Method and system for quantifying expected healthspan gain based on multi-source health data

Abstract

The application relates to the field of health management, and discloses a method and system for quantifying expected health life gain based on multi-source health data, the method comprising the following steps: acquiring multi-source health data; quantifying the gain value of each positive health behavior on the expected health life of a user and the impairment value of each bad living habit on the expected health life of the user respectively; calculating the total net expected health life gain value of the user; and outputting the total net expected health life gain value to generate prompt information for encouraging the user to improve the living habit. The application solves the problem that the health management in the prior art lacks quantitative basis and has low user compliance; by simultaneously acquiring real-time data of a smart wearable device, health report data and long-term living habit data, the application breaks the data island, makes the quantitative evaluation more comprehensive and accurate, and enhances the motivation for behavior change.

Description

Technical Field

[0001] This invention relates to the field of health management service technology, and in particular to a method and system for quantifying expected healthy lifespan gain based on multi-source health data. Background Technology

[0002] With the widespread adoption of smart wearable devices, users can continuously obtain health data such as heart rate, blood pressure, blood oxygen, sleep, and exercise. Existing health management products generally suffer from the following shortcomings: First, their health advice is entirely qualitative, only suggesting "staying up late is bad, exercise is good for health," without precisely informing users how much longer they can live by reducing late nights, how many years their blood pressure can be extended by achieving target levels, or how much lifespan improvement, diet, and sleep can contribute. In other words, users cannot perceive the specific health benefits, leading to extremely low adherence. Second, existing technologies suffer from severe data silos. Data from smartwatches, medical examination reports, chronic disease follow-ups, and daily diet and lifestyle are scattered and independent, lacking a unified model to comprehensively calculate the combined impact of this multi-source data on lifespan. For example, patent application CN115910333A discloses a method for assessing vitality gain and loss based on wearable devices, which can only output an abstract "vitality value" or "vitality gain / loss days." This is essentially an evaluation of current vitality, failing to establish a direct causal relationship between each specific lifestyle habit and its quantifiable impact on expected lifespan, and thus cannot solve the data silo problem.

[0003] Therefore, it is necessary to provide a method and system for quantifying the expected healthy lifespan gain based on multi-source health data to overcome the above-mentioned shortcomings. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quantifying the expected healthy lifespan gain based on multi-source health data. It aims to solve the technical problem of how to effectively integrate users' multi-source health data and accurately quantify each specific positive health behavior and unhealthy lifestyle habit as an increase or decrease in an individual's expected healthy lifespan, thereby providing users with highly motivating behavioral improvement guidance.

[0005] To achieve the above objectives, the present invention provides a method for quantifying expected healthy lifespan gain based on multi-source health data, comprising the following steps: Acquire multi-source health data, which includes at least physiological data monitored in real time through smart wearable devices, as well as users' health report data and long-term lifestyle and behavioral data; Based on a pre-defined quantification model, the gain value of each positive health behavior on the user's expected healthy lifespan and the reduction value of each unhealthy lifestyle habit on the user's expected healthy lifespan are quantified respectively. Based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit, the user's total net expected healthy life gain value is calculated. Output the total net expected healthy lifespan gain value to generate prompts that incentivize users to improve their lifestyle habits.

[0006] In a preferred embodiment, the physiological data includes at least one of heart rate, blood pressure, blood oxygen, body temperature, sleep data, and exercise data; the health report data includes at least one of physical examination index data and chronic disease diagnosis data; and the long-term lifestyle behavior data includes at least one of work-rest patterns, dietary structure, smoking frequency, alcohol consumption, and medication adherence.

[0007] In a preferred embodiment, the positive health behaviors include compliance behaviors in at least one of the following health dimensions: first aid safety compliance, chronic disease management compliance, cardiovascular health compliance, early screening compliance for major diseases compliance, traditional Chinese medicine constitution conditioning compliance, lifestyle improvement compliance, mental health compliance, health science popularization compliance, and physical fitness improvement compliance; the unhealthy lifestyle habits include non-compliance behaviors corresponding to the health dimensions.

