WO2023025037A1 - 健康管理方法、系统和电子设备 - Google Patents

健康管理方法、系统和电子设备 Download PDF

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Publication number
WO2023025037A1
WO2023025037A1 PCT/CN2022/113387 CN2022113387W WO2023025037A1 WO 2023025037 A1 WO2023025037 A1 WO 2023025037A1 CN 2022113387 W CN2022113387 W CN 2022113387W WO 2023025037 A1 WO2023025037 A1 WO 2023025037A1
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WIPO (PCT)
Prior art keywords
user
health
intervention
plan
electronic device
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PCT/CN2022/113387
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English (en)
French (fr)
Inventor
杨剑
东巍
李扬
苏诚
李雪晨
于野
朱洲
Original Assignee
华为技术有限公司
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP22860383.3A priority Critical patent/EP4369357A1/en
Publication of WO2023025037A1 publication Critical patent/WO2023025037A1/zh
Priority to US18/589,054 priority patent/US20240203603A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present application relates to the field of terminal and communication technologies, in particular to a health management method, system and electronic equipment.
  • the present application provides a health management method, system and electronic equipment, which are used to manage the user's health and promote the achievement of the user's active health management goal.
  • the present application provides a health management method, the method comprising: an electronic device acquires a first intervention plan generated according to user data of a first user; the electronic device predicts part and/or all of the first intervention plan The predicted value of the health indicator after completion.
  • the first intervention plan may include a first exercise plan, and/or a first diet plan, and/or a first health habit check-in task set.
  • the electronic device can predict the predicted value of the user's health indicators after the intervention plan is partially and/or fully completed, and the predicted value can greatly improve the user's confidence and subjective initiative in implementing the intervention plan, and promote the user's Achievement of active health management goals.
  • the electronic device acquiring the first intervention plan generated according to the user data of the first user specifically includes: the electronic device acquiring the user data of the first user; the electronic The device generates a first intervention plan according to the user data.
  • the electronic device generates the first intervention plan based on the acquired user data of the first user, which improves the effectiveness of the generated first intervention plan and promotes the achievement of the user's active health management goal.
  • the electronic device generates a first intervention plan according to the user data, which specifically includes: the electronic device identifies the user's health risk factors according to the user data; The user's health risk factors generate the first intervention plan.
  • the electronic device can first identify the user's health risk factors of the first user according to the user data of the first user, and then generate a first intervention plan for the user risk factors. A user is more targeted, which improves the effectiveness of the generated intervention plan for user health management of the first user.
  • the user data includes user basic information, user behavior data, and/or user health data.
  • the user data may include not only user basic information, but also user behavior data and/or user health data, so that the electronic device can identify the first user's user health risk based on the first user's personalized information Factors, formulate corresponding intervention plans, and promote the achievement of users' active health management goals.
  • the basic user information includes age and/or gender; the user behavior data includes exercise data, stress data, sleep data, diet data, drinking data, and smoking data. At least one; the user health data includes at least one of weight data, body composition data, blood pressure data, blood sugar data, and blood fat data.
  • the user data includes data related to various aspects of the user's personal behavior and health characteristics, which improves the accuracy of the identified user's health risk factors and also improves the effectiveness of the formulated first intervention plan.
  • the electronic device identifies the user's health risk factors according to the user data, specifically including: the electronic device obtains information related to the user's health risk factors from the correspondence between multiple groups of risk factors The risk factor correspondence of the first group corresponding to the basic information, wherein the age range and/or gender corresponding to the risk factor correspondence of different groups are different; the risk factor correspondence of the first group includes one or more health risk factors and their corresponding The corresponding relationship between the preset conditions, including the corresponding relationship between the first health risk factor and the first preset condition; the electronic device combines the first group according to the user behavior data and/or the user health data Corresponding relationship of risk factors to determine the user health risk factors, wherein, when the user behavior data and/or the user health data meet the first preset condition, the user health risk factors include the first health risk factor.
  • the risk factor correspondence of a group includes the correspondence between one or more health risk factors and their corresponding preset conditions, for example, it may include the first health risk factor and the first preset condition
  • the correspondence between the second health risk factor and the second preset condition may also be included.
  • the electronic device may determine that the first health risk factor is one of the user health risk factors of the first user; and
  • the electronic device can determine that the second health risk factor is another health risk among the user health risk factors of the first user factor.
  • the electronic device may determine that the second health risk factor is not one of the user health risk factors of the first user. health risk factors.
  • the first user's own user health risk factors can be identified effectively, accurately and more comprehensively, and good conditions are provided for accurate health management of the first user.
  • the preset condition corresponding to the health risk factor may include more subdivided sub-preset conditions, To present the degree of risk of its corresponding health risk factors.
  • the electronic device determines the user's health risk factors according to the user behavior data and/or the user health data in combination with the risk factor correspondence of the first group, which may specifically include: the electronic device determines the user's health risk factors according to the user behavior data and/or the The user's health data, combined with the corresponding relationship of the risk factors of the first group, determines the user's health risk factors and the exposure level of one or more health risk factors in the user's health risk factors.
  • the electronic device generates the first intervention plan according to the user's health risk factors and the exposure level of the one or more health risk factors.
  • the exposure level of one or more of the health risk factors can be determined, so as to more accurately determine which health risk factors have the greatest impact on the user's current health. Taking the exposure level of health risk factors as a reference for generating intervention plans can generate more targeted intervention plans for health risk factors with higher risks, and improve the intervention effect of the generated first intervention plan.
  • the electronic device predicts the predicted value of the health index after part and/or all of the first intervention plan is completed, specifically including: the electronic device predicts the health index according to the first user's Model training is performed on the individual training data to obtain the first individual health benefit prediction model; the individual training data of the first user includes: the implementation of the first user's historical intervention plan, the health indicators of the first user's historical intervention plan before execution value and the value of the health index after the implementation of part and/or all of the first user history intervention plan; the electronic device inputs the user health data in the user data and part and/or all of the first intervention plan into the first user The individual health benefit prediction model is used to predict and obtain the predicted value of the health index after part and/or all of the first intervention plan is completed.
  • the first individual health benefit prediction model is obtained through machine learning algorithm training, and then the model is used to predict the predicted value of the health indicators after part and/or all of the first intervention plan is completed. No human effort is required during the prediction process.
  • the intervention improves the efficiency of obtaining the predicted value of the health index, and also improves the accuracy of the obtained predicted value of the health index.
  • the electronic device uses a machine learning algorithm for model training to obtain the first individual health benefit prediction model in a variety of ways:
  • the electronic device may first acquire the group basic model of the first group corresponding to the basic user information in the user data; the group basic model of the first group is one of the group basic models of multiple groups, wherein different groups The corresponding age ranges and/or genders are different; for any group basic model in the group basic models of the multiple groups: the group basic model is the group training data of the group corresponding to the group basic model, which is obtained by training with a machine learning algorithm
  • the group training data of the corresponding group includes: the implementation of the user intervention plan of the group corresponding to the basic model of the group, the value of the health indicator before the implementation of the intervention plan, and the value of the health indicator after the implementation of some and/or all of the intervention plan.
  • the electronic device can train and optimize the group basic model of the first group according to the individual training data of the first user to obtain the first individual health benefit prediction model.
  • the first individual health benefit prediction model exclusive to the first user is obtained, and the first individual health benefit prediction model is used to predict the The predicted value of the health index after the completion of part and/or all of the first intervention plan of the first user, so that the privacy of the training data for training the health benefit prediction model can be guaranteed, and the prediction accuracy of the health benefit prediction model can also be improved. sex.
  • the electronic device may also directly use the first user's individual training data to perform model training using a machine learning algorithm to obtain the first individual health benefit prediction model, which is not limited here.
  • the health indicator includes at least one of body weight, body mass index, body fat percentage, systolic blood pressure, diastolic blood pressure, fasting blood sugar, total cholesterol, and triglycerides.
  • the health indicators may include various indicators related to the user's health, which improves the comprehensiveness and effectiveness of the user's health management.
  • the method further includes: the electronic device displaying predicted values of health indicators after part and/or all of the first intervention plan is completed.
  • the predicted value of the health index after completing the first intervention plan is displayed to the user, which improves the user's confidence and subjective initiative in implementing the intervention plan, and promotes the achievement of the user's active health management goal.
  • the first intervention plan includes N cycles of intervention plans, where N is a positive integer greater than 1; part and/or all of the first intervention plan are completed
  • the predicted value of the post-health index includes the predicted value of the health index after part and/or all of the N cycles of the intervention plan in the first intervention plan are completed;
  • the electronic device displays a part of the first intervention plan and/or or the predicted value of the health index after the completion of all, specifically includes: the electronic device displays the change trend of the health index after the completion of the first intervention plan, and the change trend of the health index is obtained from the prediction of the N cycles of the first intervention plan Consists of predicted values of health indicators after some and/or all cycles of the intervention plan are completed, respectively.
  • the first intervention plan includes intervention plans of multiple cycles
  • the electronic device can predict the predicted values of the health indicators after the completion of the intervention plans of multiple cycles in the first intervention plan and use the change trend
  • the method is displayed to the user, which can improve the user's confidence and subjective initiative in implementing the intervention plan, and promote the achievement of the user's active health management goal.
  • the health management system where the electronic device is located also includes: smart wearable devices, and/or health detection equipment, and/or smart fitness equipment; the method also includes: The electronic device sends the wearable intervention sub-plan in the first intervention plan to the smart wearable device; the wearable intervention sub-plan is part or all of the first intervention plan; the electronic device sends the wearable intervention sub-plan in the first intervention plan
  • the detection intervention sub-plan is sent to the health detection device; the detection intervention sub-plan is part or all of the first intervention plan; the electronic device sends the fitness intervention sub-plan in the first intervention plan to the smart phone Fitness equipment; the fitness intervention sub-plan is part or all of the first intervention plan; the wearing intervention sub-plan, the detection intervention sub-plan, and the fitness intervention sub-plan are the same or different.
  • the wearable intervention subplan is a plan to be executed by the smart wearable device in the first intervention plan;
  • the detection intervention subplan is a plan to be executed by the health detection device in the first intervention plan;
  • the fitness intervention subplan is A plan to be executed by the smart fitness equipment in the first intervention plan.
  • the electronic device can send some or all of the various types of intervention plans in the first intervention plan to corresponding other devices in the health management system, for example, the wearable intervention child in the first intervention plan
  • the plan is issued to the smart wearable device
  • the detection intervention sub-plan in the first intervention plan is issued to the health detection device
  • the fitness intervention sub-plan in the first intervention plan is issued to the smart fitness equipment, etc., so that All devices in the health management system can work together to promote the achievement of the user's active health management goals.
  • the first intervention plan includes a first exercise plan, and/or a first diet plan, and/or a first set of health habits check-in tasks; the wearing intervention sub The plan includes part or all of the first exercise plan, and/or part or all of the first diet plan, and/or part or all of the first health habit check-in task set; the detection intervention sub-plan Including part or all of the health index detection tasks in the first health habit check-in task set; the fitness intervention sub-plan includes part or all of the first exercise plan.
  • the first intervention plan may include plans related to various aspects of the user's health, such as exercise plans, diet plans, health habits check-in task sets, etc., and these various aspects of plans may be part or all of the Included in the intervention plan sent to other devices in the health management system, it improves the effectiveness of each device in the health management system to cooperate to promote the user's active health management goals.
  • the first intervention plan includes N cycles of intervention plans, where N is a positive integer greater than 1; the wearable intervention sub-plan is the first intervention plan A wearing intervention sub-plan for one cycle or all cycles; the detection intervention sub-plan is a detection intervention sub-plan for one cycle or all cycles in the first intervention plan; the fitness intervention sub-plan is a cycle in the first intervention plan , or the entire cycle of fitness intervention sub-plans.
  • the intervention plan delivered to other devices in the health management system may be a periodic intervention plan, which can reduce the amount of data sent by the electronic device and reduce the power consumption of the electronic device. It is also possible to issue the intervention plan for the entire cycle, which can ensure that when the electronic device is disconnected from the network of other devices, other devices can also complete the intervention plan for the entire cycle, which improves the anti-interference performance of the health management system.
  • the first intervention plan includes N cycles of intervention plans, where N is a positive integer greater than 1; the method further includes: the electronic device acquiring the first The actual execution data and/or the value of the health index during the execution of the first cycle of the intervention plan; equipment monitoring.
  • the electronic device can monitor itself or other devices in the health management system to obtain the actual execution data and/or the value of the health index during the first cycle of the first intervention plan, in order to accurately monitor the health of the user.
  • Management provides data support.
  • the actual execution data and/or the value of the health index during the execution of the first cycle in the first intervention plan includes the value of the health index after the execution of the first cycle in the first intervention plan is completed.
  • other devices in the health management system where the electronic device is located include: smart wearable devices, and/or health detection devices, and/or smart fitness equipment; the electronic device Obtaining the actual execution data and/or the value of the health indicator during the execution of the first cycle of the first intervention plan specifically includes: the electronic device monitoring the first part of the execution of the first cycle of the first intervention plan on the device The actual execution data and/or the value of the first part of the health index; the electronic device receives the second part of the actual execution data during the execution of the first cycle of the first intervention plan monitored by the smart fitness equipment; the electronic device receives the health detection The value of the second part of the health index during the execution of the first cycle of the first intervention plan measured by the device; the electronic device receives the third part of the first cycle of the first intervention plan monitored by the smart wearable device The actual execution data and/or the value of the third part of the health index; the electronic device uses the first part of the actual execution data, the second part of the actual execution data and
  • other devices in the health management system may include smart wearable devices, and/or health detection devices, and/or smart fitness equipment, and the devices in the health management system jointly promote the user to complete the intervention plan, effectively improving the user's Likelihood of achieving proactive health management goals.
  • the predicted value of the health indicator after the completion of part and/or all of the first intervention plan includes the predicted value of the health indicator after the completion of the first cycle in the first intervention plan
  • the method also includes: the electronic device compares the predicted value of the health index after the completion of the first cycle in the first intervention plan with the value of the health index after the actual completion of the first cycle of the intervention plan, to obtain an evaluation of the intervention effect result.
  • the predicted value of the health index can be compared with the value of the health index after the actual completion of the first cycle of the intervention plan to obtain the evaluation result of the intervention effect, which can be more accurate The evaluation of the effect of the first intervention program.
  • the method further includes: based on the evaluation result of the intervention effect, the electronic device combines the actual execution data and/or the value of the health indicator during the execution of the first cycle, An evaluation of one or more of the first intervention plans is generated as a result of the evaluation of the first intervention plan.
  • the evaluation result of the first intervention plan may be generated in combination with the actual execution data and/or the value of the health index, so as to more accurately evaluate whether the first intervention plan is suitable for the first user.
  • the method further includes: the electronic device adjusts the intervention plan of the second cycle in the first intervention plan according to the evaluation result of the first intervention plan, and the first intervention plan The second cycle is the next cycle of the first cycle.
  • the intervention plan for the next period in the first intervention plan can be adjusted to realize a closed-loop health management, improve user experience, and achieve long-term health promotion.
  • the method further includes: the electronic device predicts a part of the adjusted first intervention plan and/or Or the predicted value of the health indicator after all is done.
  • the method further includes: the electronic device displays an adjusted change trend of health indicators after part and/or all of the first intervention plan is completed.
