Smart health management methods and platforms based on data analysis

Abstract

The present invention discloses a smart health management method and platform based on data analysis, which relates to the field of health management technology. The method includes: obtaining target health management expectations based on target historical health records of target users, clustering and screening predetermined physiological indicators, and obtaining target key physiological indicators; matching a first indicator set corresponding to a first expectation; analyzing a first real-time indicator parameter set through a first management channel in a health management model to obtain a first management analysis result; if the first expectation does not reach a first predetermined expectation threshold, issuing a first management instruction, calling a first management plan, and performing health management on the target user. The present invention solves the technical problem in the prior art that the accuracy and effectiveness of current health management methods are low due to the large amount of health monitoring data and individual differences, and achieves the technical effect of improving the accuracy and effectiveness of health management by extracting key physiological indicators for personalized monitoring and management.

Description

Technical Field

[0001] The present invention relates to the field of health management technology, and in particular to a smart health management method and platform based on data analysis. Background Art

[0002] The purpose of health management is to prevent and control the occurrence and development of diseases, reduce medical expenses, and improve the quality of life. Smart health management is a new management model that uses intelligent equipment and advanced technologies to monitor, evaluate, and intervene in personal health. It can process a large amount of health data and provide various health management and service applications. However, due to the large amount of health monitoring information data and individual differences between users, the existing technology has the problem of being unable to achieve personalized monitoring and management of users due to individual differences between users when using big data of individual characteristics of users for user health management. In other words, the current health management accuracy and effectiveness are low. Summary of the invention

[0003] The present application provides a smart health management method and platform based on data analysis, which is used to solve the technical problem in the prior art that the current health management methods have low accuracy and effectiveness due to the large amount of health monitoring data and individual differences.

[0004] The first aspect of the present application provides a smart health management method based on data analysis, the method comprising: obtaining a target historical health record of a target user; clustering and screening predetermined physiological indicators according to the target health management expectations obtained by analyzing the target historical health records to obtain target key physiological indicators; matching a first indicator set corresponding to a first expectation in the target key physiological indicators, wherein the first expectation refers to any one of the target health management expectations; analyzing a first real-time indicator parameter set of the target user collected based on the first indicator set through a first management channel in a health management model to obtain a first management analysis result; according to the first management analysis result, if the first expectation does not reach a first predetermined expectation threshold, issuing a first management instruction; based on the first management instruction, retrieving a first management plan, and performing health management on the target user according to the first management plan.

[0005] The second aspect of the present application provides a smart health management platform based on data analysis, the platform comprising: a historical health record acquisition module, the historical health record acquisition module is used to acquire the target historical health record of the target user; a key physiological indicator acquisition module, the key physiological indicator acquisition module is used to cluster and screen the predetermined physiological indicators according to the target health management expectation obtained by analyzing the target historical health record, and obtain the target key physiological indicators; a first indicator set acquisition module, the first indicator set acquisition module is used to match the first indicator set corresponding to the first expectation in the target key physiological indicators, wherein the first expectation refers to any management expectation in the target health management expectations; a first management analysis result acquisition module, the first management analysis result acquisition module is used to analyze the first real-time indicator parameter set of the target user collected based on the first indicator set through the first management channel in the health management model, and obtain a first management analysis result; a first management instruction sending module, the first management instruction sending module is used to issue a first management instruction according to the first management analysis result if the first expectation does not reach the first predetermined expectation threshold; a user health management module, the user health management module is used to call a first management plan based on the first management instruction, and perform health management on the target user according to the first management plan.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The data analysis-based smart health management method provided by the present application relates to the field of health management technology. According to the target health management expectations of the target user, the predetermined physiological indicators are clustered and screened to obtain the target key physiological indicators, and the first indicator set corresponding to the first expectation is matched, and the corresponding first real-time indicator parameter set is obtained for analysis. If the first expectation in the analysis result does not reach the first predetermined expectation threshold, a first management instruction is issued, and the first management plan is called to perform health management on the target user. The technical problem that the accuracy and effectiveness of the current health management method are low due to the large amount of health monitoring data and individual differences in the prior art is solved, and the technical effect of improving the accuracy and effectiveness of health management by extracting key physiological indicators for personalized monitoring and management is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1A flowchart of a smart health management method based on data analysis provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a process for obtaining target health management expectations in a smart health management method based on data analysis provided in an embodiment of the present application;