[0008] In a preferred embodiment, quantifying the gain value of each positive health behavior on the user's expected healthy lifespan specifically includes: According to the formula Calculate the expected healthy lifespan gain value of the i-th positive health behavior; in, LE i Let be the expected healthy lifespan gain value of the i-th positive health behavior; α i This is the preset medical baseline coefficient corresponding to the i-th positive health behavior; β i The coefficient represents the user's actual compliance with the i-th positive health behavior, and its value ranges from [0,1]. c i These are personalized correction coefficients generated based on the user's personal information.

[0009] In a preferred embodiment, quantifying the reduction in a user's expected healthy lifespan caused by each unhealthy lifestyle habit specifically includes: According to the formula Calculate the expected healthy lifespan reduction value of the j-th unhealthy lifestyle habit; in, LS j The expected healthy lifespan reduction value for the j-th unhealthy lifestyle habit; d j The preset base risk coefficient corresponding to the j-th unhealthy lifestyle habit; e j Let be the frequency intensity coefficient of the user's attitude towards the j-th unhealthy lifestyle habit, with a value range of [0,1].

[0010] In a preferred embodiment, calculating the user's total net life expectancy gain specifically includes: According to the formula Calculate the total net expected healthy life gain value; in, LE total This represents the total net expected healthy life gain; ∑ LE i The sum of the gain values ​​of all positive health behaviors, ∑ LS j It is the sum of the detrimental effects of all unhealthy lifestyle habits; i This is a preset multi-health behavior synergistic life-extending amplification factor, with a value greater than 1, used to correct the synergistic effect of multiple health behaviors.

[0011] In a preferred embodiment, after acquiring the multi-source health data, the method further includes the steps of: performing data cleaning, standardization, and fusion alignment on the multi-source health data to generate time series data in a unified format.

[0012] In a preferred embodiment, the method further includes the step of: generating and displaying a priority list of improvement recommendations based on the total net expected healthy life gain value; wherein the priority list of improvement recommendations is used to indicate the potential for each health behavior to increase expected healthy life.

[0013] In a preferred embodiment, the method further includes the step of: summarizing and statistically analyzing the total net expected health life gain value of multiple users to generate a group health life gain statistical report.

[0014] The present invention also provides a system for quantifying expected healthy lifespan gain based on multi-source health data, comprising: Multi-source data acquisition module, used to acquire health data from multiple sources; The quantitative calculation engine is used to quantify the gain value of each positive health behavior on the user's expected healthy lifespan, and the reduction value of each bad lifestyle habit on the user's expected healthy lifespan, based on a preset quantitative model. The Net Life Extension Summary Module is used to calculate the user's total net expected healthy life gain based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit. The visualization incentive module outputs the total net expected healthy life gain value, which is used to generate prompts to motivate users to improve their lifestyle habits.

[0015] The method and system for quantifying expected healthy lifespan gain based on multi-source health data provided by this invention have the following beneficial effects: (1) It transforms abstract health advice (such as "exercise is good for health") into quantifiable results that can be intuitively perceived (such as "expected healthy lifespan increases by XX years"), which solves the problems of lack of quantitative basis and low user compliance in health management in the existing technology. (2) By simultaneously acquiring real-time data from smart wearable devices, health report data, and long-term lifestyle data, data silos are broken down, making quantitative assessments more comprehensive and accurate; (3) It can quantify the gain value of positive health behaviors and the loss value of bad lifestyle habits separately, forming a complete "reward and punishment" evaluation system, so that users can see both the "benefits of improvement" and the "losses of not improving", thereby enhancing their motivation to change their behavior; (4) The final output is the total net expected healthy life gain value, which provides users with clear and intuitive health improvement goals. It can be directly used to generate personalized incentive prompts, effectively improving users' willingness and compliance to improve their lifestyle habits. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the method for quantifying expected healthy lifespan gain based on multi-source health data provided by this invention; Figure 2 The diagram shows the architecture of the expected health lifespan gain quantification system based on multi-source health data provided by this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described in this specification are merely for explaining the invention and are not intended to limit the invention.

[0019] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] In an embodiment of the present invention, a method and system for quantifying the expected healthy lifespan gain based on multi-source health data are provided. This method can integrate multi-source health data and quantify each positive health behavior and each unhealthy lifestyle habit into a precise gain or loss value for expected healthy lifespan. Finally, by outputting an intuitive total net expected healthy lifespan gain value, the method can motivate users to actively improve their lifestyles.