  • the electronic device can predict the predicted value of some and/or all completed health indicators of the adjusted first intervention plan and display it to the user, thereby improving the user's ability to continue to implement the adjusted first intervention plan.
  • the confidence and subjective initiative of the user promote the achievement of the user's active health management goals.
  • the method further includes: the electronic device sends part and/or all of the adjusted first intervention plan to the health management system where the electronic device is located other equipment.
  • part and/or all of the adjusted first intervention plan may be sent to other devices in the health management system where the electronic device is located, so as to update the
  • the stored first intervention plan enables each device in the health management system to promote the achievement of the user's active health management goal according to the latest first intervention plan, improving the effect of health management.
  • the method further includes: the electronic device evaluates the age of the first user according to the user data, as the user evaluation age of the first user; the user data includes User behavior data and/or user health data; the electronic device predicts a predicted user assessment age of the first user after part and/or full completion of the first intervention plan.
  • the electronic device can obtain the user evaluation age of the first user according to the user data evaluation, and can predict the first user's estimated user evaluation age after the user completes part and/or all of the first intervention plan, which can be concise reflect the overall health risk status of the user.
  • the method further includes: displaying, by the electronic device, the user's estimated age and the predicted user's estimated age.
  • the electronic device may display the user's estimated age and the predicted user's estimated age to the user, so as to improve the user's subjective initiative in implementing the first intervention plan, and promote the achievement of the user's active health management goal.
  • the method further includes: determining the baseline death probability of each cause-of-death disease of the first group corresponding to the basic user information in the user data without risk exposure; Among them, the risk-free exposure situation refers to the situation where everyone in the group is assumed to have no health risk factors; the group life expectancy of the first group is determined; Determine the user's life expectancy based on the baseline death probability of a cause-of-death disease; determine the user's estimated age of the first user according to the user's expected life, the group's life expectancy of the first group, and the actual age of the first user.
  • the process of determining the user's estimated age not only the user's specific health risk factors and actual age are referred to, but also the group life expectancy of the first group to which the first user belongs, so that the estimated The user-assessed age of the first user can more accurately reflect the overall health status of the user.
  • the present application provides a method for evaluating an intervention plan, the method comprising: the electronic device obtains the predicted value of the health index after part and/or all of the first intervention plan is completed; the electronic device obtains the first intervention plan execution Actual execution data and/or values of health indicators during the process; the electronic device compares the predicted value of the health indicators after the completion of part and/or all of the first intervention plan with the actual completion of part and/or all of the first intervention plan According to the coincidence degree of the value of the final health index, the evaluation result of the intervention effect is obtained.
  • the predicted value of the health index can be compared with the value of the health index after the actual completion of the first cycle of the intervention plan to obtain the evaluation result of the intervention effect, which can be more accurate The evaluation of the effect of the first intervention program.
  • the first intervention plan includes N cycles of intervention plans, where N is a positive integer greater than 1; after part and/or all of the first intervention plan is completed
  • the predicted value of the health indicator includes the predicted value of the health indicator after the completion of the first cycle in the first intervention plan; the electronic device obtains the actual execution data and/or the value of the health indicator during the execution of the first intervention plan, specifically including :
  • the electronic device obtains the actual execution data and/or the value of the health index during the execution of the first cycle in the first intervention plan; the actual execution data and/or the value of the health index are determined by the electronic device and/or the electronic device Obtained by monitoring of other equipment in the health management system; the electronic device compares the predicted value of the health indicator after the first intervention plan is partially and/or fully completed with the value of the health indicator after the actual completion of the first intervention plan.
  • the electronic device compares the predicted value of the health index after the completion of the first cycle in the first intervention plan with the value of the health index after the actual completion of the first cycle of the intervention plan. , to obtain the evaluation result of the intervention effect.
  • other devices in the health management system where the electronic device is located include: smart wearable devices, and/or health detection devices, and/or smart fitness equipment.
  • other devices in the health management system may include smart wearable devices, and/or health detection devices, and/or smart fitness equipment, and the devices in the health management system jointly promote the user to complete the intervention plan, effectively improving the user's Likelihood of achieving proactive health management goals.
  • the method further includes: the electronic device combines the actual execution data and/or the value of the health indicator during the execution of the first cycle based on the evaluation result of the intervention effect, An evaluation of one or more of the first intervention plans is generated as a result of the evaluation of the first intervention plan.
  • the evaluation result of the first intervention plan may be generated in combination with the actual execution data and/or the value of the health index, so as to more accurately evaluate whether the first intervention plan is suitable for the first user.
  • the method further includes: the electronic device adjusts the intervention plan of the second cycle in the first intervention plan according to the evaluation result of the first intervention plan, and the first intervention plan The second cycle is the next cycle of the first cycle.
  • the intervention plan for the next period in the first intervention plan can be adjusted to realize a closed-loop health management, improve user experience, and achieve long-term health promotion.
  • the electronic device compares the predicted value of the health indicator after the completion of the first cycle in the first intervention plan with the health indicator after the actual completion of the first cycle of the intervention plan
  • the degree of coincidence of the value, to obtain the evaluation result of the intervention effect specifically includes: the electronic device compares the predicted value of the health index after the completion of the first cycle in the first intervention plan with the value of the health index after the actual completion of the first cycle of the intervention plan degree of coincidence, and the degree of coincidence between the predicted user assessment age after the completion of the first cycle in the first intervention plan and the user assessment age after the actual completion of the first cycle of the intervention plan, to obtain the intervention effect evaluation result.
  • the user evaluation age parameter can be added in the intervention effect evaluation process, since the user evaluation age can reflect the user's health status as a whole, so this can make the intervention effect evaluation result more accurate.
  • the method further includes: the electronic device acquiring the first intervention plan generated according to the user data of the first user; the electronic device acquiring a part of the first intervention plan and And/or the predicted value of the health index after all completion, specifically includes: the electronic device predicts the predicted value of the health index after part and/or all of the first intervention plan is completed.
  • the electronic device can generate the first intervention plan and predict the predicted value of the health index after part and/or all of the intervention plan is completed, which improves the user's confidence and subjective initiative in implementing the intervention plan, and promotes the user's initiative. Achievement of health management goals.
  • the present application provides a method for evaluating a user's age, including: an electronic device obtains user data of a first user; the user data includes user behavior data and/or user health data; the user data, evaluate the age of the first user as the user evaluation age of the first user.
  • the electronic device can obtain the user-assessed age of the first user according to the user data evaluation, which can succinctly reflect the overall health risk status of the user.
  • the electronic device evaluates the age of the first user according to the user data of the first user, and as the user evaluation age of the first user, specifically includes: the electronic device The device identifies the user's health risk factors according to the user data; the electronic device evaluates the age of the first user according to the user data and the user's health risk factors, and serves as the user-assessed age of the first user.
  • the electronic device needs to first identify user health risk factors, so that the obtained user-assessed age can more accurately reflect the overall health level of the user.
  • the method further includes: the electronic device generates a first intervention plan for the user's health risk factors; the electronic device predicts a part of the first intervention plan and/or The predicted user evaluation age of the first user after all are completed.
  • the electronic device can predict the predicted user assessment age of the user after the completion of the first intervention plan, which improves the user's subjective initiative in implementing the intervention plan, and promotes the achievement of the user's active health management goal.
  • the electronic device evaluates the age of the first user according to the user data and the user's health risk factors, as the user's estimated age of the first user, specifically including: Determine the baseline death probability of each cause-of-death disease of the first group corresponding to the basic user information in the user data under the condition of no risk exposure; wherein, the condition of no risk exposure refers to the assumption that everyone in the group has no health risk factors Determine the group life expectancy of the first group; determine the user's life expectancy based on the user's health risk factors and the baseline death probability of each cause of death of the first group in the case of no risk exposure; determine the user's life expectancy according to the user's life expectancy, the The group life expectancy of the first group and the actual age of the first user determine the user-assessed age of the first user.
  • the process of determining the user's estimated age not only the user's specific health risk factors and actual age are referred to, but also the group life expectancy of the first group to which the first user belongs, so that the estimated The user-assessed age of the first user can more accurately reflect the overall health status of the user.
  • an embodiment of the present application provides an electronic device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program codes,
  • the computer program code includes computer instructions, and the one or more processors call the computer instructions to make the electronic device execute the first aspect and any possible implementation of the first aspect, the second aspect and any of the second aspects.
  • the present application provides a chip system, which is applied to an electronic device, and the chip system includes one or more processors, and the processor is used to invoke computer instructions so that the electronic device executes the electronic device according to the first aspect and The method described in any possible implementation manner of the first aspect, the second aspect and any possible implementation manner of the second aspect, the third aspect and any possible implementation manner of the third aspect.
  • the present application provides a computer program product containing instructions.
  • the above-mentioned computer program product is run on an electronic device, the above-mentioned electronic device is made to execute the first aspect and any possible implementation manner in the first aspect, The method described in the second aspect and any possible implementation manner of the second aspect, the third aspect and any possible implementation manner of the third aspect.
  • the present application provides a computer-readable storage medium, including instructions.
  • the above-mentioned instructions When the above-mentioned instructions are run on the electronic device, the above-mentioned electronic device executes the first aspect and any possible implementation manner in the first aspect, The method described in the second aspect and any possible implementation manner of the second aspect, the third aspect and any possible implementation manner of the third aspect.
  • the present application provides a user health management system
  • the health management system includes at least one of smart wearable devices, health detection devices or smart fitness equipment and the electronic device; the electronic device is used to perform the first
  • the electronic device provided by the fourth aspect the chip system provided by the fifth aspect, the computer program product provided by the sixth aspect, the computer storage medium provided by the seventh aspect, and the user health management system provided by the eighth aspect all use
  • the beneficial effects that it can achieve can refer to the beneficial effects in the corresponding method, and will not be repeated here.
  • Figure 1 is a disease risk assessment method provided by the embodiment of this application.
  • Figure 2 is a health management method provided by the embodiment of this application.
  • Fig. 3 is an exemplary schematic diagram of a hardware composition scenario of the health management system provided by the embodiment of the present application.
  • Fig. 4 is a schematic diagram of an exemplary information interaction of the health management method in the embodiment of the present application.
  • Fig. 5 is an exemplary schematic diagram of examples of information categories and corresponding collection methods in various types of user data in the embodiment of the present application;
  • Fig. 6 is a schematic diagram of an exemplary information flow during the process of generating user health risk factors in the embodiment of the present application
  • Fig. 7 is an exemplary flowchart of a method for identifying user health risk factors in the embodiment of the present application.
  • Figure 8 is an exemplary schematic diagram of a screening method for health risk factors in the embodiment of the present application.
  • Fig. 9 is a schematic diagram of an exemplary information flow for generating a personalized intervention plan in the embodiment of the present application.
  • Fig. 10 is an exemplary schematic diagram of the type division in the intervention plan of the embodiment of the present application.
  • Figure 11 is a schematic diagram of an exemplary information flow of the revenue forecasting model training process in the embodiment of the present application.
  • Fig. 12 is an exemplary schematic diagram of the training process of the population basic model in the embodiment of the present application.
  • Fig. 13 is a schematic flow chart of the method for forecasting the benefits of the intervention plan in the embodiment of the present application.
  • Fig. 14 is an exemplary schematic diagram of sending different types of intervention plans to different devices in the embodiment of the present application.
  • Fig. 15 is an exemplary information flow diagram of a process of generating intervention plan effect evaluation in the embodiment of the present application.
  • Fig. 16 is a schematic diagram of an intervention plan effect evaluation process in the embodiment of the present application.
  • FIG. 17 is an exemplary schematic diagram of information flow during the process of evaluating the user's age in the embodiment of the present application.
  • Fig. 18 is a schematic flowchart of a method for evaluating user age in an embodiment of the present application.
  • Fig. 19 is a schematic diagram of information flow of an intervention plan effect evaluation in the embodiment of the present application.
  • Fig. 20 is another schematic flowchart of the method for forecasting the benefits of the intervention plan in the embodiment of the present application.
  • Fig. 21 is an exemplary information flow diagram of another process of generating intervention plan effect evaluation in the embodiment of the present application.
  • Figure 22 is a schematic flow chart of another intervention plan effect evaluation method in the embodiment of the present application.
  • Fig. 23 is a schematic diagram of an exemplary software module architecture of the health management system in the embodiment of the present application.
  • Fig. 24 is a schematic diagram of an exemplary hardware structure of an electronic device in an embodiment of the present application.
  • first and second are used for descriptive purposes only, and cannot be understood as implying or implying relative importance or implicitly specifying the quantity of indicated technical features. Therefore, the features defined as “first” and “second” may explicitly or implicitly include one or more of these features. In the description of the embodiments of the present application, unless otherwise specified, the “multiple” The meaning is two or more.
  • Health management involves many fields, such as health risk assessment, health intervention, health data monitoring, etc. There are also many different ways to implement health management:
  • disease prevention and control guidelines provide risk factor items and risk assessment scales related to single diseases, which provide a basis for the developed single disease risk assessment tools. basis.
  • this health management method lacks a risk identification and evaluation method for the user's overall health status.
  • Single-disease risk assessment cannot be associated with various risk factors related to normal causes of death, and the impact on death cannot be directly obtained, and this assessment lacks the measurement of the overall health risk level.
  • the disease risk assessment method shown in Figure 1 it mainly evaluates and predicts the risk of chronic diseases that affect health.
  • the specific process is: obtain health big data through longitudinal research, organize the data, and The name defines the threshold range of each index of the disease, and at the same time, according to the threshold range of each index of the disease, use statistical analysis software to establish a queue corresponding to the disease.
  • each variable was selected using the Cox univariate regression analysis method, and finally the variables included in the Cox regression model were subjected to multivariate regression analysis to construct a Cox proportional hazards model.
  • the Cox proportional hazards model was internally validated and externally validated to obtain a disease risk identification and prediction model. Input new individual signs and basic information, and generate disease risk assessment results based on the disease prediction model and disease risk assessment hyperbola.
  • the risk assessment model and method for a single disease is based on the risk level and risk factor items obtained for the single disease.
  • the pathogenesis and influencing factors of various diseases are very different.
  • the assessment results can be used for cardiovascular and cerebrovascular diseases and diabetes Chronic diseases with a high rate play a preventive role, but the evaluation results cannot reflect the overall health status of the user and the complete set of risk items that affect health, nor can it reflect the impact of risk factors on the user's healthy life span, which will lead to a negative impact on the user's overall health status. Assessments are inaccurate, leading to inaccurate or inappropriate interventions.
  • Health assessment and risk assessment first obtain the basic information of the user, collect the user's discomfort symptoms, exercise habits, daily physical activity and lifestyle data through the standard questionnaire, and collect the necessary physical examination data (body composition, blood pressure, blood sugar, Blood lipids), assess the individual's disease risk level through the disease risk assessment model, and identify disease risk factors.
  • Health intervention According to the risk of chronic disease of the assessment object and the risk factors corresponding to the risk of high-risk groups of diseases, for controllable risk items (mostly bad lifestyle and behavioral habits), formulate personalized intervention measures and implementation plans, Such as exercise plan (mode, frequency, intensity, time).
  • Execution monitoring and feedback Push the plan to the evaluation object, monitor the implementation of the plan through wearable device monitoring or user subjective feedback, and track the changes in daily detectable signs.