[0011] Figure 3 A flowchart of a process for obtaining a first indicator set in a smart health management method based on data analysis provided in an embodiment of the present application;

[0012] Figure 4 A schematic diagram of the structure of a smart health management platform based on data analysis provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: historical health record acquisition module 11, key physiological indicator acquisition module 12, first indicator set acquisition module 13, first management analysis result acquisition module 14, first management instruction sending module 15, user health management module 16. DETAILED DESCRIPTION

[0014] The present application provides an intelligent health management method based on data analysis, which is used to solve the technical problem in the prior art that the current health management methods have low accuracy and effectiveness due to the large amount of health monitoring data and individual differences.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0017] Embodiment 1

[0018] like Figure 1As shown, the present application provides a smart health management method based on data analysis, the method comprising:

[0019] P10: Obtain the target historical health records of the target user;

[0020] Specifically, the target historical health records of the target user are obtained through various channels, such as manual upload by the user, data interface of medical institutions, third-party health data platform, etc., to obtain the user's physical examination report, medical history record, medication record, life and eating habits data, etc., and data cleaning is performed to remove duplicate, erroneous or inconsistent information to obtain the target historical health record, which is stored in a safe and reliable database as the basic data for user health management. In addition, the target historical health record is dynamically updated with real-time manual updates by the user and changes in data on the third-party health data platform.

[0021] P20: clustering and screening predetermined physiological indicators according to the target health management expectations obtained by analyzing the target historical health records to obtain target key physiological indicators;

[0022] Further, such as Figure 2 As shown, step P20 of the embodiment of the present application also includes:

[0023] P21: extracting the first historical physical examination record in the target historical health record;

[0024] P22: Analyze the first historical physical examination record to obtain the first historical abnormal part of the target user;

[0025] P23: Performing a health assessment on the first historical abnormal part in combination with the target basic information of the target user to obtain the first expectation;

[0026] The target basic information includes the target personal information and target medical records of the target user.

[0027] P24: Establish the target health management expectation based on the first expectation.

[0028] Optionally, the target historical health records are analyzed to obtain the target health management expectations, that is, the health management goals expected by the target user, such as the target user's personal weight management goals, heart rate management goals, etc., and the predetermined physiological indicators are clustered and screened based on the target health management expectations to identify key physiological indicators closely related to the target user's health management as reference data for user health management. Among them, the predetermined physiological indicators include heart rate, blood pressure, blood sugar, weight, etc.

[0029] Specifically, the process of obtaining the target health management expectations may be to randomly extract user health records of any time period from the target historical health records as the first historical physical examination records, and analyze the first historical physical examination records to identify abnormal parts or indicators shown by the target user in past physical examinations, and obtain the first historical abnormal part of the target user, such as the heart or spleen.

[0030] Furthermore, a health assessment is performed on the first historical abnormal part in combination with the target basic information of the target user, and the target basic information includes the target personal information and target medical records of the target user, wherein the target personal information such as age, gender, genetics, etc., and the target medical records such as past medical history, surgical history, medication history, etc., have health standards that are different from normal. Therefore, a personalized health assessment can be performed based on the target personal information and target medical records of the target user, thereby determining the target user's specific expectations or needs for health management and obtaining the first expectation.

[0031] P30: Matching a first indicator set corresponding to a first expectation in the target key physiological indicator, wherein the first expectation refers to any management expectation in the target health management expectations;

[0032] Further, such as Figure 3 As shown, step P30 of the embodiment of the present application also includes:

[0033] P31: taking the first physiological indicator extracted from the predetermined physiological indicator as the first independent variable, and taking the first expectation as the first dependent variable;

[0034] P32: sequentially traverse the first historical physical examination records to obtain a first independent variable parameter of the first independent variable and a first dependent variable parameter of the first dependent variable;

[0035] P33: Correlation analysis is performed on the first independent variable parameter and the first dependent variable parameter to obtain a first correlation index;

[0036] P34: When the first correlation index reaches a predetermined correlation threshold, adding the first physiological indicator to the first indicator set.