[0022] Example 1 In this embodiment, as Figure 1 As shown, the method for quantifying the expected healthy lifespan gain based on multi-source health data includes the following steps S101-S104.

[0023] Step S101: Acquire multi-source health data. Multi-source health data includes at least physiological data monitored in real time through smart wearable devices, as well as user health report data and long-term lifestyle and behavioral data.

[0024] Specifically, the system can interact with external data sources via wireless communication networks (such as Wi-Fi, Bluetooth, and 5G) to obtain the three core data sources mentioned above. To ensure the comprehensiveness of the data, the acquired multi-source health data includes at least physiological data monitored in real time through smart wearable devices, health report data imported by users through application programming interfaces, and long-term lifestyle and behavioral data actively recorded by users on their terminal devices or obtained by the system through behavioral analysis.

[0025] In one exemplary embodiment, the physiological data can be data from various sensors acquired from a smartwatch or smart bracelet worn by the user. For example, heart rate, blood pressure, blood oxygen saturation, and heart rate variability (HRV) can be acquired via optical heart rate sensors and bioelectrical impedance sensors; step count, exercise intensity, and sedentary time can be acquired via accelerometers and gyroscopes; skin temperature can be acquired via temperature sensors; the system can also analyze the user's sleep duration, the ratio of deep to light sleep, and the number of times they stayed up late based on changes in body movement and heart rate during sleep. This data is recorded in time-series format.

[0026] Health report data can be physical examination report files (such as PDF files) uploaded by users through mobile applications or manually entered. The system extracts the structured information from them using existing optical character recognition technology, including but not limited to: biochemical indicators such as total cholesterol, low-density lipoprotein cholesterol, fasting blood glucose, uric acid, and creatinine, as well as electrocardiogram report conclusions and information on whether chronic diseases such as hypertension and diabetes have been diagnosed.

[0027] Long-term lifestyle and behavioral data are obtained through users' daily check-ins or by linking with smart devices. For example, the dietary structure and approximate salt and sugar intake of breakfast, lunch and dinner recorded by users in the application, the number of cigarettes smoked and the amount of alcohol consumed per day (in standard cups) manually entered by users, and medication reminders set by users and their adherence feedback.

[0028] Furthermore, after acquiring the aforementioned multi-source health data, this method also includes the steps of: performing data cleaning, standardization, and fusion alignment on the multi-source health data to generate time series data in a unified format.

[0029] Specifically, the system first performs anomaly data exclusion. For example, if a heart rate sensor generates invalid data exceeding 300 beats per minute due to improper wear, the system will automatically mark it as an anomaly and remove it. Secondly, for occasional missing values ​​in the time series, such as missing step count data for a particular hour, the system can automatically complete the data using linear interpolation or forward imputation. Finally, because data from different sources uses different units (e.g., blood pressure is measured in mmHg, and blood glucose in mmol / L), the system will standardize and normalize all data and align it using timestamps to create a standardized user health dataset with a consistent timeline.

[0030] Step S102: Based on the preset quantification model, quantify the gain value of each positive health behavior on the user's expected healthy lifespan, and the reduction value of each bad lifestyle habit on the user's expected healthy lifespan.

[0031] First, the preprocessed data needs to be analyzed to automatically identify the positive health behaviors and unhealthy lifestyle habits exhibited by the user in the current period.

[0032] In one exemplary embodiment, this system incorporates an assessment system covering nine health dimensions. Positive health behaviors refer to behaviors that meet preset medical standards across these dimensions. These positive health behaviors include behaviors meeting standards in at least one of the following health dimensions: emergency safety, chronic disease management, cardiovascular health, early screening for major diseases, traditional Chinese medicine constitution conditioning, lifestyle improvement, mental health, health education, and physical fitness enhancement. Unhealthy lifestyle habits include behaviors that do not meet the corresponding health dimensions.

[0033] For example, under the "Cardiovascular and Vascular Health Life Extension Dimension," if a user's systolic blood pressure is consistently controlled below 120 mmHg and diastolic blood pressure is consistently controlled below 80 mmHg, it is identified as a positive health behavior of "blood pressure reaching target levels." Under the "Lifestyle Habit Intervention Life Extension Dimension," if a user's daily sleep duration remains stable at 7-8 hours for a week, it is identified as a positive health behavior of "sufficient sleep." Conversely, unhealthy lifestyle habits refer to behaviors that do not meet the standards, such as "poor blood pressure control," "long-term sleep deprivation," and "smoking more than 5 cigarettes per day."