  • This health management method can only formulate an intervention plan based on the previous user input and related information, and cannot accurately and dynamically adjust the next step intervention plan, resulting in poor overall intervention effect, or the intervention plan formulated later cannot be implemented.
  • This health management method mainly focuses on the management of chronic diseases, but in this case some structural lesions are irreversible, and the intervention effect of the intervention plan adopted in these populations will be greatly reduced.
  • This application provides another health management method, which can help users assess the health risk factors currently exposed based on user sign data and daily behavior data, evaluate the user's overall health risk factors, and provide controllable risk items that are closely related to individual health.
  • the health management method of the present application is applied to a health management system.
  • FIG. 3 it is an exemplary schematic diagram of a hardware composition scenario of the health management system.
  • the exemplary health management system 300 may include: an electronic device 301 (such as a mobile phone, a tablet computer, etc.), a smart wearable device 302 (such as a smart bracelet, a smart watch, etc.), a health detection device 303 (such as a body fat scale, blood pressure meter, blood glucose meter, blood lipid detector, etc.), smart fitness equipment 304 (for example, smart treadmill, smart rowing machine, etc.), the health management system may include cloud 305 composed of cloud infrastructure (such as cloud server, etc.), wherein:
  • Electronic equipment 301 mainly responsible for collecting basic user information, health questionnaire information and behavioral data, synchronizing data from other devices in the health management system, completing storage and processing; and responsible for user health risk assessment, intervention plan generation, health benefit prediction and execution effect evaluation, and the data authorized by the user can also be uploaded to the cloud 305;
  • Smart wearable device 302 mainly responsible for the monitoring and collection of user exercise, sleep, and stress data;
  • Health detection equipment 303 mainly responsible for the detection and collection of user health indicators (body composition, blood pressure, blood sugar, blood lipid);
  • Smart fitness equipment 304 It can receive the exercise program sent by the electronic device 301 and execute it automatically. After the execution is completed, it can return the exercise data to the electronic device 301;
  • Cloud 305 Mainly responsible for the training of the basic model of health benefit prediction population, the management and update of the intervention knowledge base, etc.
  • the health management system 300 may include more or less devices, which is not limited here.
  • FIG. 4 it is a schematic diagram of an exemplary information interaction of the health management method in the embodiment of the present application.
  • the health management method in the embodiment of this application can be divided into 6 stages:
  • Evaluation stage including steps S401-S402: based on the user data, the electronic device 301 automatically evaluates and identifies the controllable health risk factors that the user is currently exposed to from the overall health risk factors, and obtains the user's health risk factors;
  • Intervention plan generation stage including step S403: the electronic device 301 generates multiple cycles of individualized intervention plans for the identified health risk factors of the user. For example, a 3-month intervention plan can be generated, wherein every 7-day intervention plan is a cycle.
  • step S404 the electronic device 301 predicts the change trend of health indicators after the completion of the intervention plan according to the income preset model and displays it to the user;
  • Intervention implementation stage including step S405: the electronic device 301 issues the intervention plan to other devices in the health management system 300, and executes the intervention plan;
  • each device in the health management system 300 monitors the actual execution data and/or the value of health indicators during the execution of the intervention plan, and summarizes them to the electronic device 301;
  • Effect evaluation and intervention plan adjustment stage including steps S411-S412: the electronic device 301 evaluates the implementation effect of the intervention plan in this cycle, and adjusts the intervention plan in the next cycle accordingly.
  • Each device in the health management system collects user data and collects it into the electronic device
  • user data can be classified into:
  • Basic user information data including basic user deterministic information
  • User behavior data including data related to user behavior
  • User health data includes data related to the user's health status.
  • Fig. 5 is an exemplary schematic diagram of examples of information categories in various types of user data and corresponding collection methods in the embodiment of the present application.
  • the basic user information may include the user's date of birth (age) and/or gender. In some embodiments, the basic user information may also include information such as height, which is not limited here.
  • the basic user information can be directly manually entered into the electronic device 301 by the user; etc. to identify and extract these basic user information, which is not limited here.
  • the user behavior data may include at least one type of data in the user's exercise data 521 , stress data 522 , sleep data 523 , diet data 524 , drinking data 525 and smoking data 526 .
  • the exercise data can also be recorded by the smart fitness equipment, and sent to the electronic device 301 online or manually entered into the electronic device 301 by the user.
  • exercise data can include data on exercise duration, number of exercise steps, exercise distance, exercise heart rate, exercise intensity level, physical activity level, etc.:
  • an electronic device 301 with a positioning function or a smart wearable device 302 with an accelerometer and a gyroscope sensor can collect the user's exercise duration, number of exercise steps, and exercise distance;
  • the smart wearable device 302 with photoplethysmography (photo plethysmography, PPG) heart rate monitoring function can collect the user's resting heart rate and exercise heart rate HRe.
  • PPG photo plethysmography
  • HRe exercise heart rate
  • PPG is a simple optical technique used to detect the volume change of blood in the peripheral vascular circulation. This is a low-cost and non-invasive method that can be measured on the surface of the skin. Widely used in heart rate monitoring of wearable devices.
  • the electronic device 301 or the smart wearable device 302 can estimate the user's maximum heart rate HRmax by subtracting the age from 220, according to the The user's maximum heart rate HRmax and exercise heart rate HRe can calculate the user's exercise intensity level: whether it belongs to low-intensity exercise, medium-intensity exercise or high-intensity exercise.
  • a calculation and grading method can be: low-intensity exercise: HRe ⁇ 65%*HRmax; medium-intensity exercise: 65%*HRmax ⁇ HRe ⁇ 75%*HRmax; high-intensity exercise: HRe>75%*HRmax .
  • the electronic device 301 or the smart wearable device 302 can obtain the user's exercise volume and physical activity level in the last 7 days (Low Medium High).
  • the user's daily stress value can be collected through the smart wearable device 302 .
  • the smart wearable device 302 based on PPG signal analysis technology or the electronic device 301 based on ultrasonic respiratory signal analysis technology can collect sleep staging and sleep quality data.
  • the electronic device 301 or the smart wearable device 302 can also receive the sleep time recorded manually by the user at night, and by comparing the sleep time recorded manually by the user with the user's sleep time recognized by the device itself, it can be judged whether the user has difficulty falling asleep or has a bad habit of going to bed late .
  • the sleep data may also be manually entered by the user into the electronic device 301 by the user.
  • It can be generated by the user taking photos while eating or drinking, or can be manually entered into the electronic device 301 by the user based on his own eating conditions.
  • the electronic device 301 recognizes and collects dietary intake information through manual entry or photographing by the user, which may include: food type, intake, and the like.
  • the electronic device 301 can also obtain the nutritional elements ingested by the user through the ratio of nutritional elements of each food material in the diet library.
  • the electronic device 301 may be manually entered by the user after drinking or smoking.
  • the electronic device 301 may collect users' smoking and drinking habits (frequency and average amount each time) in the form of questionnaires.
  • the user's health data may include at least one of body weight, body composition, blood pressure, blood sugar, and blood lipid.
  • These user health data can be recorded by the health detection device 303 and then sent to the electronic device 301 online, or manually entered by the user into the electronic device 301, which is not limited here.
  • users can use a smart body fat scale to collect weight and body composition data every week.
  • electronic device 301 can also divide body weight (kg) by height square (m2) to obtain BMI; ⁇ 24), overweight (24 ⁇ BMI ⁇ 28), obese (BMI>28).
  • users can use a smart blood pressure monitor to collect systolic and diastolic blood pressure every day to obtain blood pressure data.
  • users can use a smart blood glucose meter to collect fasting blood glucose FPG every day to obtain blood glucose data.
  • users can collect blood lipid indicators (total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol) with a blood lipid detector every week to obtain blood lipid data.
  • blood lipid indicators total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol
  • the user can manually enter or upload a picture of the latest medical examination report (identified by OCR technology) to the electronic device 301, and the electronic device 301 can obtain the user's medical examination information.
  • the electronic device 301 may also collect the user's personal disease history, disease family history, etc. in the form of questionnaires.
  • the manual input of data by the user into the electronic device 301 is not limited to the form in which the user directly inputs relevant data, and may also include the electronic device 301 displaying relevant data options for the user to select, and the electronic device 301 displaying questionnaires.
  • the form for the user to fill in, the electronic device 301 to record the user's check-in situation through the check-in task, etc., requires the user to manually intervene in the operation so that the electronic device 301 obtains relevant data, which is not limited here.
  • smart wearable device 302 After other devices in the health management system (smart wearable device 302, health detection device 303 and/or smart fitness equipment 304) have collected relevant user data: user behavior data and user health data, they can be connected to the Internet (for example, through Bluetooth connection or wireless network) and upload and synchronize the collected data to the electronic device 301.
  • the Internet for example, through Bluetooth connection or wireless network
  • the electronic device identifies user health risk factors according to the collected user data
  • the electronic device 301 After the electronic device 301 obtains the user data collected by each device in the health management system 300, it can identify the user's health risk factors related to the user's lifestyle and controllable based on the user data and the complete set of risk factors affecting health.
  • FIG. 6 is a schematic diagram of an exemplary information flow during the process of generating user health risk factors in the embodiment of the present application
  • FIG. 7 is an exemplary flow chart of a method for identifying user health risk factors in an embodiment of the present application.
  • the user health risk factor identification method includes:
  • Steps SS701-SS703 are used for data preparation for identification of user health risk factors; in some embodiments of the present application, the sub-group database can also be referred to as the corresponding relationship of risk factors of multiple groups.
  • Steps SS704 to SS705 are used to determine user health risk factors based on the subgroup database and collected user data.
  • the population health and disease burden database can be a database released by some large international or domestic organizations to display the global or a country's population health and disease burden, for example, it can be the Global Burden of Disease (Global Burden of Disease, GBD) database, or some similar other databases.
  • GBD Global Burden of Disease
  • GBD database Take the GBD database as an example: Specifically, all risk factors for death-related diseases can be obtained based on the GBD database query, a total of more than 60, plus sleep, stress and other common factors that may affect health that are not counted in the database, Get the complete collection of risk factors that affect your health.
  • controllable risk factor items related to lifestyle and daily behavior will be selected in the end (it can be called a set of controllable risk factors related to life and behavior), which can at least include: exercise in the last 7 days, daily dietary intake (fruit, Vegetables, red meat, grains, salt), daily alcohol consumption, daily smoking, as well as body fat percentage, systolic blood pressure, diastolic blood pressure, fasting blood sugar, blood lipid indicators.
  • step SS702 There are many specific screening methods for step SS702. For example, it can be determined based on many different standards or rules which risk factors are controllable, or it can be determined based on many different standards or rules which risk factors are related to lifestyle and related to daily behavior.
  • Fig. 8 is an exemplary schematic diagram of a screening method of health risk factors in the embodiment of the present application.
  • the set A of prominent risk factors in the Chinese population can be selected from all the monitored risk factors in the GBD database, and at the same time, the risk factors with research literature data that can support the calculation of relative risk can be selected from all the monitored risk factors set B of .
  • set C of risk factors that users can control daily can be obtained. From the set C, some risk factors related to the user's lifestyle and daily behavior are screened out as controllable risk factors related to life and behavior.
  • a health risk factor database can be established, and the corresponding relationship of the database can include: risk factor categories and category classification standards. Since the classification criteria of some of these risk factors are related to gender or age, the corresponding relationship of the database may also include gender and age. If different gender and age ranges are regarded as a different group, the health risk factor items and threshold database can also be called sub-group health risk factor items and threshold databases.
  • Table 1 it is an exemplary schematic table of the group health risk factor items and threshold database in the embodiment of the present application:
  • the above Table 1 is only an exemplary illustration of the sub-group health risk factor items and the threshold value database. In practical applications, other forms can be used to store the sub-group health risk factor items and the threshold value database.
  • the group health risk factor items and threshold database may store not the risk range values corresponding to the risk factors, but the health range values corresponding to the risk factors; in some embodiments, both the risk The dangerous range value corresponding to the factor also stores the healthy range value corresponding to the risk factor, which is not limited here.
  • the group health risk factor items and the threshold database can be stored in the cloud 305 or directly in the electronic device 301 , which is not limited here.
  • the health risk factor items and the threshold database may also be referred to as risk factor correspondences, and similarly, the sub-group health risk factor items and threshold databases may also be referred to as risk factor correspondences for multiple groups. Relationships in which risk factor correspondences for different groups correspond to different age ranges and/or sexes.
  • the risk factor correspondence of a group includes the correspondence between one or more health risk factors and their corresponding preset conditions. For example, in Table 1 above, the preset condition corresponding to the health risk factor "insufficient physical activity" is "the level of physical activity is "low”"; The preset condition corresponding to the health risk factor "high body fat percentage" is ">20%”.
  • the preset condition corresponding to the health risk factor may contain more subdivided sub-preset conditions to present its corresponding Risk level of health risk factors.
  • the preset condition corresponding to the health risk factor "high body fat percentage” is ">20%”.
  • the preset condition may also include 3 sub-preset conditions: mild: >20% and ⁇ 25%; common: >25% and ⁇ 28%; severe: >28%.
  • the preset condition corresponding to the health risk factor "insufficient daily vegetable intake” is "daily vegetable intake ⁇ 300 grams".
  • two sub-preset conditions may also be included: deficiency: 250 grams ⁇ daily vegetable intake ⁇ 300 grams; severe deficiency: daily vegetable intake ⁇ 250 grams.
  • the electronic device 301 can obtain the first group corresponding to the basic user information from the cloud 305 according to the basic user information collected in step S401 and the group health risk factor items and threshold database stored in the cloud 305. Health risk factor items and threshold database.
  • the electronic device 301 may, based on the basic user information collected in step S401, select the sub-group health risk factor items and the threshold database The health risk factor items and the threshold database of the first group corresponding to the user's basic information are determined in the database.
  • step S401 if the basic user information collected in step S401 is male, 40 years old, and 170 cm tall. And the sub-group health risk factor items and threshold database established in step SS703 are shown in Table 1 above, then the health risk factor items and threshold database of the first group corresponding to the basic information of the user acquired by the electronic device 301 can be as follows 2 shows:
  • step S401 if the basic user information collected in step S401 is female, 32 years old, and 150 cm tall. And the sub-group health risk factor items and threshold database established in step SS703 are shown in Table 1 above, then the health risk factor items and threshold database of the first group corresponding to the basic information of the user acquired by the electronic device 301 can be as follows 3 shows:
  • SS705. Determine the user's health risk factors according to the user behavior data, user health data, and the health risk factor items and threshold database of the first group.
  • the electronic device 301 After the electronic device 301 acquires the health risk factor items and the threshold database of the first group, it can determine the health risk factors that the user has been exposed to based on the user behavior data and user health data in the user data collected in step S401, namely User health risk factors.
  • the health risk factor items and threshold database of the first group of users are as shown in Table 2 above, and the physical activity level obtained from the user behavior data in the user data collected in step S401 is "low”, If it meets the risk value range of the risk factor of "insufficient physical activity”, then the risk factor of "insufficient physical activity” is a health risk factor to which the user has been exposed, and should be included in the user's user health risk factors.
  • the risk factor items and threshold database of the first group of users are as shown in Table 2 above, and the body fat percentage in the user health data collected in step S401 is 25% or greater than 20% , which meets the risk value range of the risk factor "high body fat percentage", then the risk factor "high body fat percentage” is also a health risk factor that the user has been exposed to and should be included in the user health risk factors of the user .