[0037] It should be understood that the first indicator set corresponding to the first expectation is matched in the target key physiological indicators, and the first indicator set is a collection of all physiological indicators that affect the first expectation, and the first expectation refers to any management expectation in the target health management expectations.

[0038] Specifically, the first physiological indicator extracted from the predetermined physiological indicators is used as the first independent variable, such as heart rate, blood pressure, etc. as the first independent variable, and the first expectation is used as the first dependent variable, such as blood pressure management target, blood sugar management target, etc. as the first dependent variable. Furthermore, data related to the first independent variable and the first dependent variable are searched in the first historical physical examination record, and the specific value of the first independent variable is extracted as the first independent variable parameter, and the state or value of the first dependent variable is extracted as the first dependent variable parameter.

[0039] Furthermore, statistical methods, such as the Pearson correlation coefficient, the Spearman rank correlation coefficient, etc., are used to perform correlation analysis on the first independent variable parameter and the first dependent variable parameter to obtain a first correlation index, which can reflect the strength of the correlation between the two. When the first correlation index reaches a predetermined correlation threshold, it indicates that there is a significant correlation between the two. The first physiological indicator is then added to the first indicator set, and the first indicator set is used for subsequent health management analysis and formulation of intervention measures, which can make subsequent health management strategies more accurate and effective, and can more directly intervene and adjust to the actual needs of target users.

[0040] P40: Analyze the first real-time indicator parameter set of the target user collected based on the first indicator set through the first management channel in the health management model to obtain a first management analysis result;

[0041] Furthermore, step P40 of the embodiment of the present application also includes:

[0042] P41: using the first indicator set as a data screening constraint, and forming a first indicator parameter group based on the data screening constraint, wherein the first indicator parameter group has an identifier of a first expected parameter;

[0043] P42: Based on the support vector machine principle, supervised learning is performed on the first data group formed based on the first indicator parameter group and the first expected parameter to obtain the first management channel.

[0044] Optionally, a specific management channel in a pre-built health management model is used to process and analyze the data collected in real time. The health management model includes multiple management channels, each management channel corresponds to a management expectation. Therefore, the first real-time indicator parameter set of the target user collected based on the first indicator set can be analyzed through the first management channel in the health management model to obtain a first management analysis result.

[0045] Among them, the construction process of the first management channel can be, first, using the first indicator set as a data screening constraint, filtering out data corresponding to the data screening constraint from the data collected in real time to form a first indicator parameter group, and in order to ensure the accuracy and traceability of the data, adding an identifier of the first expected parameter to the first indicator parameter group, so that the association between these parameters and specific expectations can be clearly identified in subsequent analysis.

[0046] Furthermore, supervised learning is performed on the first data group consisting of the first indicator parameter group and the first expected parameter group using a machine learning algorithm such as a support vector machine, wherein the first data group contains both positive samples and negative samples, that is, the expected health indicator data and the data that are not achieved. The supervised learning is a method of training a model using a known training data set, that is, using labeled data. In the present application, the first indicator parameter group is used as input data, and the first expected parameter is used as output or label data, and supervised training is performed until the output data converges to obtain the first management channel.

[0047] P50: According to the first management analysis result, if the first expectation does not reach a first predetermined expectation threshold, issuing a first management instruction;

[0048] Specifically, based on the first management analysis result, it is determined whether the user's health indicators reach the predetermined expected indicators, that is, whether the first expectation reaches the first predetermined expected threshold. First, the first expectation is extracted based on the first management analysis result and compared with the first predetermined expected threshold. If the first expectation does not reach the first predetermined expected threshold, the response mechanism is immediately triggered, that is, the first management instruction is issued. The first management instruction may include various forms of health management suggestions or intervention measures, such as reminding the user to adjust his lifestyle, suggesting that the user undergo further medical examinations, adjusting the dosage of medication, etc.

[0049] P60: Retrieve a first management plan based on the first management instruction, and perform health management on the target user according to the first management plan.

