal cycle pairs of the abnormal cycle pair group is used as the time difference similarity of each abnormal cycle pair group; finally, a threshold ξ is preset. When the ratio of the time difference similarity of any abnormal cycle pair group is greater than or equal to ξ, the oth abnormal cycle has the long-term persistence feature. When the ratio of the time difference similarity of all abnormal cycle pair groups is less than ξ, the oth abnormal cycle does not have the long-term persistence feature.

9. The data processing method for the smart big health system according to claim 1, characterized in that: The obtaining of a new health monitoring data curve and the compression processing of the new health monitoring data curve include the following specific methods: Perform mean fitting on all normal cycles in the health monitoring data curve to obtain fitting results of all normal cycles in the health monitoring data curve; use the fitting results of all normal cycles in the health monitoring data curve to replace all normal cycles in the health monitoring data curve and all abnormal cycles that do not have long-term persistence characteristics to obtain a new health monitoring data curve, and finally use run-length encoding to compress the data corresponding to the new health monitoring data curve.

Images