  • the health risk factor items and threshold database of the first group of users are as shown in Table 2 above, and the daily vegetable intake in the user behavior data in the user data collected in step S401 is 150g, which is less than 300g, in line with the risk value range of the risk factor "insufficient daily vegetable intake", then the risk factor "insufficient daily vegetable intake” is also a health risk factor to which the user has been exposed, and should be included in the Among the user's health risk factors.
  • the electronic device 301 may pre-store or obtain correspondences between user behavior data and user health data of different types of data and health risk factor items and risk factors in the threshold database. Therefore, based on the collected user behavior data and user health data, it is possible to query the health risk factor items and threshold database of the first group to determine whether each risk factor is a risk factor that the user has been exposed to, and add it to the user's user health risk factor.
  • the electronic device will The corresponding relationship among data, user health data and the risk factors of the first group can not only determine the user health risk factors, but also determine the exposure level (that is, the degree of risk) of one or more health risk factors among the user health risk factors.
  • the user health risk factor identification method in the embodiment of the present application can reflect the user's overall health risk status and expose the complete set of risk factors that affect health, so that the user's health status can be managed more accurately.
  • the electronic device generates a multiple-period personalized intervention plan for the user's health risk factors
  • FIG. 9 is a schematic diagram of an exemplary information flow for generating a personalized intervention plan in the embodiment of the present application.
  • health risk factors such as insufficient physical activity, unbalanced nutrition or unhealthy eating habits, lack of sleep or sleep disorders, excessive stress, smoking, excessive drinking, etc.
  • the obtained user's personalized information can generate a specific intervention plan through the lifestyle intervention program recommendation rule base.
  • diet plans and daily meal plans can be generated;
  • a daily breathing training check-in and decompression plan can be generated
  • smoking cessation and alcohol restriction programs and programs can be generated for smoking and excessive alcohol consumption.
  • step S401 which types of data in the user data collected in step S401 belong to the user's personalized information can be preset, and the electronic device can automatically identify the user's personalized information from the collected user data based on a preset algorithm. information, without limitation.
  • the rules in the lifestyle intervention recommendation rule base can be preset, or can be a deep learning algorithm, which is not limited here.
  • a way of setting a lifestyle intervention recommendation rule base may be as follows:
  • ⁇ 1> Based on the user's current body mass index (BMI), blood pressure indicators, blood sugar indicators, and blood lipid indicators, at the same time query the user's past medical history database to determine the type of exercise program and diet program, the types are as follows: weight management (BMI> 24kg/m2), blood pressure management (systolic blood pressure SBP>130mmHg or diastolic blood pressure DBP>80mmHg or diagnosed hypertension), blood sugar management (fasting plasma glucose (FPG)>6.1mmol/L or diagnosed diabetes); if All indicators of this user are within the normal range and there is no history of high blood pressure or diabetes.
  • the type of exercise program and diet program is health promotion. If more than one item is satisfied, then the determined priority order of the type of exercise program and diet program is: blood sugar management, blood pressure management, weight management, and health promotion.
  • the goal corresponding to each type of exercise program and diet program is that the corresponding indicators can reach the normal value, and the goal of this type of program for health promotion is to maintain health (exercise up to standard, diet and nutrition balanced).
  • the exercise plan and program are generated through the exercise intervention knowledge base, which can include: exercise type, exercise frequency, exercise intensity ( It is reflected in exercise heart rate range), exercise time, exercise contraindications, etc.
  • a daily meal plan can be generated, including daily energy intake recommendations for each meal, and nutritional intake ratios and total amount, food type and weight, food recommendations, and food types to avoid or food contraindications, etc.
  • ⁇ 6> For late sleep (more than 23:00) or serious lack of sleep time ( ⁇ 6 hours), you can generate two check-in tasks of going to bed early and getting up regularly, and generate a sleep improvement plan (for example, meditation, deep sleep exercises, sleep Soothing and relaxing before bedtime, etc.), it can also provide sleep aid music services to help users improve sleep disorders and develop a normal work and rest time.
  • a sleep improvement plan for example, meditation, deep sleep exercises, sleep Soothing and relaxing before bedtime, etc.
  • a smoking cessation plan for example: daily reduction or immediate withdrawal, daily postponement, etc.
  • smoking cessation reminder check-in tasks can be generated.
  • a drinking restriction plan for example: daily reduction, etc.
  • a drinking restriction reminder check-in task can be generated.
  • it can also push information about the harm caused by bad habits such as smoking and alcohol abuse every week, so as to improve the education of this group of people.
  • the electronic device also determines the exposure level of one or more of the health risk factors when determining the user's health risk factors
  • the exposure level of the health risk factors can also be used as an item for generating the intervention plan. Reference, added to the input data for generating intervention plans, thereby generating more targeted intervention plans for health risk factors with higher risk levels.
  • the electronic device can extract user personalized information from the user data collected in step S401, and then search the life style intervention recommendation rule library according to the user personalized information and the user's health risk factors identified in step S402, and obtain the first An intervention plan, the first intervention plan may include a first exercise plan, a first diet plan, and a first set of health habits check-in tasks.
  • the first intervention plan may include multiple cycles of intervention programs.
  • the first intervention plan may include a 3-month intervention program, where a weekly (7-day) intervention program is one cycle, and the first intervention plan includes a 12-cycle intervention program.
  • the generated intervention plan may include many different types of plans. Exemplarily, as shown in FIG. 10 , it is an exemplary schematic diagram of type division in the intervention plan in the embodiment of the present application.
  • the intervention plan 100 can be divided into an exercise plan 101, a diet plan 102 and a check-in task 103, wherein:
  • Exercise plan 101 can be used to plan exercise for a period of time.
  • it may include: exercise type, exercise frequency, exercise intensity, exercise time, exercise contraindications, etc.
  • the exercise plan 101 may also include exercise program configuration information of the smart fitness equipment, and the smart fitness equipment may directly run according to the exercise program configuration information without manual configuration by the user.
  • the content in the exercise plan 101 can generally be monitored by smart wearable devices or electronic devices when the user actually executes it, or the smart fitness equipment can also record some execution processes or results of the exercise plan 101 .
  • Diet plan 102 can be used to plan diet for a period of time.
  • it may include daily energy intake recommendations for each meal, nutrient intake ratio and total amount, food type and weight, food recommendations, and food types to avoid or food contraindications, etc.
  • the check-in task 103 can be used to plan health events that cannot be directly monitored by electronic equipment for a period of time.
  • it may include sleep check-in tasks, decompression check-in tasks, smoking cessation check-in tasks, alcohol restriction check-in tasks, health indicator detection check-in tasks, etc.
  • the sleep check-in task may include reminders to sleep and wake up; the decompression check-in task may include breathing training check-in, mindfulness training check-in, meditation check-in, etc.; the smoking cessation check-in task may include daily non-smoking check-in; the alcohol restriction check-in task may include Check in without drinking alcohol every day; the task of checking in for health indicator detection can include regular reminders to check in for health indicator detection, etc.
  • the intervention plan generation stage in the embodiment of this application provides a personalized and achievable health intervention plan based on user habits and group characteristics, making full use of wearable devices and portable home detection devices to track user behavior and health indicators, providing health management Health benefit assessment and effect evaluation in the system provide effective input.
  • the electronic device predicts the change trend of the health indicators after the completion of the intervention plan and displays it to the user;
  • the electronic device can predict the predicted value of the health index after the completion of the intervention plan according to the benefit prediction model.
  • the electronic device After the electronic device generates personalized intervention plans for multiple periods, it can predict the change trend of the health indicators after some and/or all of the intervention plans are completed according to the income prediction model, and display the change trend to the user.
  • the change trend may be in the form of a text, a graph, or an animation, etc., which is not limited here.
  • the change trend of the health indicators after the completion of part and/or all of the intervention plans predicted by the electronic device is the health indicators after the completion of some and/or all cycles of the multiple cycles of the intervention plan respectively. composed of predicted values.
  • the electronic device can respectively predict and obtain the predicted values of the health indicators after the completion of the 12 cycles of the intervention plan, and then display the predicted values in the form of a line graph The change trend of the predicted value composition of these 12 health indicators is displayed to the user. It is also possible to predict the predicted values of the health indicators after the intervention plans of the first 6 cycles are completed, and display the changing trend of the composition of the predicted values of the 6 health indicators to the user in the form of a line chart, which is not limited here.
  • FIG. 11 is a schematic diagram of an exemplary information flow of the revenue forecasting model training process in the embodiment of the present application.
  • two health benefit prediction models need to be established through machine learning algorithms: one is a group-based model established on the cloud, and the other is an individual health benefit prediction model established on electronic devices.
  • the establishment process of the group basic model can be as follows:
  • ⁇ 2> Organize and clean the data uploaded to the cloud by a large number of electronic devices belonging to different users, and divide them into group training data for different groups according to age group and gender;
  • the group training data for a group can be divided into a training data set and a verification data set, and the basic model of the group can be trained with the supervision of a machine learning neural network or a regression algorithm:
  • the independent variable is the implementation of the group's weekly intervention plan (daily Average exercise duration, exercise heart rate, dietary calorie intake, intake of three major nutrients, sleep duration, smoking and alcohol consumption, etc.), the value of health indicators before the implementation of the intervention plan (weight, BMI, body fat percentage, contraction blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, triglycerides, etc.);
  • the dependent variable is the value of some and/or all health indicators after the implementation of the intervention plan; the objective function of the machine learning neural network or regression algorithm can be used for the implementation of the intervention plan The mean square error of the values of the before and after health indicators.
  • the value of the health indicators after the implementation of the real intervention plan in the training data set can be used to optimize the objective function, so that the predicted value of the health indicators after the implementation of the intervention plan predicted by the machine learning neural network or regression algorithm is consistent with the real health indicators after the implementation of the intervention plan
  • the gap of the actual value of is within the preset range, and the basic model of the population to be verified is obtained.
  • the data in the verification data set can be used to verify the basic model of the population to be verified. If the predicted value of the health indicator after the implementation of the predicted intervention plan and the actual value of the actual value of the health indicator after the implementation of the intervention plan are within the preset range, Then it can be determined that the group basic model of the group has been obtained.
  • the user's individual health benefit prediction model can be established, and the establishment process can be as follows:
  • ⁇ 2> Use the user's historical intervention plan execution in the electronic device, the value of the user's health indicators before implementing the intervention plan, and the value of the user's part and/or all of the health indicators after implementing the intervention plan as the user's individual training data for training.
  • the parameters of the group basic model are fine-tuned, so that the user's individual health benefit prediction model can be obtained.
  • Fig. 13 is a schematic flow chart of a method for forecasting benefits of an intervention plan in an embodiment of the present application.
  • the cloud can use the collected training data of each group to train the basic model of each group by using machine learning algorithms.
  • each group can be consistent with the different groups in step SS703, or can be different from the different groups in step SS703, which is not limited here.
  • table 4 is an example of a plurality of basic models of different groups obtained through training:
  • the basic model of each group can also be an overall model, and gender and age are two variables in it. If these two variables are input, the overall model can be used as the group basic model of a specific group, It is not limited here.
  • step SS1301 is executed by the cloud server, and in some embodiments, it can also be executed by the electronic device after obtaining relevant data from the cloud server, which is not limited here.
  • the electronic device can obtain the information related to the user.
  • the population basis model of the first population matched by the basic information.
  • the electronic device can obtain the group basic model B that matches this information, as the user The group base model of the first group to which it belongs.
  • the electronic device can obtain the group basic model C that matches the information, as the user The group base model of the first group to which it belongs.
  • the individual training data of the user in the electronic device may include completion data of the user's historical intervention plan, health index data of the user before executing the intervention plan, and health index data of the user after executing the intervention plan.
  • the group basic model of the first group can be directly used as the first individual health benefit prediction model at this time.
  • the value of the user's current health index and the first individual health benefit prediction model predict and obtain the prediction of the health index after the execution of part and/or all of the first intervention plan value.
  • the electronic device may input part and/or all of the first intervention plan and the value of the user's current health index into the first intervention plan.
  • the predicted value of the health index after the implementation of the first intervention plan is obtained.
  • it may include the predicted values of the health indicators after each cycle in the first intervention plan is completed.
  • the predicted value of the health index can be presented in different ways, for example, it can be presented as a change trend of the health index, or it can be presented as a numerical value of the health index, etc., which is not limited here.
  • the electronic device formulates the first intervention plan, it can directly predict the predicted value of the health index after executing the first intervention plan, and display it on the electronic device , for users to view, so that users can understand the possible effects of implementing the intervention plan, so as to have a stronger motivation to implement the intervention plan. It greatly improves the user's confidence and subjective initiative in implementing the intervention plan, and at the same time provides comparative input for post-intervention effect evaluation.
  • using the device-cloud collaboration method to establish a health benefit model from coarse to fine can also obtain a more accurate device-side model on the premise of fully protecting user privacy and security.
  • the above-mentioned training process of the first individual health benefit prediction model is only an example in this application, and in practical applications, there may be other training methods to obtain the first individual health benefit prediction model,
  • the individual training data of the first user can be directly used, and the machine learning algorithm is used to perform model training to obtain the first individual health income prediction model, etc., which is not limited here.
  • the electronic device sends the intervention plan to other devices in the health management system;
  • the electronic device may directly send the first intervention plan generated in step S403 to other devices in the health management system, and each device determines which types of plans in the first intervention plan to implement according to its own capabilities. monitor.
  • the electronic device may send the plan matching the capabilities of each device in the first intervention plan to the corresponding device, which is not limited here.
  • other devices in the health management system may include: smart wearable devices, and/or health detection devices, and/or smart fitness equipment:
  • the electronic device can send the wearable intervention sub-plan in the first intervention plan to the smart wearable device; the wearable intervention sub-plan is part or all of the first intervention plan; the wearable intervention sub-plan includes the first intervention sub-plan Part or all of an exercise plan, and/or part or all of the first diet plan, and/or part or all of the first health habit check-in task set; the wearable intervention sub-plan is the first intervention plan The plan to be executed through the smart wearable device;
  • the electronic device sends the detection intervention sub-plan in the first intervention plan to the health monitoring device; the detection intervention sub-plan is part or all of the first intervention plan; the detection intervention sub-plan includes the first Some or all of the health indicator detection tasks in the health habit check-in task set; the detection intervention sub-plan is the plan to be executed by the health detection device in the first intervention plan;
  • the electronic device sends the fitness intervention sub-plan in the first intervention plan to the smart fitness equipment; the fitness intervention sub-plan is part or all of the first intervention plan; the fitness intervention sub-plan includes the first Part or all of the exercise plan; the fitness intervention sub-plan is a plan to be executed by the smart fitness equipment in the first intervention plan.
  • the electronic device may directly send the intervention plans of all cycles in the first intervention plan to each other device, or may first send the intervention plans of some cycles in the first intervention plan to each other device (for example, it may only send The intervention plan of the first cycle in the first intervention plan is for each other device), which is not limited here.