[0050] Exemplarily, based on the specific content of the first management instruction and the current health status of the target user, the most appropriate management plan is matched from the health management plan library as the first management plan, and health management of the target user is performed according to the first management plan, including reminding the user to adjust his or her lifestyle, recommending a suitable exercise plan, providing nutritional advice, etc., and in the process of executing health management, the user's health status and response are continuously monitored, and the management plan is adjusted and optimized according to actual conditions. At the same time, user feedback is collected to further improve the quality and effectiveness of health management services.

[0051] Furthermore, the embodiment of the present application further includes step P70, and step P70 further includes:

[0052] P71: Obtain any indicator in the first indicator set;

[0053] P72: Match any indicator parameter of any indicator in the first real-time indicator parameter set;

[0054] P73: The arbitrary indicator parameter is combined with the collection time of the first real-time indicator parameter set to obtain an arbitrary indicator parameter time series;

[0055] P74: Analyze the time series of the arbitrary indicator parameters to obtain the target prediction parameters of the arbitrary indicator at the target time;

[0056] P75: Analyze the target prediction indicator parameter set formed based on the target prediction parameter through the first management channel to obtain the target management analysis result under the target time;

[0057] P76: Perform health prediction management on the target user based on the target management analysis results.

[0058] In a possible embodiment of the present application, any one physiological indicator is selected from the first indicator set for further analysis, and the real-time parameter value corresponding to the selected arbitrary indicator is matched in the first real-time indicator parameter set of the target user collected in real time, that is, the arbitrary indicator parameter. Further, the arbitrary indicator parameter is serialized in combination with the collection time of the first real-time indicator parameter set to generate time series data of the indicator changing over time, that is, the arbitrary indicator parameter time series.

[0059] Furthermore, the indicator parameter time series is analyzed using time series analysis, machine learning prediction models and other technologies to predict the target prediction parameters of the arbitrary indicator at a certain target time point in the future. Furthermore, the target prediction parameters are combined into a target prediction indicator parameter set, and analyzed through the first management channel constructed previously to obtain the health management analysis result at the target time, that is, the predicted health management result.

[0060] Finally, based on the target management analysis results, health prediction management suggestions are provided to target users, such as early warning of potential health risks, provision of preventive health suggestions, etc., thereby predicting future health trends and risks and providing users with more comprehensive and forward-looking health management services.

[0061] Furthermore, step P74 of the embodiment of the present application also includes:

[0062] P74-1: Perform fitting analysis on the arbitrary index parameter time series to obtain an arbitrary index curve;

[0063] P74-2: Input the target time into the arbitrary curve polynomial of the arbitrary indicator curve to obtain the output result of the arbitrary curve polynomial, wherein the output result includes the target prediction parameter.

[0064] Specifically, mathematical methods such as polynomial fitting, curve fitting, etc. are used to fit and analyze the arbitrary indicator parameter time series, and the best mathematical expression that can describe the change of the parameter over time is found, that is, the arbitrary indicator curve, which can be a polynomial or other types of functions. Further, the target time is used as an input value and substituted into the arbitrary curve polynomial of the arbitrary indicator curve. The output result of the arbitrary curve polynomial at the target time is obtained by calculation. The output result is the predicted arbitrary indicator parameter value at the target time, that is, the target prediction parameter.

[0065] In summary, the embodiments of the present application have at least the following technical effects:

[0066] This application clusters and screens predetermined physiological indicators based on the target health management expectations of the target user, obtains target key physiological indicators, matches the first indicator set corresponding to the first expectation, obtains the corresponding first real-time indicator parameter set for analysis, and if the first expectation in the analysis result does not reach the first predetermined expectation threshold, a first management instruction is issued to call the first management plan to perform health management on the target user.

[0067] The technical effect of extracting key physiological indicators for personalized monitoring and management has been achieved, thereby improving the accuracy and effectiveness of health management.