[0034] In this embodiment, for each identified positive health behavior, its gain value on the user's expected healthy lifespan is calculated according to a preset formula. Specifically, for the i-th positive health behavior, the formula for calculating its expected healthy lifespan gain value is as follows: in, LE i Let be the expected healthy lifespan gain value of the i-th positive health behavior.

[0035] α i This is a pre-defined medical baseline coefficient corresponding to the i-th positive health behavior. This coefficient is derived from authoritative public health research or medical literature and represents the average life expectancy extension resulting from achieving the target for this behavior. For example, for the behavior of "achieving target blood pressure," α i It can be set to 2.5 years, meaning that according to epidemiological data, effective blood pressure control can extend lifespan by an average of 2.5 years.

[0036] β i This is the user's actual compliance coefficient for the i-th positive health behavior, ranging from [0,1]. This coefficient is used to adjust the user's "level of achievement". For example, for the "sufficient sleep" behavior, if the medical baseline considers 7-8 hours of sleep per night as fully achieved (compliance is 1), and the user's average sleep duration in a cycle is 6.5 hours, then their compliance coefficient is... β i It can be set to 0.5 (this is just an example and not a practical limit), which means that its execution rate is 50% satisfactory.

[0037] c i This is a personalized correction coefficient generated based on the user's personal information. This coefficient takes into account the impact of individual differences such as age, gender, and existing medical history on the life-extending benefits. For example, for a 65-year-old user with a history of hypertension, the relative benefit of achieving target blood pressure may be higher than that of a healthy 35-year-old user. ci It can be set to a value greater than 1 for positive correction.

[0038] In this embodiment, for each identified unhealthy lifestyle habit, the reduction in the user's expected healthy lifespan is calculated according to a preset formula. Specifically, for the j-th unhealthy lifestyle habit, the formula for calculating the reduction in expected healthy lifespan is as follows: in, LS j This represents the reduction in expected healthy lifespan due to the j-th unhealthy lifestyle habit.

[0039] d j This is the pre-defined baseline risk coefficient for the j-th unhealthy lifestyle habit. This coefficient represents the inherent harmfulness of this unhealthy habit to lifespan. For example, for the unhealthy habit of "long-term smoking (one pack a day)," d j It can be set to 8 years, meaning that this habit can reduce lifespan by an average of 8 years.

[0040] e j Let be the severity frequency intensity coefficient for the user's j-th unhealthy lifestyle habit, with a value ranging from [0,1]. This coefficient is used to quantify the user's "execution dose" of the habit. For example, if "long-term smoking" in the medical baseline is defined as one pack (20 cigarettes) per day, and the user actually smokes 10 cigarettes per day, then their severity frequency intensity coefficient... e j It can be set to 0.5, indicating that its harm level is 50% of the baseline value. If a user smokes 30 cigarettes a day, then... e j It can be set to 1.

[0041] Step S103: Calculate the user's total net expected healthy life gain based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit.

[0042] Specifically, the formula for calculating the total net life expectancy gain is as follows: in, LE total This represents the total net expected healthy life gain; ∑ LE i The sum of the gain values ​​of all positive health behaviors, ∑ LS j It is the sum of the detrimental effects of all unhealthy lifestyle habits; iThis is a preset multi-health behavior synergistic life-extending amplification factor, with a value greater than 1, used to correct the synergistic effect of multiple health behaviors.

[0043] in, i The coefficient is introduced to correct for the synergistic effect that occurs when multiple health behaviors are improved simultaneously. For example, when a user quits smoking, controls blood pressure, and begins regular exercise simultaneously, the combined effect of these three actions on lifespan extension may be greater than the simple sum of their individual effects. i The coefficient is used to simulate this synergistic effect of "1+1>2". Through this step, the system can finally obtain a net life extension value accurate to two decimal places, such as "+3.25 years" or "-1.50 years".

[0044] Step S104: Output the total net expected healthy life gain value to generate prompts that motivate users to improve their lifestyle habits.