  • FIG. 14 is an exemplary schematic diagram of delivering different types of intervention plans to different devices in the embodiment of the present application. If the first intervention plan is generated in step S403, the first intervention plan includes three types of plans: exercise plan 101, diet plan 102 and check-in task 103:
  • the electronic device can send part or all of the first exercise plan in the first intervention plan to the smart fitness equipment in the health management system, so that the smart fitness equipment performs exercise configuration according to the first exercise plan, and records the user's execution of the exercise plan. Actual execution data of the first motion plan.
  • the electronic device may send part or all of the health index detection tasks in the first health habit check-in task set in the first intervention plan to the health detection device in the health management system, so that the health detection device detects The task reminds the user to perform a health check and records the measured user's health data.
  • the electronic device can send part or all of the first exercise plan, the first diet plan and the first health habit check-in task set in the first intervention plan to the smart wearable device in the health management system, so that the smart wearable device The user is reminded to execute the first intervention plan, the actual execution data of the intervention plan is recorded, and the health data of the user during the execution of the intervention plan is monitored.
  • the revenue prediction phase may be executed first, and then the (4) Intervention execution phase; in some embodiments, (4) intervention execution phase can be executed first, and then (3) revenue prediction phase can be executed; in some embodiments, (3) revenue prediction phase and (4) Intervention execution phases are executed simultaneously, which is not limited here.
  • the smart wearable device monitors the actual execution data and health data of the intervention plan
  • the smart wearable device can monitor the actual execution data and health data of the first intervention plan:
  • the smart wearable device can remind the user to exercise, and monitor actual execution data during the exercise. If there is a health detection module in the smart wearable device, the values of health indicators before and after the user executes the intervention plan can also be obtained.
  • the smart wearable device may remind the user to eat according to the first diet plan.
  • the smart wearable device may remind the user to complete the check-in task content.
  • the smart wearable device can automatically complete the check-in task after detecting that the user has completed the task, which is not limited here.
  • the health detection equipment measures the health data
  • the health detection device can remind the user to measure the health index according to the health index detection task, and record the value of the health index measured each time .
  • the smart fitness equipment can perform each exercise configuration according to the first exercise plan, for example, configure according to the first exercise plan
  • the exercise type, exercise frequency, exercise intensity, exercise time, etc. of each exercise, and the actual implementation of the first exercise plan can be monitored.
  • the smart fitness equipment can also regularly remind the user to start exercising according to the first exercise plan.
  • the electronic device can also monitor the actual implementation of the first intervention plan and the value of health indicators:
  • the electronic device may remind the user to exercise, and monitor actual execution data during the exercise. If there is a health detection module in the electronic device, the values of health indicators before and after the user executes the intervention plan can also be obtained.
  • the electronic device may remind the user to eat according to the first diet plan, and record the diet situation.
  • the electronic device may remind the user to complete the check-in task content.
  • the electronic device can automatically complete the check-in task after detecting that the user has completed the task; for check-in tasks that cannot be monitored by the electronic device, the electronic device can accept the user's request. Operate and complete the check-in task.
  • each device in the health management system can send the monitored actual execution situation and the value of the health index to the electronic device.
  • the electronic device determines that one cycle in the first intervention plan has been executed, and obtains the actual execution status of one cycle in the first intervention plan and the value of the health index, (6) the stage of effect evaluation and intervention plan adjustment can be executed.
  • the electronic device runs the effect evaluation model to evaluate and evaluate the actual execution of the intervention plan in this cycle, and give suggestions;
  • the electronic equipment compares the predicted value of the health indicators after the completion of the first intervention plan in the first intervention plan with the value of the health indicators after the actual completion of the intervention plan of the cycle, and obtains the evaluation level of the intervention effect;
  • the intervention effect evaluation level is an intervention effect evaluation result.
  • the evaluation result of the intervention effect may also be expressed in other forms besides grades, such as scores, percentages, etc., which are not limited here.
  • the electronic device may obtain the predicted value of the health index after completion of the intervention plan for each period in the first intervention plan.
  • the electronic device may obtain the value of the health indicator before the execution of the intervention plan of the cycle and the value of the health index after the actual completion of the intervention plan of the cycle.
  • the electronic device can compare the predicted values of the health indicators (for example, body weight, BMI, body fat percentage, contraction blood pressure, diastolic blood pressure, fasting blood sugar, total cholesterol, triglycerides, etc.) and the actual value of the health indicators after the actual completion of the first cycle of the intervention plan, to obtain the evaluation level of the intervention effect.
  • the health indicators for example, body weight, BMI, body fat percentage, contraction blood pressure, diastolic blood pressure, fasting blood sugar, total cholesterol, triglycerides, etc.
  • the specific evaluation level of the intervention effect can be expressed in many different ways, such as star rating, percentile score, numerical level, etc. There can also be many different preset division methods for different levels, which are not limited here.
  • Intervention effect evaluation grades can be divided into five grades: S, A, B, C, and D:
  • the trigger condition of S is that the actual health index reaches or is better than the predicted health index
  • the trigger condition of A is that the actual health index is better than the health index before the implementation of the intervention plan, but the change value of the improvement is less than 30% compared with the predicted value. It can be understood as: (actual health index - health index before implementing the intervention plan) ⁇ (predicted health index - health index before implementing the intervention plan) * (1-30%);
  • the trigger condition of B is that the actual health index is better than the health index before the implementation of the intervention plan, but the change value of the improvement is less than 60% compared with the predicted value. It can be understood as: (actual health index - health index before implementing the intervention plan) ⁇ (predicted health index - health index before implementing the intervention plan) * (1-60%);
  • condition of C is that the actual health index is better than the health index before the implementation of the intervention plan, but the benefit effect of B and above has not been achieved;
  • the condition of D is that the actual health index is worse than the health index before implementing the intervention plan.
  • the electronic device Based on the evaluation level of the intervention effect, the electronic device combines the intervention plan of this cycle and the behavior data of the actual completion of the intervention plan of this cycle to generate evaluation and suggestions for one or more plans in the first intervention plan.
  • the electronic device After the electronic device obtains the evaluation level of the intervention effect of the period in the first intervention plan, it can combine the intervention plan of the period and the behavior data actually completed in the intervention plan of the period, and generate an evaluation of the first intervention plan according to the preset evaluation rule base. Evaluation and recommendations for one or more programmes.
  • the intervention effect evaluation method in the embodiment of this application can evaluate the actual effect of the intervention plan by comparing the reasoning results of the health benefit prediction model with the actual changes in health indicators, combined with the joint evaluation rule base of exercise and diet, and solves the problem The problem that health management cannot be closed. Realized the closed loop of health management, improved user experience, and achieved long-term health promotion.
  • the electronic device adjusts the intervention plan for the next cycle based on the evaluation result
  • the electronic device can adjust the intervention plan of the next cycle in the first intervention plan according to the evaluation and suggestion of each plan in the first intervention plan, and the reason input by the user for not completing the intervention plan of this cycle, thereby Ensure that intervention programs are achievable and optimally effective.
  • Adjustment strategy 1 Based on the subjective feeling of the user after the completion of the exercise (using subjective feeling to evaluate the exercise training load, choose from a range of 1-10, where 1 means very easy and 10 means very tired), use R to indicate the user's choice
  • adjust the exercise heart rate range for the next cycle the exercise heart rate range set at the current stage + 2*(R-5). If the current weekly exercise frequency is less than or equal to 4 times and R ⁇ 8, adjust the weekly exercise frequency to 5 times in the next cycle, reduce the duration of each exercise, but keep the total weekly exercise duration unchanged;
  • Adjustment strategy 2 If the result of the evaluation is that the user's fat loss enters the plateau (even if the user completes the set intervention plan, but the weight does not drop or even rebounds for more than two consecutive weeks), on the basis of the original aerobic exercise intervention Add resistance exercise courses, and at the same time, according to the user's cardiorespiratory endurance level, join the high-intensity interval training (HIIT) program for low exercise risk users with higher cardiorespiratory endurance levels.
  • HIIT high-intensity interval training
  • the electronic device may execute steps S404-S412 again based on the adjusted intervention plan.
  • the user's subjective initiative and compliance in implementing the intervention plan are greatly increased by predicting the health benefits before the execution of the intervention plan. And it can evaluate the generated intervention plan by predicting health indicators and actual health indicators after the actual implementation of the intervention plan, as well as user behavior data, and then adjust the intervention plan, so that health management can achieve a true closed-loop mode, which can be effectively Improve user health while improving user compliance to achieve a virtuous cycle.
  • each device in the health management system has multiple users (for example, an electronic device has multiple users), then each step in the above-mentioned embodiment can be aimed at different users, and for each user, the above-mentioned In steps S401-S411, the collected data and the generated intervention plan are all aimed at only one of the multiple users, and relevant data of different users can be distinguished by different user identifiers, which are not limited here.
  • the user's current health condition can be reflected by identifying the user's health risk factors, and an intervention plan to help improve the user's health condition can be generated.
  • the embodiment of the present application also provides a method for assessing the user's age.
  • the user's estimated age can be used to evaluate the user's health risk level as a whole, so that the user's health condition can be managed more accurately.
  • Data preparation (1) Based on the monitoring data of causes of death and risk factors in the burden of disease database (such as the GBD database), the cause of death and population data of the national population over the years can be obtained, including:
  • Population risk factor A set of risk factors used to represent the average exposure in different populations; denoted by J below;
  • Population Attributable Fraction A statistical indicator used to indicate the effect of exposure to risk factors on the occurrence of diseases in the population, indicating the proportion of the incidence of a certain disease in the population attributable to a certain risk factor to the total incidence of the population , can also be understood as the proportion of the disease incidence in the population that can be reduced after eliminating a certain risk factor; PAF jo that appears below represents the PAF of risk factor j and death cause disease o;
  • Risk factor mediation effect weight (Mediation Factor, MF): It is used to indicate the role of the first risk factor in the causal path of the second risk factor to the disease and its effect; MF of disease o as the cause of death;
  • Risk factor relative risk (Relative Risk, RR): It is used to express the ratio of the incidence of a disease in a group exposed to a risk factor and not exposed to the risk factor. The greater the relative risk, the greater the effect of exposure, that is, the greater the strength of the association between exposure and outcome; RR jo represents the RR of risk factor j on death cause disease o.
  • the group life expectancy SE of each group can be calculated according to the group average mortality rate MR o of each cause of death in each group:
  • the death probability q o of the group due to various causes of death in each subsequent year (considering the longest life expectancy of 120 years) can be calculated;
  • a calculation method of the death probability q o may be shown in the following formula (1):
  • M represents the collection of death-cause diseases
  • the life expectancy SE of the group can be calculated by the mathematical expectation method.
  • a calculation method of population life expectancy SE may be as follows:
  • a is the actual age of the user
  • i represents the lifespan of the group (the range is from a to 120)
  • t i represents the probability of the group living to age i multiplied by age i
  • another expression of living to age i is a age to i- No death at age 1, death at age i.
  • Data preparation (4) According to the group average mortality rate MR o , the population attributable fraction PAF jo and the risk factor mediation effect weight MF ijo of each cause of death disease in each group, each group can be determined under the condition of no risk exposure Baseline mortality rate by cause of death BD o ; where no-risk-exposure situation refers to the situation in which everyone in the population is assumed to have no health risk factors.
  • the calculation method of the baseline mortality rate of each cause of death in a population without risk exposure may be as follows:
  • the population attributable fraction PAF Jo of the exposed risk factor set can be calculated according to the following formula (5):
  • J is the set of risk factors exposed to the population
  • I is the set of risk factors associated with the effect of risk factor j on the cause of death o;
  • Data preparation (5) According to the baseline death rate BD o of each cause of death in each group without risk exposure, it can be determined that each cause of death in each subsequent year (the longest consideration 120-year lifespan) probability of death
  • S1801. Determine the group average death probability of each cause-of-death disease of the first group to which the user belongs;
  • the first group to which the user belongs may be determined first, and then the group average death probability of each death-cause disease of the first group may be obtained.
  • the gender in the user's basic information is male and the age is 25, and the group division method shown in Table 5 above is used for group division, it can be determined that the user belongs to group 2: 18-30 years old, male. Then obtain the group average death probability of each cause of death disease corresponding to the group 2.
  • the electronic device can directly obtain the group average death probability of each cause of death disease of the group 2 from the cloud.
  • the electronic device can obtain the required data, and calculate each group 2 according to the calculation method in the above data preparation (3).
  • S1802. Determine the baseline death probability of each cause-of-death disease in the first group without risk exposure
  • the electronic device can directly obtain the baseline death probability of each cause of death of the first group of risk-free exposure from the cloud.
  • the electronic device can obtain the required data and calculate the risk-free risk of the first group according to the calculation method in the above data preparation (4). Baseline probability of death for each cause of death disease under exposure.
  • the electronic device may directly acquire the group life expectancy of the first group from the cloud.
  • the electronic device can calculate the group life expectancy of the first group according to the calculation method in the above data preparation (3) after obtaining the required data.
  • a calculation process may be as follows:
  • the user's health risk factors can be identified in the manner in step S402 above, which is not limited here.
  • K is the set of risk factors in the user's health risk factors
  • L is the set of risk factors related to the effect of risk factor k on the cause of death o
  • m k is the exposure of risk factor k in users relative to the theoretical minimum risk exposure of this risk factor
  • the exposure weight of the level (Theoretical minimum risk exposure level, TMREL);
  • the relative risk (RR kM ) corresponding to each risk factor among the user's health risk factors and the all-cause disease can also be calculated according to formula (9).
  • the risk factors of user exposure are sorted based on the order of RR kM from high to low.
  • the user's death probability q′ 0 can be calculated for each year due to various causes of death (considering a maximum life span of 120 years);
  • the user's death probability q' o of each cause of death can be integrated to obtain the user's all-cause death probability Q' in each year in the future;
  • M represents the collection of causes of death.
  • the user's life expectancy AE can be calculated by the mathematical expectation method
  • a calculation method of a user's life expectancy AE may be shown in the following formula (12) and formula (13):
  • a is the user's actual age
  • i represents the user's life span (range is a to 120)
  • t' i represents the probability that the user lives to age i multiplied by age i
  • another expression for living to age i is a ⁇ No death at age i-1, death at age i.
  • 1-Q′ i-1 represents the probability that the user does not die at the age of i-1
  • Q′ i represents the probability that the user dies at the age of i.
  • the user's expected life AE is obtained by accumulating t' a to t' 120 :
  • S1805. Determine the user's healthy age according to the group life expectancy of the first group and the user's life expectancy.
  • a calculation method may be: adding the user's actual age a to the life expectancy of the first group, and then subtracting the user's life expectancy to obtain the user's healthy age.
  • the user's actual age is 25 years old
  • the calculated group life expectancy of the first group is 70 years old
  • the calculated user's life expectancy is 60 years old
  • the user's healthy age may also be referred to as the user's evaluation age.
  • the above user health age assessment method can be applied in various stages of the health management method of this application:
  • the electronic device can identify the user's health risk factors and the user's healthy age based on the collected user data.
  • the electronic device can generate a multi-cycle personalized intervention plan according to the user's health risk factors and the ranking of each risk factor.
  • the data input by the electronic device into the income health model may include (1) the user's health age identified in the evaluation stage, and the predicted health index may include the user's health age after the execution of the intervention plan.