[0068] Embodiment 2

[0069] Based on the same inventive concept as the smart health management method based on data analysis in the aforementioned embodiment, Figure 4 As shown, the present application provides a smart health management platform based on data analysis, and the platform and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the platform includes:

[0070] A historical health record acquisition module 11, wherein the historical health record acquisition module 11 is used to acquire a target historical health record of a target user;

[0071] A key physiological indicator acquisition module 12, the key physiological indicator acquisition module 12 is used to cluster and screen predetermined physiological indicators according to the target health management expectations obtained by analyzing the target historical health records, to obtain target key physiological indicators;

[0072] A first indicator set acquisition module 13, the first indicator set acquisition module 13 is used to match a first indicator set corresponding to a first expectation in the target key physiological indicator, wherein the first expectation refers to any management expectation in the target health management expectations;

[0073] A first management analysis result acquisition module 14, which is used to analyze the first real-time indicator parameter set of the target user collected based on the first indicator set through the first management channel in the health management model to obtain a first management analysis result;

[0074] A first management instruction sending module 15, the first management instruction sending module 15 is used to issue a first management instruction according to the first management analysis result if the first expectation does not reach a first predetermined expectation threshold;

[0075] The user health management module 16 is used to call a first management plan based on the first management instruction, and perform health management on the target user according to the first management plan.

[0076] Furthermore, the key physiological indicator acquisition module 12 is also used to perform the following steps:

[0077] Extracting a first historical physical examination record from the target historical health record;

[0078] Analyzing the first historical physical examination record to obtain a first historical abnormal part of the target user;

[0079] Performing a health assessment on the first historical abnormal part in combination with the target basic information of the target user to obtain the first expectation;

[0080] The target health management expectation is constructed based on the first expectation.

[0081] Furthermore, the first indicator set acquisition module 13 is further configured to perform the following steps:

[0082] Using a first physiological indicator extracted from the predetermined physiological indicators as a first independent variable and using the first expectation as a first dependent variable;

[0083] Sequentially traverse the first historical physical examination records to obtain a first independent variable parameter of the first independent variable and a first dependent variable parameter of the first dependent variable;

[0084] Correlation analysis is performed on the first independent variable parameter and the first dependent variable parameter to obtain a first correlation index;

[0085] When the first correlation index reaches a predetermined correlation threshold, the first physiological indicator is added to the first indicator set.

[0086] Furthermore, the first management analysis result acquisition module 14 is further configured to perform the following steps:

[0087] Using the first indicator set as a data screening constraint, and forming a first indicator parameter group based on the data screening constraint, wherein the first indicator parameter group has an identifier of a first expected parameter;

[0088] Based on the support vector machine principle, supervised learning is performed on the first data group formed based on the first indicator parameter group and the first expected parameter to obtain the first management channel.

[0089] Furthermore, the platform also includes:

[0090] An arbitrary indicator acquisition module, the arbitrary indicator acquisition module is used to acquire any indicator in the first indicator set;

[0091] An arbitrary indicator parameter acquisition module, the arbitrary indicator parameter acquisition module is used to match an arbitrary indicator parameter of the arbitrary indicator in the first real-time indicator parameter set;

[0092] An arbitrary indicator parameter timing acquisition module, wherein the arbitrary indicator parameter timing acquisition module is used to combine the arbitrary indicator parameter with the collection time of the first real-time indicator parameter set to obtain an arbitrary indicator parameter timing;

[0093] A target prediction parameter acquisition module, wherein the target prediction parameter acquisition module is used to analyze the time series of the arbitrary indicator parameters to obtain the target prediction parameters of the arbitrary indicator at the target time;

[0094] A target management analysis module, the target management analysis module is used to analyze the target prediction indicator parameter set formed based on the target prediction parameters through the first management channel to obtain a target management analysis result under the target time;

[0095] A health prediction management module is used to perform health prediction management on the target user according to the target management analysis result.

[0096] Furthermore, the platform also includes:

[0097] An arbitrary indicator curve acquisition module, the arbitrary indicator curve acquisition module is used to perform fitting analysis on the arbitrary indicator parameter time series to obtain an arbitrary indicator curve;

[0098] A target prediction parameter acquisition module is used to input the target time into the arbitrary curve polynomial of the arbitrary indicator curve to obtain an output result of the arbitrary curve polynomial, wherein the output result includes the target prediction parameter.