[0045] Finally, the total net life expectancy gain will be... LE total Output in an intuitive way, such as through screen display or voice prompts, to generate prompts that motivate users to improve their lifestyle habits. For example, a prominent card could be displayed on a user's smartphone and / or smartwatch app, such as: "Your healthy habits can increase your expected healthy lifespan by 3.25 years! Keep it up!" or "Current unhealthy habits may reduce your expected healthy lifespan by 1.5 years; improving the following behaviors can extend it by 1.8 years." Furthermore, this method also includes the step of generating and displaying a priority list of improvement recommendations based on the total net expected healthy life gain. The priority list of improvement recommendations indicates the potential for each health behavior to increase expected healthy life.

[0046] For example, the list might indicate that improving the bad habit of "smoking" could reduce life expectancy by XX years, while improving "blood pressure control" could increase life expectancy by YY years, thus guiding users to prioritize changing the behavior with the highest benefit. Users don't need to analyze the behavior themselves to know which behavior to start changing to gain the greatest life expectancy benefit, greatly improving the effectiveness of interventions and the user experience.

[0047] Furthermore, this method also includes the step of summarizing and statistically analyzing the total net life expectancy gain of multiple users to generate a group life expectancy gain statistical report. This report can display the average net life extension value of the entire organization, the improvement trend of various health behaviors, etc. That is, the above steps can also be extended from the individual level to the group level (such as enterprises and communities), enabling health management institutions or enterprises to understand the overall health level change trend of the group from a macro perspective, evaluate the overall effect of health intervention measures, and provide data support for the formulation and optimization of corporate health strategies.

[0048] It should be noted that the numerical values ​​in the above examples are for reference only, to help illustrate the implementation process of the technical solution of the present invention, and do not represent a specific limitation on the numerical values ​​of the present invention.

[0049] Example 2 This invention also provides a system 100 for quantifying expected healthy lifespan gain based on multi-source health data, used to execute the steps of the above-described method. This system can be deployed on a cloud server or on a terminal device (such as a smartphone or smartwatch) that communicates with a smart wearable device. It should be noted that the implementation principle of the system 100 for quantifying expected healthy lifespan gain based on multi-source health data can refer to the above-described method for quantifying expected healthy lifespan gain based on multi-source health data, and will not be repeated hereafter.

[0050] like Figure 2 As shown, the expected healthy lifespan gain quantification system 100 based on multi-source health data includes: Multi-source data acquisition module 10 is used to acquire multi-source health data; The quantitative calculation engine 20 is used to quantify the gain value of each positive health behavior on the user's expected healthy lifespan and the reduction value of each bad lifestyle habit on the user's expected healthy lifespan based on the preset quantitative model. The Net Life Extension Summary Module 30 is used to calculate the user's total net expected healthy life gain based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit. The visualization incentive module 40 is used to output the total net expected healthy life gain value, which is used to generate prompts to motivate users to improve their lifestyle habits.

[0051] In summary, the method and system for quantifying expected healthy lifespan gain based on multi-source health data provided by this invention have the following beneficial effects: (1) It transforms abstract health advice (such as "exercise is good for health") into quantifiable results that can be intuitively perceived (such as "expected healthy lifespan increases by XX years"), which solves the problems of lack of quantitative basis and low user compliance in health management in the existing technology. (2) By simultaneously acquiring real-time data from smart wearable devices, health report data, and long-term lifestyle data, data silos are broken down, making quantitative assessments more comprehensive and accurate; (3) It can quantify the gain value of positive health behaviors and the loss value of bad lifestyle habits separately, forming a complete "reward and punishment" evaluation system, so that users can see both the "benefits of improvement" and the "losses of not improving", thereby enhancing their motivation to change their behavior; (4) The final output is the total net expected healthy life gain value, which provides users with clear and intuitive health improvement goals. It can be directly used to generate personalized incentive prompts, effectively improving users' willingness and compliance to improve their lifestyle habits.

[0052] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0053] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0054] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0055] In the embodiments provided by this invention, it should be understood that the disclosed systems, devices / terminal equipment, and methods can be implemented in other ways. For example, the system or device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of systems or units may be electrical, mechanical, or other forms.