  • the process can be:
  • ⁇ 2> Organize and clean the data uploaded to the cloud by a large number of mobile electronic devices belonging to different users, and divide them into group training data for different groups according to age and gender;
  • the group training data for a group can be divided into a training data set and a verification data set, and the basic model of the group can be trained with the supervision of a machine learning neural network or a regression algorithm:
  • the independent variable is the implementation of the group's weekly intervention plan (daily Average exercise duration, exercise heart rate, dietary calorie intake, intake of three major nutrients, sleep duration, smoking and alcohol consumption, etc.), the value of health indicators before the implementation of the intervention plan (weight, BMI, body fat percentage, contraction Blood pressure, diastolic blood pressure, fasting blood sugar, total cholesterol, triglycerides, etc.), the user's healthy age before the implementation of the intervention plan;
  • the dependent variable is the value of the health index after the implementation of the intervention plan, the user's healthy age after the implementation of the intervention plan; machine learning
  • the objective function of the neural network or the regression algorithm may be the mean square error of the user's healthy age before and after the execution of the intervention plan.
  • the value of the user's health index after the implementation of the real intervention plan in the training data set and the user's health age can be used to optimize the objective function, so that the predicted value of the user's health index after the execution of the intervention plan predicted by the machine learning neural network or regression algorithm and the user's
  • the difference between the healthy age and the value of the user's health index after the implementation of the real intervention plan and the user's healthy age is within the preset range, and the basic model of the population to be verified is obtained.
  • the data in the verification data set to verify the basic model of the population to be verified. If the predicted value of the user's health index after the implementation of the predicted intervention plan and the predicted user's health age are the same as the value of the actual user's health index after the implementation of the intervention plan If the gap with the user's healthy age is within the preset range, it can be determined that the group basic model of the group has been obtained.
  • the process can be:
  • the user's health age is used as the user's individual training data to train the obtained group basic model of the first group, and the parameters of the group basic model are fine-tuned, so as to obtain the user's individual health benefit prediction model.
  • Fig. 20 is another schematic flowchart of the method for predicting the benefits of an intervention plan in the embodiment of the present application.
  • the training data of each group may include the user's healthy age before the execution of the intervention plan and the user's healthy age after the execution of the intervention plan. Therefore, the input of the trained basic model for each group may include the user's healthy age before the execution of the intervention plan, and the output may include the user's healthy age after the execution of the intervention plan.
  • step SS1301 Other processes are similar to those in step SS1301 and will not be repeated here.
  • step SS1302 Similar to step SS1302, details are not repeated here.
  • the user's individual training data may include the user's health age before the execution of the intervention plan and the user's health age after the execution of the intervention plan. Therefore, the input of the trained first individual health benefit prediction model may include the user's health age before the execution of the intervention plan, and the output may include the user's health age after the execution of the intervention plan.
  • step SS1303 Other processes are similar to step SS1303 and will not be repeated here.
  • the value of the user's current health index and the current user's health age predict and obtain the predicted value of the health index and the predicted user's health age after the execution of the first intervention plan.
  • the electronic device After generating the user's personalized intervention plan to be executed in step S403, for example, after generating the first intervention plan, the electronic device can input the first intervention plan, the value of the user's current health index and the current user's health age into the first In the body health benefit prediction model, the predicted value of the health index and the predicted user's healthy age after the implementation of the first intervention plan are obtained.
  • step SS1304 Other processes are similar to those in step SS1304 and will not be repeated here.
  • the parameter of the user's health age is added to the income prediction model. Since the user's health age can reflect the user's health as a whole, it can make the prediction of the execution effect of the intervention plan more accurate.
  • electronic devices can not only compare the coincidence of the predicted health indicators after the completion of a cycle of intervention plans with the actual health indicators after the completion of the cycle of intervention plans, but also compare the predicted health indicators after the completion of the cycle of intervention plans. The degree of coincidence between the healthy age and the actual user's healthy age after the actual completion of the intervention plan of this cycle, so as to obtain the effect evaluation result of the intervention plan.
  • the evaluation level of the intervention effect can be determined according to the degree of coincidence between the health index and the user's healthy age. Different user health ages also correspond to different intervention evaluation levels, and the lower intervention evaluation level between the two is selected as the final intervention effect evaluation level. For example, if the evaluation level of the intervention effect determined by the matching degree of the health index is B, and the evaluation level of the intervention effect determined by the matching degree of the user's health age is C, then the final evaluation level of the intervention effect can be determined to be C. For another example, different intervention effect evaluation levels can correspond to the matching degree of the health index and the matching degree of the user's healthy age. Intervention effect evaluation level. There may also be many other determination methods, which are not limited here.
  • step SS1601 Other processes are similar to those in step SS1601 and will not be repeated here.
  • the electronic device Based on the evaluation level of the intervention effect, the electronic device combines the intervention plan of this cycle and the behavior data of the actual completion of the intervention plan of this cycle to generate evaluation and suggestions for one or more plans in the first intervention plan.
  • step SS1602 It is similar to step SS1602 and will not be repeated here.
  • the user's health age is used in the evaluation process, because the user's health age can reflect the user's health as a whole, so the evaluation result of the intervention plan is more accurate.
  • the health management system may include: a data collection module, a health risk factor identification and a healthy age assessment module, a personalized lifestyle intervention program generation module, a health benefit prediction module, an intervention program execution monitoring module, a behavior-based compliance management module, and an actual Implementation effect joint evaluation module:
  • the data acquisition module can collect user data, including: user basic information, such as age, gender; user health data, such as blood pressure, blood sugar, blood lipids, body composition, stress, sleep conditions, etc.; user behavior data, such as exercise, diet , sleep habits, drinking, smoking, health questionnaire data, etc.
  • the data collection module can execute the above step S401.
  • the health risk factor identification and health age assessment module can identify the user's health risk factors, calculate the user's health age, and sort the user's health risk factors by degree of risk.
  • the health risk factor identification and healthy age assessment module can execute the above step S402 and the user's healthy age assessment method in the embodiment of the application shown in FIG. 18 .
  • the personalized lifestyle intervention plan generation module can generate various intervention plans that are beneficial to the user's health, such as exercise intervention plans and plans, diet intervention plans and plans, sleep intervention plans, psychological and stress intervention plans, smoking, excessive drinking, etc. Bad lifestyle intervention programs, etc.
  • the personalized lifestyle intervention plan generation module can execute the above step S403.
  • the health benefit prediction module can predict the health indicators after the completion of the intervention plan, and the user's healthy age after the completion of the intervention plan.
  • the health benefit forecasting module can execute the intervention plan benefit forecasting method in the embodiment of the present application shown in FIG. 13 and FIG. 22 .
  • the intervention program execution detection module can detect the behaviors such as exercise, diet and related health indicators before and after the implementation of the intervention plan and during the implementation process, and obtain the actual implementation data and health indicators of the intervention plan.
  • the intervention program execution detection module may execute the above steps S406-S410.
  • the behavior-based compliance management module can reward the user with points after detecting that the user has implemented the intervention plan on time and in volume.
  • the actual implementation effect joint evaluation module can evaluate the effect of the implementation effect of the intervention plan, and connect it with the diet situation for comprehensive evaluation, and can also dynamically adjust the intervention plan based on the evaluation results.
  • the actual implementation effect joint evaluation module can execute the above steps S411-S412 and the intervention plan effect evaluation method in the embodiment of the present application.
  • the following introduces an exemplary electronic device 100 in the health management system provided in the embodiment of the present application.
  • FIG. 24 is a schematic structural diagram of an electronic device 100 provided by an embodiment of the present application.
  • electronic device 100 may have more or fewer components than shown in the figures, may combine two or more components, or may have a different configuration of components.
  • the various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and/or application specific integrated circuits.
  • the electronic device 100 may include: a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, and an antenna 2.
  • Mobile communication module 150 wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194 and A subscriber identification module (subscriber identification module, SIM) card interface 195 and the like.
  • SIM subscriber identification module
  • the sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, bone conduction sensor 180M, etc.
  • the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 100 .
  • the electronic device 100 may include more or fewer components than shown in the figure, or combine certain components, or separate certain components, or arrange different components.
  • the illustrated components can be realized in hardware, software or a combination of software and hardware.
  • the processor 110 may include one or more processing units, for example: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU) wait. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.
  • application processor application processor, AP
  • modem processor graphics processing unit
  • GPU graphics processing unit
  • image signal processor image signal processor
  • ISP image signal processor
  • controller memory
  • video codec digital signal processor
  • DSP digital signal processor
  • baseband processor baseband processor
  • neural network processor neural-network processing unit, NPU
  • the controller may be the nerve center and command center of the electronic device 100 .
  • the controller can generate an operation control signal according to the instruction opcode and timing signal, and complete the control of fetching and executing the instruction.
  • a memory may also be provided in the processor 110 for storing instructions and data.
  • the memory in processor 110 is a cache memory.
  • the memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to use the instruction or data again, it can be called directly from the memory. Repeated access is avoided, and the waiting time of the processor 110 is reduced, thus improving the efficiency of the system.
  • processor 110 may include one or more interfaces.
  • the interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous transmitter (universal asynchronous receiver/transmitter, UART) interface, mobile industry processor interface (mobile industry processor interface, MIPI), general-purpose input and output (general-purpose input/output, GPIO) interface, subscriber identity module (subscriber identity module, SIM) interface, and /or universal serial bus (universal serial bus, USB) interface, etc.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • PCM pulse code modulation
  • UART universal asynchronous transmitter
  • MIPI mobile industry processor interface
  • GPIO general-purpose input and output
  • subscriber identity module subscriber identity module
  • SIM subscriber identity module
  • USB universal serial bus
  • the I2C interface is a bidirectional synchronous serial bus, including a serial data line (serial data line, SDA) and a serial clock line (derail clock line, SCL).
  • processor 110 may include multiple sets of I2C buses.
  • the processor 110 can be respectively coupled to the touch sensor 180K, the charger, the flashlight, the camera 193 and the like through different I2C bus interfaces.
  • the processor 110 may be coupled to the touch sensor 180K through the I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to realize the touch function of the electronic device 100 .
  • the I2S interface can be used for audio communication.
  • processor 110 may include multiple sets of I2S buses.
  • the processor 110 may be coupled to the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170 .
  • the audio module 170 can transmit audio signals to the wireless communication module 160 through the I2S interface, so as to realize the function of answering calls through the Bluetooth headset.
  • the PCM interface can also be used for audio communication, sampling, quantizing and encoding the analog signal.
  • the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface.
  • the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.
  • the UART interface is a universal serial data bus used for asynchronous communication.
  • the bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication.
  • a UART interface is generally used to connect the processor 110 and the wireless communication module 160 .
  • the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function.
  • the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface, so as to realize the function of playing music through the Bluetooth headset.
  • the MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 .
  • MIPI interface includes camera serial interface (camera serial interface, CSI), display serial interface (display serial interface, DSI), etc.
  • the processor 110 communicates with the camera 193 through the CSI interface to realize the shooting function of the electronic device 100 .
  • the processor 110 communicates with the display screen 194 through the DSI interface to realize the display function of the electronic device 100 .
  • the GPIO interface can be configured by software.
  • the GPIO interface can be configured as a control signal or as a data signal.
  • the GPIO interface can be used to connect the processor 110 with the camera 193 , the display screen 194 , the wireless communication module 160 , the audio module 170 , the sensor module 180 and so on.
  • the GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.
  • the SIM interface can be used to communicate with the SIM card interface 195 to realize the function of transmitting data to the SIM card or reading data in the SIM card.
  • the USB interface 130 is an interface conforming to the USB standard specification, specifically, it can be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like.
  • the USB interface 130 can be used to connect a charger to charge the electronic device 100 , and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones and play audio through them. This interface can also be used to connect other electronic devices, such as AR devices.
  • the interface connection relationship between the modules shown in the embodiment of the present application is only a schematic illustration, and does not constitute a structural limitation of the electronic device 100 .
  • the electronic device 100 may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.
  • the charging management module 140 is configured to receive a charging input from a charger.
  • the charger may be a wireless charger or a wired charger.
  • the power management module 141 is used for connecting the battery 142 , the charging management module 140 and the processor 110 .
  • the power management module 141 receives the input from the battery 142 and/or the charging management module 140 to provide power for the processor 110 , the internal memory 121 , the external memory, the display screen 194 , the camera 193 , and the wireless communication module 160 .
  • the wireless communication function of the electronic device 100 can be realized by the antenna 1 , the antenna 2 , the mobile communication module 150 , the wireless communication module 160 , a modem processor, a baseband processor, and the like.
  • Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals.
  • Each antenna in electronic device 100 may be used to cover single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization of the antennas.
  • Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network.
  • the antenna may be used in conjunction with a tuning switch.
  • the mobile communication module 150 can provide wireless communication solutions including 2G/3G/4G/5G applied on the electronic device 100 .
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA) and the like.
  • the mobile communication module 150 can receive electromagnetic waves through the antenna 1, filter and amplify the received electromagnetic waves, and send them to the modem processor for demodulation.
  • the mobile communication module 150 can also amplify the signals modulated by the modem processor, and convert them into electromagnetic waves through the antenna 1 for radiation.
  • at least part of the functional modules of the mobile communication module 150 may be set in the processor 110 .
  • at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be set in the same device.
  • a modem processor may include a modulator and a demodulator.
  • the modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal.
  • the demodulator is used to demodulate the received electromagnetic wave signal into a low frequency baseband signal. Then the demodulator sends the demodulated low-frequency baseband signal to the baseband processor for processing.
  • the low-frequency baseband signal is passed to the application processor after being processed by the baseband processor.
  • the application processor outputs sound signals through audio equipment (not limited to speaker 170A, receiver 170B, etc.), or displays images or videos through display screen 194 .
  • the modem processor may be a stand-alone device.
  • the modem processor may be independent from the processor 110, and be set in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide wireless local area networks (wireless local area networks, WLAN) (such as wireless fidelity (Wireless Fidelity, Wi-Fi) network), bluetooth (bluetooth, BT), global navigation satellite, etc. applied on the electronic device 100.
  • System global navigation satellite system, GNSS
  • frequency modulation frequency modulation, FM
  • near field communication technology near field communication, NFC
  • infrared technology infrared, IR
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency-modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 .
  • the wireless communication module 160 can also receive the signal to be sent from the processor 110 , frequency-modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.
  • the antenna 1 of the electronic device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology.
  • the wireless communication technology may include global system for mobile communications (GSM), general packet radio service (general packet radio service, GPRS), code division multiple access (code division multiple access, CDMA), broadband Code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC , FM, and/or IR techniques, etc.
  • GSM global system for mobile communications
  • GPRS general packet radio service
  • code division multiple access code division multiple access
  • CDMA broadband Code division multiple access
  • WCDMA wideband code division multiple access
  • time division code division multiple access time-division code division multiple access
  • TD-SCDMA time-division code division multiple access
  • the GNSS may include a global positioning system (global positioning system, GPS), a global navigation satellite system (global navigation satellite system, GLONASS), a Beidou navigation satellite system (beidou navigation satellite system, BDS), a quasi-zenith satellite system (quasi -zenith satellite system (QZSS) and/or satellite based augmentation systems (SBAS).
  • GPS global positioning system
  • GLONASS global navigation satellite system
  • Beidou navigation satellite system beidou navigation satellite system
  • BDS Beidou navigation satellite system
  • QZSS quasi-zenith satellite system
  • SBAS satellite based augmentation systems
  • the electronic device 100 realizes the display function through the GPU, the display screen 194 , and the application processor.