[0099] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0101] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A smart health management method based on data analysis, characterized in that: include: Obtain the target historical health records of the target user; Clustering and screening predetermined physiological indicators according to target health management expectations obtained by analyzing the target historical health records to obtain target key physiological indicators; Matching a first indicator set corresponding to a first expectation in the target key physiological indicator, wherein the first expectation refers to any management expectation in the target health management expectations; Analyzing a first real-time indicator parameter set of the target user collected based on the first indicator set through a first management channel in a health management model to obtain a first management analysis result; According to the first management analysis result, if the first expectation does not reach a first predetermined expectation threshold, issuing a first management instruction; Retrieving a first management plan based on the first management instruction, and performing health management on the target user according to the first management plan; The process of obtaining the target health management expectation includes: Extracting a first historical physical examination record from the target historical health record; Analyzing the first historical physical examination record to obtain a first historical abnormal part of the target user; Performing a health assessment on the first historical abnormal part in combination with the target basic information of the target user to obtain the first expectation, wherein the target basic information includes the target personal information and target medical records of the target user; Establishing the target health management expectation based on the first expectation; Clustering and screening predetermined physiological indicators according to the target health management expectation, identifying key physiological indicators closely related to the health management of the target user, and obtaining the target key physiological indicators; The process of obtaining the first indicator set includes: Using a first physiological indicator extracted from the predetermined physiological indicators as a first independent variable and using the first expectation as a first dependent variable; Sequentially traverse the first historical physical examination records to obtain a first independent variable parameter of the first independent variable and a first dependent variable parameter of the first dependent variable; Correlation analysis is performed on the first independent variable parameter and the first dependent variable parameter to obtain a first correlation index; When the first correlation index reaches a predetermined correlation threshold, adding the first physiological indicator to the first indicator set; The process of acquiring the first management channel includes: Using the first indicator set as a data screening constraint, and forming a first indicator parameter group based on the data screening constraint, wherein the first indicator parameter group has an identifier of a first expected parameter; Performing supervised learning on a first data group formed based on the first indicator parameter group and the first expected parameter based on the support vector machine principle to obtain the first management channel; Analyzing, through the first management channel, a first real-time indicator parameter set of the target user collected based on the first indicator set to obtain a first management analysis result; Extracting a first expectation based on the first management analysis result and comparing it with the first predetermined expectation threshold, and immediately triggering a response mechanism, that is, issuing a first management instruction, if the first expectation does not reach the first predetermined expectation threshold; Obtain any indicator in the first indicator set; Matching any indicator parameter of the any indicator in the first real-time indicator parameter set; The arbitrary indicator parameter is combined with the collection time of the first real-time indicator parameter set to obtain an arbitrary indicator parameter time series; Analyze the time series of the arbitrary indicator parameters to obtain the target prediction parameters of the arbitrary indicator at the target time; Analyzing the target prediction indicator parameter set formed based on the target prediction parameters through the first management channel to obtain the target management analysis result under the target time; Perform health prediction management on the target user according to the target management analysis result; The process of obtaining the target prediction parameter includes: Performing fitting analysis on the arbitrary indicator parameter time series to obtain an arbitrary indicator curve; The target time is input into an arbitrary curve polynomial of the arbitrary indicator curve to obtain an output result of the arbitrary curve polynomial, wherein the output result includes the target prediction parameter.

2. A smart health management platform based on data analysis, characterized by: The smart health management platform based on data analysis is used to execute the smart health management method based on data analysis described in claim 1, and the platform includes: A historical health record acquisition module, wherein the historical health record acquisition module is used to acquire a target historical health record of a target user; A key physiological index acquisition module, the key physiological index acquisition module is used to cluster and screen predetermined physiological indexes according to the target health management expectations obtained by analyzing the target historical health records to obtain target key physiological indexes; A first indicator set acquisition module, the first indicator set acquisition module is used to match a first indicator set corresponding to a first expectation in the target key physiological indicator, wherein the first expectation refers to any management expectation in the target health management expectations; a first management analysis result acquisition module, the first management analysis result acquisition module being used to analyze the first real-time indicator parameter set of the target user collected based on the first indicator set through a first management channel in the health management model to obtain a first management analysis result; a first management instruction sending module, the first management instruction sending module being used to issue a first management instruction according to the first management analysis result if the first expectation does not reach a first predetermined expectation threshold; A user health management module, wherein the user health management module is used to call a first management plan based on the first management instruction, and perform health management on the target user according to the first management plan.

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