[0056] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0057] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0058] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. A method for quantifying expected healthy lifespan gain based on multi-source health data, characterized in that, Includes the following steps: Acquire multi-source health data, which includes at least physiological data monitored in real time through smart wearable devices, as well as users' health report data and long-term lifestyle and behavioral data; Based on a pre-defined quantification model, the gain value of each positive health behavior on the user's expected healthy lifespan and the reduction value of each unhealthy lifestyle habit on the user's expected healthy lifespan are quantified respectively. Based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit, the user's total net expected healthy life gain value is calculated. Output the total net expected healthy lifespan gain value to generate prompts that incentivize users to improve their lifestyle habits.

2. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, The physiological data includes at least one of heart rate, blood pressure, blood oxygen, body temperature, sleep data, and exercise data; the health report data includes at least one of physical examination indicators and chronic disease diagnosis data; the long-term lifestyle behavior data includes at least one of work and rest patterns, dietary structure, smoking frequency, alcohol consumption, and medication adherence.

3. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, The positive health behaviors include behaviors that meet the standards in at least one of the following health dimensions: first aid safety, chronic disease management, cardiovascular health, early screening for major diseases, traditional Chinese medicine constitution conditioning, lifestyle improvement, mental health, health science popularization, and physical fitness improvement; the unhealthy lifestyle habits include behaviors that do not meet the standards corresponding to the aforementioned health dimensions.

4. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, The quantification of the gain value of each positive health behavior on the user's expected healthy lifespan specifically includes: According to the formula Calculate the expected healthy lifespan gain value of the i-th positive health behavior; in, LE i Let be the expected healthy lifespan gain value of the i-th positive health behavior; α i This is the preset medical baseline coefficient corresponding to the i-th positive health behavior; β i The coefficient represents the user's actual compliance with the i-th positive health behavior, and its value ranges from [0,1]. γ i These are personalized correction coefficients generated based on the user's personal information.

5. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 4, characterized in that, The quantification of the reduction in a user's expected healthy lifespan caused by each unhealthy lifestyle habit specifically includes: According to the formula Calculate the expected healthy lifespan reduction value of the j-th unhealthy lifestyle habit; in, LS j The expected healthy lifespan reduction value for the j-th unhealthy lifestyle habit; δ j The preset base risk coefficient corresponding to the j-th unhealthy lifestyle habit; ε j Let be the frequency intensity coefficient of the user's attitude towards the j-th unhealthy lifestyle habit, with a value range of [0,1].

6. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 5, characterized in that, The calculation of the user's total net life expectancy gain specifically includes: According to the formula Calculate the total net expected healthy life gain value; in, LE tota This represents the total net expected healthy life gain; ∑ LE i The sum of the gain values ​​of all positive health behaviors, ∑ LS j It is the sum of the detrimental effects of all unhealthy lifestyle habits; θ This is a preset multi-health behavior synergistic life-extending amplification factor, with a value greater than 1, used to correct the synergistic effect of multiple health behaviors.

7. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, After acquiring multi-source health data, the process further includes the steps of data cleaning, standardization, and fusion alignment of the multi-source health data to generate time-series data in a unified format.

8. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, It also includes the step of generating and displaying a priority list of improvement recommendations based on the total net expected healthy life gain value; wherein the priority list of improvement recommendations is used to indicate the potential for each health behavior to increase expected healthy life.

9. The method for quantifying expected healthy lifespan gain based on multi-source health data as described in claim 1, characterized in that, It also includes the step of summarizing and statistically analyzing the total net expected healthy life gain value of multiple users to generate a group healthy life gain statistical report.

10. A system for quantifying expected healthy lifespan gain based on multi-source health data, characterized in that, include: Multi-source data acquisition module, used to acquire health data from multiple sources; The quantitative calculation engine is used to quantify the gain value of each positive health behavior on the user's expected healthy lifespan, and the reduction value of each bad lifestyle habit on the user's expected healthy lifespan, based on a preset quantitative model. The Net Life Extension Summary Module is used to calculate the user's total net expected healthy life gain based on the gain value of each positive health behavior and the loss value of each unhealthy lifestyle habit. The visualization incentive module outputs the total net expected healthy life gain value, which is used to generate prompts to motivate users to improve their lifestyle habits.

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