  • the GPU is a microprocessor for image processing, and is connected to the display screen 194 and the application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering.
  • Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.
  • the display screen 194 is used to display images, videos and the like.
  • the display screen 194 includes a display panel.
  • the display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light emitting diode or an active matrix organic light emitting diode (active-matrix organic light emitting diode, AMOLED), flexible light-emitting diode (flex light-emitting diode, FLED), Miniled, MicroLed, Micro-oLed, quantum dot light emitting diodes (quantum dot light emitting diodes, QLED), etc.
  • the electronic device 100 may include 1 or N display screens 194 , where N is a positive integer greater than 1.
  • the electronic device 100 can realize the shooting function through the ISP, the camera 193 , the video codec, the GPU, the display screen 194 and the application processor.
  • the ISP is used for processing the data fed back by the camera 193 .
  • the light is transmitted to the photosensitive element of the camera through the lens, and the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye.
  • ISP can also perform algorithm optimization on image noise, brightness, and skin color.
  • ISP can also optimize the exposure, color temperature and other parameters of the shooting scene.
  • the ISP may be located in the camera 193 .
  • Camera 193 is used to capture still images or video.
  • the object generates an optical image through the lens and projects it to the photosensitive element.
  • the photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor.
  • CMOS complementary metal-oxide-semiconductor
  • the photosensitive element converts the light signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal.
  • the ISP outputs the digital image signal to the DSP for processing.
  • DSP converts digital image signals into standard RGB, YUV and other image signals.
  • the electronic device 100 may include 1 or N cameras 193 , where N is a positive integer greater than 1.
  • Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.
  • Video codecs are used to compress or decompress digital video.
  • the electronic device 100 may support one or more video codecs.
  • the electronic device 100 can play or record videos in various encoding formats, for example: moving picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4 and so on.
  • MPEG moving picture experts group
  • the NPU is a neural-network (NN) computing processor.
  • NN neural-network
  • Applications such as intelligent cognition of the electronic device 100 can be realized through the NPU, such as image recognition, face recognition, speech recognition, text understanding, and the like.
  • the internal memory 121 may include one or more random access memories (random access memory, RAM) and one or more non-volatile memories (non-volatile memory, NVM).
  • RAM random access memory
  • NVM non-volatile memory
  • Random access memory can include static random-access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (synchronous dynamic random access memory, SDRAM), double data rate synchronous Dynamic random access memory (double data rate synchronous dynamic random access memory, DDR SDRAM, such as the fifth generation DDR SDRAM is generally called DDR5SDRAM), etc.;
  • SRAM static random-access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • double data rate synchronous Dynamic random access memory double data rate synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • DDR5SDRAM double data rate synchronous dynamic random access memory
  • Non-volatile memory may include magnetic disk storage devices, flash memory (flash memory).
  • flash memory can include NOR FLASH, NAND FLASH, 3D NAND FLASH, etc.
  • it can include single-level storage cells (single-level cell, SLC), multi-level storage cells (multi-level cell, MLC), triple-level cell (TLC), quad-level cell (QLC), etc.
  • SLC single-level storage cells
  • MLC multi-level storage cells
  • TLC triple-level cell
  • QLC quad-level cell
  • UFS universal flash storage
  • embedded multimedia memory card embedded multi media Card
  • the random access memory can be directly read and written by the processor 110, and can be used to store executable programs (such as machine instructions) of an operating system or other running programs, and can also be used to store data of users and application programs.
  • the non-volatile memory can also store executable programs and data of users and application programs, etc., and can be loaded into the random access memory in advance for the processor 110 to directly read and write.
  • the external memory interface 120 can be used to connect an external non-volatile memory, so as to expand the storage capacity of the electronic device 100 .
  • the external non-volatile memory communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music and video are stored in an external non-volatile memory.
  • the electronic device 100 can implement audio functions through the audio module 170 , the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. Such as music playback, recording, etc.
  • the audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signal.
  • the audio module 170 may also be used to encode and decode audio signals.
  • the audio module 170 may be set in the processor 110 , or some functional modules of the audio module 170 may be set in the processor 110 .
  • Speaker 170A also referred to as a "horn" is used to convert audio electrical signals into sound signals.
  • Electronic device 100 can listen to music through speaker 170A, or listen to hands-free calls.
  • Receiver 170B also called “earpiece” is used to convert audio electrical signals into sound signals.
  • the receiver 170B can be placed close to the human ear to receive the voice.
  • the microphone 170C also called “microphone” or “microphone” is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can put his mouth close to the microphone 170C to make a sound, and input the sound signal to the microphone 170C.
  • the electronic device 100 may be provided with at least one microphone 170C. In some other embodiments, the electronic device 100 may be provided with two microphones 170C, which may also implement a noise reduction function in addition to collecting sound signals. In some other embodiments, the electronic device 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions, etc.
  • the earphone interface 170D is used for connecting wired earphones.
  • the earphone interface 170D can be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, or a cellular telecommunications industry association of the USA (CTIA) standard interface.
  • OMTP open mobile terminal platform
  • CTIA cellular telecommunications industry association of the USA
  • the pressure sensor 180A is used to sense the pressure signal and convert the pressure signal into an electrical signal.
  • the pressure sensor 180A may be located on the display screen 194 .
  • pressure sensors 180A such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors.
  • a capacitive pressure sensor may be comprised of at least two parallel plates with conductive material.
  • the electronic device 100 determines the intensity of pressure according to the change in capacitance.
  • the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A.
  • the electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A.
  • touch operations acting on the same touch position but with different touch operation intensities may correspond to different operation instructions. For example: when a touch operation with a touch operation intensity less than the first pressure threshold acts on the short message application icon, an instruction to view short messages is executed. When a touch operation whose intensity is greater than or equal to the first pressure threshold acts on the icon of the short message application, the instruction of creating a new short message is executed.
  • the gyro sensor 180B can be used to determine the motion posture of the electronic device 100 .
  • the angular velocity of the electronic device 100 around three axes may be determined by the gyro sensor 180B.
  • the gyro sensor 180B can be used for image stabilization. Exemplarily, when the shutter is pressed, the gyro sensor 180B detects the shaking angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to counteract the shaking of the electronic device 100 through reverse movement to achieve anti-shake.
  • the gyro sensor 180B can also be used for navigation and somatosensory game scenes.
  • the air pressure sensor 180C is used to measure air pressure.
  • the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.
  • the magnetic sensor 180D includes a Hall sensor.
  • the electronic device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip leather case.
  • the electronic device 100 when the electronic device 100 is a clamshell machine, the electronic device 100 can detect opening and closing of the clamshell according to the magnetic sensor 180D.
  • features such as automatic unlocking of the flip cover are set.
  • the acceleration sensor 180E can detect the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of electronic devices, and can be used in applications such as horizontal and vertical screen switching, pedometers, etc.
  • the distance sensor 180F is used to measure the distance.
  • the electronic device 100 may measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F for distance measurement to achieve fast focusing.
  • Proximity light sensor 180G may include, for example, light emitting diodes (LEDs) and light detectors, such as photodiodes.
  • the light emitting diodes may be infrared light emitting diodes.
  • the electronic device 100 emits infrared light through the light emitting diode.
  • Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it may be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 .
  • the electronic device 100 can use the proximity light sensor 180G to detect that the user is holding the electronic device 100 close to the ear to make a call, so as to automatically turn off the screen to save power.
  • the proximity light sensor 180G can also be used in leather case mode, automatic unlock and lock screen in pocket mode.
  • the ambient light sensor 180L is used for sensing ambient light brightness.
  • the electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness.
  • the ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures.
  • the ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket, so as to prevent accidental touch.
  • the fingerprint sensor 180H is used to collect fingerprints.
  • the electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access to application locks, take pictures with fingerprints, answer incoming calls with fingerprints, and the like.
  • the temperature sensor 180J is used to detect temperature.
  • the electronic device 100 uses the temperature detected by the temperature sensor 180J to implement a temperature treatment strategy. For example, when the temperature reported by the temperature sensor 180J exceeds the threshold, the electronic device 100 may reduce the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection.
  • the electronic device 100 when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 caused by the low temperature.
  • the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.
  • Touch sensor 180K also known as "touch panel”.
  • the touch sensor 180K can be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch screen”.
  • the touch sensor 180K is used to detect a touch operation on or near it.
  • the touch sensor can pass the detected touch operation to the application processor to determine the type of touch event.
  • Visual output related to the touch operation can be provided through the display screen 194 .
  • the touch sensor 180K may also be disposed on the surface of the electronic device 100 , which is different from the position of the display screen 194 .
  • the keys 190 include a power key, a volume key and the like.
  • the key 190 may be a mechanical key. It can also be a touch button.
  • the electronic device 100 can receive key input and generate key signal input related to user settings and function control of the electronic device 100 .
  • the motor 191 can generate a vibrating reminder.
  • the motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback.
  • touch operations applied to different applications may correspond to different vibration feedback effects.
  • the motor 191 may also correspond to different vibration feedback effects for touch operations acting on different areas of the display screen 194 .
  • Different application scenarios for example: time reminder, receiving information, alarm clock, games, etc.
  • the touch vibration feedback effect can also support customization.
  • the indicator 192 can be an indicator light, and can be used to indicate charging status, power change, and can also be used to indicate messages, missed calls, notifications, and the like.
  • the SIM card interface 195 is used for connecting a SIM card.
  • the SIM card can be connected and separated from the electronic device 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195 .
  • the electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1.
  • SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards may be the same or different.
  • the SIM card interface 195 is also compatible with different types of SIM cards.
  • the SIM card interface 195 is also compatible with external memory cards.
  • the electronic device 100 interacts with the network through the SIM card to implement functions such as calling and data communication.
  • the processor 110 can call the computer instructions stored in the internal memory 121 to make the electronic device 100 execute the health management method, the intervention plan benefit prediction method, the intervention plan effect evaluation method and the user's healthy age in the embodiment of the application. assessment method.
  • the term “when” may be interpreted to mean “if” or “after” or “in response to determining" or “in response to detecting".
  • the phrases “in determining” or “if detected (a stated condition or event)” may be interpreted to mean “if determining" or “in response to determining" or “on detecting (a stated condition or event)” or “in response to detecting (a stated condition or event)”.
  • all or part of them may be implemented by software, hardware, firmware or any combination thereof.
  • software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server, or data center by wired (eg, coaxial cable, optical fiber, DSL) or wireless (eg, infrared, wireless, microwave, etc.) means.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media.
  • the available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media (eg, solid state hard disk), etc.
  • the processes can be completed by computer programs to instruct related hardware.
  • the programs can be stored in computer-readable storage media.
  • When the programs are executed may include the processes of the foregoing method embodiments.
  • the aforementioned storage medium includes: ROM or random access memory RAM, magnetic disk or optical disk, and other various media that can store program codes.

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Abstract

健康管理方法、系统和电子设备。在该方法中，基于用户数据帮助用户评估当前暴露的健康危险因素，评测用户整体健康风险因素，针对其中与个体健康息息相关的可控风险项，提供个性化综合干预计划，并对干预计划的健康收益进行预测；在执行完一阶段的干预计划后，还可以提供干预效果评估结果，对下一阶段的干预计划进行调整，从而促进用户健康生活的养成以及主动健康管理目标的达成。

Description

健康管理方法、系统和电子设备
本申请要求于2021年08月27日提交中国专利局、申请号为202111000243.1、申请名称为“健康管理方法、系统和电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及终端及通信技术领域，尤其涉及健康管理方法、系统和电子设备。
背景技术
居民生活水平不断提高的同时，不健康的生活方式导致我国慢性非传染性病(肥胖、心血管疾病、糖尿病、癌症、慢性阻塞性肺部疾病等)患病率不断攀升，并且发病年龄年轻化，给家庭和社会带来了严重的负担。病症的发生和发展是一个长期演进的变化过程，是基因、生理、环境和行为因素综合作用的结果。
日常生活中用户缺乏对自身可控危险因素(体力活动不足、不健康饮食、压力、睡眠不足等)的识别和干预管理，并且即使用户知晓自己的不良生活方式和自身健康的密切关系，在疾病确诊或严重影响生活质量前难以做出改进并保持，对良好习惯和行为的依从性低。因此，如何促进用户进行主动健康管理成为一个亟待解决的问题。
发明内容
本申请提供了健康管理方法、系统和电子设备，用于进行用户的健康管理，促进用户主动健康管理目标的达成。
第一方面，本申请提供了一种健康管理方法，该方法包括：电子设备获取根据第一用户的用户数据生成的第一干预计划；该电子设备预测该第一干预计划的部分和/或全部完成后健康指标的预测值。
其中，该第一干预计划中可以包括第一运动计划、和/或第一饮食计划、和/或第一健康习惯打卡类任务集合。
在上述实现方式中，电子设备可以预测出用户的干预计划部分和/或全部完成后健康指标的预测值，该预测值可极大的提高用户执行该干预计划的信心和主观能动性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备获取根据第一用户的用户数据生成的第一干预计划，具体包括：该电子设备获取该第一用户的用户数据；该电子设备根据该用户数据，生成第一干预计划。
在上述实现方式中，电子设备基于获取到的第一用户的用户数据生成第一干预计划，提升了生成的第一干预计划的有效性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备根据该用户数据，生成第一干预计划，具体包括：该电子设备根据该用户数据，识别用户健康危险因素；该电子设备针对该用户健康危险因素，生成该第一干预计划。
在上述实现方式中，电子设备可以先根据该第一用户的用户数据识别出该第一用户的用户健康危险因素，再针对该用户危险因素生成第一干预计划，这样生成的干预计划对于该第 一用户更有针对性，提升了生成的干预计划对该第一用户的用户健康管理的有效性。
结合第一方面的一些实现方式，在一些实现方式中，该用户数据包括用户基本信息、用户行为数据和/或用户健康数据。
在上述实现方式中，用户数据中不仅可以包括用户基本信息，而且可以包括用户行为数据和/或用户健康数据，使得电子设备可以基于第一用户个性化的信息识别该第一用户的用户健康危险因素，制定相应的干预计划，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该用户基本信息包括年龄和/或性别；该用户行为数据包括运动数据、压力数据、睡眠数据、饮食数据、饮酒数据和吸烟数据中的至少一种；该用户健康数据包括体重数据、体成分数据、血压数据、血糖数据、血脂数据中的至少一种。
在上述实现方式中，用户数据中包括涉及用户个人行为和健康特征的各方面的数据，提升了识别出的用户健康危险因素的准确性，也提高了制定出的第一干预计划的有效性。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备根据该用户数据，识别用户健康危险因素，具体包括：该电子设备从多个群体的危险因素对应关系中，获取与该用户基本信息对应的第一群体的危险因素对应关系，其中不同群体的危险因素对应关系对应的年龄范围和/或性别不同；该第一群体的危险因素对应关系包括一个或多个健康危险因素与其相应的预设条件之间的对应关系，其中包括第一健康危险因素与第一预设条件之间的对应关系；该电子设备根据该用户行为数据和/或该用户健康数据，结合该第一群体的危险因素对应关系，确定该用户健康危险因素，其中，在该用户行为数据和/或该用户健康数据符合该第一预设条件的情况下，该用户健康危险因素中包括该第一健康危险因素。
在上述实现方式中，一个群体的危险因素对应关系中包括一个或多个健康危险因素与其相应的预设条件之间的对应关系，例如其中可以包括有第一健康危险因素与第一预设条件之间的对应关系，还可以包括第二健康危险因素与第二预设条件之间的对应关系。在第一用户的用户行为数据和/或用户健康数据符合第一预设条件时，电子设备可以确定该第一健康危险因素为该第一用户的用户健康危险因素中的一个健康危险因素；同理，在第一用户的用户行为数据和/或用户健康数据符合第二预设条件时，电子设备可以确定该第二健康危险因素为该第一用户的用户健康危险因素中的另一个健康危险因素。可以理解的是，若第一用户的用户行为数据和/或用户健康数据不符合第二预设条件，则电子设备可以确定该第二健康危险因素不是该第一用户的用户健康危险因素中的健康危险因素。采用这样的方式，可以有效、精准且更全面的识别出该第一用户自己的用户健康危险因素，为精准的对该第一用户进行健康管理提供良好的条件。
结合第一方面的一些实现方式，在一些实现方式中，对于危险因素对应关系中的任一个健康危险因素，该健康危险因素对应的预设条件中可以包含有更细分的子预设条件，来呈现其对应的健康危险因素的危险程度。该电子设备根据该用户行为数据和/或该用户健康数据，结合该第一群体的危险因素对应关系，确定该用户健康危险因素，具体可以包括：该电子设备根据该用户行为数据和/或该用户健康数据，结合该第一群体的危险因素对应关系，确定该用户健康危险因素和该用户健康危险因素中一项或多项健康危险因素的暴露水平。该电子设备针对该用户健康危险因素和该一项或多项健康危险因素的暴露水平，生成该第一干预计划。
在上述实现方式中，不仅可以确定出用户健康危险因素，而且可以确定出其中一项或多项健康危险因素的暴露水平，从而更准确的确定出哪些健康危险因素对用户当前的健康影响 最大。将健康危险因素的暴露水平也作为生成干预计划的一项参考，可以对危险程度更高的健康危险因素生成更有针对性的干预计划，提高了生成的第一干预计划的干预效果。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备预测该第一干预计划的部分和/或全部完成后健康指标的预测值，具体包括：该电子设备根据该第一用户的个体训练数据进行模型训练，得到第一个体健康收益预测模型；该第一用户的个体训练数据包括：该第一用户历史干预计划的执行情况、该第一用户历史干预计划执行前健康指标的值以及该第一用户历史干预计划的部分和/或全部执行后健康指标的值；该电子设备将该用户数据中的用户健康数据和该第一干预计划的部分和/或全部输入该第一个体健康收益预测模型，以预测得到该第一干预计划的部分和/或全部完成后健康指标的预测值。
在上述实现方式中，通过机器学习算法训练得到第一个体健康收益预测模型，再使用该模型预测第一干预计划的部分和/或全部完成后健康指标的预测值，预测过程中不需要人为干预，提升了得到健康指标的预测值的效率，也提高了得到的健康指标的预测值的准确性。
可选的，电子设备利用机器学习算法进行模型训练，得到第一个体健康收益预测模型可以有多种方式：
例如，该电子设备可以先获取与该用户数据中用户基本信息对应的第一群体的群体基础模型；该第一群体的群体基础模型为多个群体的群体基础模型中的一个，其中，不同群体对应的年龄范围和/或性别不同；对于该多个群体的群体基础模型中的任一个群体基础模型：该群体基础模型为根据该群体基础模型对应群体的群体训练数据，利用机器学习算法训练得到；该对应群体的群体训练数据包括：该群体基础模型对应群体的用户干预计划的执行情况、干预计划执行前健康指标的值以及干预计划的部分和/或全部执行后健康指标的值。
然后该电子设备可以根据该第一用户的个体训练数据，训练调优该第一群体的群体基础模型，得到该第一个体健康收益预测模型。
在上述实现方式中，在群体基础模型的基础上结合第一用户的个体训练数据得到该第一用户专属的第一个体健康收益预测模型，使用该第一个体健康收益预测模型来预测该第一用户的第一干预计划的部分和/或全部完成后健康指标的预测值，这样在能保障训练健康收益预测模型的训练数据的隐私性的同时，也提升了健康收益预测模型的预测准确性。
再如，该电子设备也可以直接采用第一用户的个体训练数据利用机器学习算法进行模型训练，得到该第一个体健康收益预测模型，此处不作限定。
结合第一方面的一些实现方式，在一些实现方式中，该健康指标包括体重、体重指数、体脂率、收缩压、舒张压、空腹血糖、总胆固醇、甘油三脂中的至少一种。
在上述实现方式中，健康指标中可以包括涉及用户健康的多种指标，提升了用户健康管理的全面性和有效性。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备显示该第一干预计划的部分和/或全部完成后健康指标的预测值。
在上述实现方式中，显示完成该第一干预计划后健康指标的预测值给用户，提高了用户执行该干预计划的信心和主观能动性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该第一干预计划中包括N个周期的干预计划，该N为大于1的正整数；该第一干预计划的部分和/或全部完成后健康指标的预测值包括该第一干预计划中N个周期的干预计划中的部分和/或全部周期分别完成后的健康指标的预测值；该电子设备显示该第一干预计划的部分和/或全部完成后健康指标的预测值，具 体包括：该电子设备显示该第一干预计划完成后健康指标的变化趋势，该健康指标的变化趋势由预测得到的该第一干预计划中N个周期的干预计划中部分和/或全部周期分别完成后的健康指标的预测值组成。
在上述实现方式中，该第一干预计划包括多个周期的干预计划，电子设备可以预测出该第一干预计划中多个周期的干预计划分别完成后的健康指标的预测值并以变化趋势的方式展示给用户，从而可提高用户执行该干预计划的信心和主观能动性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备所在的健康管理系统中还包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材；该方法还包括：该电子设备将该第一干预计划中的穿戴干预子计划下发给该智能穿戴设备；该穿戴干预子计划为该第一干预计划中的部分或全部；该电子设备将该第一干预计划中的检测干预子计划下发给该健康检测设备；该检测干预子计划为该第一干预计划中的部分或全部；该电子设备将该第一干预计划中的健身干预子计划下发给该智能健身器材；该健身干预子计划为该第一干预计划中的部分或全部；该穿戴干预子计划、该检测干预子计划、该健身干预子计划相同或不同。该穿戴干预子计划为该第一干预计划中要通过该智能穿戴设备执行的计划；该检测干预子计划为该第一干预计划中要通过该健康检测设备执行的计划；该健身干预子计划为该第一干预计划中要通过该智能健身器材执行的计划。
在上述实现方式中，电子设备可以将第一干预计划中各种类型的干预计划中的部分或全部分别下发给健康管理系统中相应的其他设备，例如将第一干预计划中的穿戴干预子计划下发给该智能穿戴设备，将第一干预计划中的检测干预子计划下发给该健康检测设备，将第一干预计划中的健身干预子计划下发给该智能健身器材等，从而使得健康管理系统中各设备可以协同共同促进用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该第一干预计划包括第一运动计划、和/或第一饮食计划、和/或第一健康习惯打卡类任务集合；该穿戴干预子计划中包括该第一运动计划中的部分或全部、和/或第一饮食计划中的部分或全部、和/或第一健康习惯打卡类任务集合中的部分或全部；该检测干预子计划中包括该第一健康习惯打卡类任务集合中的健康指标检测任务中的部分或全部；该健身干预子计划中包括该第一运动计划中的部分或全部。
在上述实现方式中，第一干预计划中可以包括涉及用户健康的多个方面的计划，例如运动计划、饮食计划、健康习惯打卡类任务集合等，这些多个方面的计划可以分别部分或全部的包含在下发给健康管理系统中其他设备的干预计划中，提升了健康管理系统中各设备可以协同促进用户主动健康管理目标达成的有效性。
结合第一方面的一些实现方式，在一些实现方式中，该第一干预计划中包括N个周期的干预计划，该N为大于1的正整数；该穿戴干预子计划为该第一干预计划中一个周期、或全部周期的穿戴干预子计划；该检测干预子计划为该第一干预计划中一个周期、或全部周期的检测干预子计划；该健身干预子计划为该第一干预计划中一个周期、或全部周期的健身干预子计划。
在上述实现方式中，下发给健康管理系统中其他设备的干预计划可以是一个周期的干预计划，这样可以减小电子设备的数据发送量，降低了电子设备功耗。也可以下发全部周期的干预计划，这样可以保障电子设备与其他设备网络断开情况下，其他设备也能完成全部周期的干预计划，提升了健康管理系统的抗干扰性。
结合第一方面的一些实现方式，在一些实现方式中，该第一干预计划中包括N个周期的干预计划，该N为大于1的正整数；该方法还包括：该电子设备获取该第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值；该实际执行数据和/或健康指标的值由该电子设备和/或该电子设备所在的健康管理系统中的其他设备监测得到。
在上述实现方式中，电子设备可以自己监测或通过健康管理系统中其他设备监测得到第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值，为准确的进行用户健康管理提供数据支撑。
可以理解的是，第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值包括第一干预计划中第一周期执行完成后健康指标的值。
结合第一方面的一些实现方式，在一些实现方式中，该电子设备所在的健康管理系统中的其他设备包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材；该电子设备获取该第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值，具体包括：该电子设备监测本设备上该第一干预计划中第一周期执行过程中的第一部分实际执行数据和/或第一部分健康指标的值；该电子设备接收该智能健身器材监测的该第一干预计划中第一周期执行过程中的第二部分实际执行数据；该电子设备接收该健康检测设备测量的该第一干预计划中第一周期执行过程中的第二部分健康指标的值；该电子设备接收该智能穿戴设备监测的该第一干预计划中第一周期执行过程中的第三部分实际执行数据和/或第三部分健康指标的值；该电子设备将该第一部分实际执行数据、该第二部分实际执行数据以及该第三部分实际执行数据作为该第一周期执行过程中的实际执行数据，将该第一部分健康指标的值、该第二部分健康指标的值以及该第三部分健康指标的值作为该第一周期执行过程中健康指标的值。
在上述实现方式中，健康管理系统中的其他设备可以包括智能穿戴设备、和/或健康检测设备、和/或智能健身器材，健康管理系统中的设备共同促进用户完成干预计划，有效提升了用户达成主动健康管理目标的可能性。
结合第一方面的一些实现方式，在一些实现方式中，该第一干预计划的部分和/或全部完成后健康指标的预测值包括该第一干预计划中第一周期完成后健康指标的预测值；该方法还包括：该电子设备对比该第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，得到干预效果评估结果。
在上述实现方式中，在第一干预计划完成后，可以比对健康指标的预测值和实际完成第一周期的干预计划后的健康指标的值的吻合度得到干预效果评估结果，从而能更准确的评估该第一干预计划的效果。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备基于该干预效果评估结果，结合该第一周期执行过程中的实际执行数据和/或健康指标的值，生成对该第一干预计划中一项或多项计划的评价，作为对该第一干预计划的评价结果。
在上述实现方式中，可以结合实际执行数据和/或健康指标的值生成对第一干预计划的评价结果，从而能更准确的评价该第一干预计划是否适合该第一用户。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备根据该对该第一干预计划的评价结果，调整第一干预计划中第二周期的干预计划，该第二周期为该第一周期的下一个周期。
在上述实现方式中，可以基于对第一干预计划的评价结果，调整该第一干预计划中下个周期的干预计划，实现健康管理的闭环，提高用户体验，达到长期的健康促进作用。
结合第一方面的一些实现方式，在一些实现方式中，在调整第一干预计划中第二周期的干预计划后，该方法还包括：该电子设备预测调整后的第一干预计划的部分和/或全部完成后健康指标的预测值。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备显示调整后的该第一干预计划的部分和/或全部完成后健康指标的变化趋势。
在上述实现方式中，电子设备可以预测出调整后的第一干预计划的部分和/或全部完成后的健康指标的预测值并显示给用户，从而可提高用户继续执行该调整后第一干预计划的信心和主观能动性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备将该调整后的第一干预计划的部分和/或全部下发给该电子设备所在的健康管理系统中的其他设备。
在上述实现方式中，电子设备调整第一干预计划后，可以将调整后的第一干预计划的部分和/或全部下发给电子设备所在的健康管理系统中的其他设备，以更新其他设备中存储的第一干预计划，使得健康管理系统中各设备都能按照最新的第一干预计划促进用户主动健康管理目标的达成，提升了健康管理的效果。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备根据该用户数据，评估该第一用户的年龄，作为该第一用户的用户评估年龄；该用户数据包括用户行为数据和/或用户健康数据；该电子设备预测该第一干预计划的部分和/或全部完成后该第一用户的预测用户评估年龄。
在上述实现方式中，电子设备可以根据用户数据评估得到第一用户的用户评估年龄，并能预测用户完成第一干预计划的部分和/或全部后的第一用户的预测用户评估年龄，可简洁的反映用户整体的健康风险状态。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备显示该用户评估年龄和该预测用户评估年龄。
在上述实现方式中，该电子设备可以将该用户评估年龄和该预测用户评估年龄显示给用户，以提高用户执行该第一干预计划的主观能动性，促进了用户主动健康管理目标的达成。
结合第一方面的一些实现方式，在一些实现方式中，该方法还包括：确定与该用户数据中用户基本信息对应的第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率；其中，无风险暴露情况是指假设群体中的每个人都没有健康风险因素的情况；确定第一群体的群体预期寿命；根据该用户健康危险因素和该第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率，确定用户预期寿命；根据该用户预期寿命、该第一群体的群体预期寿命以及该第一用户的实际年龄，确定该第一用户的用户评估年龄。
在上述实现方式中，在确定用户评估年龄的过程中不仅参考了第一用户专属的用户健康风险因素和实际年龄，而且参考了第一用户所属的第一群体的群体预期寿命，使得评估得到的第一用户的用户评估年龄能更准确的体现出用户的整体健康状况。
第二方面，本申请提供了一种干预计划评价方法，该方法包括：电子设备获取第一干预计划的部分和/或全部完成后健康指标的预测值；该电子设备获取该第一干预计划执行过程中的实际执行数据和/或健康指标的值；该电子设备对比该第一干预计划的部分和/或全部完成后健康指标的预测值与实际完成该第一干预计划的部分和/或全部后的健康指标的值的吻合度，得到干预效果评估结果。
在上述实现方式中，在第一干预计划完成后，可以比对健康指标的预测值和实际完成第 一周期的干预计划后的健康指标的值的吻合度得到干预效果评估结果，从而能更准确的评估该第一干预计划的效果。
结合第二方面的一些实现方式，在一些实现方式中，该第一干预计划中包括N个周期的干预计划，该N为大于1的正整数；第一干预计划的部分和/或全部完成后健康指标的预测值中包括该第一干预计划中第一周期完成后健康指标的预测值；该电子设备获取该第一干预计划执行过程中的实际执行数据和/或健康指标的值，具体包括：该电子设备获取该第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值；该实际执行数据和/或健康指标的值由该电子设备和/或该电子设备所在的健康管理系统中的其他设备监测得到；该电子设备对比该第一干预计划的部分和/或全部完成后健康指标的预测值与实际完成该第一干预计划后的健康指标的值的吻合度，得到干预效果评估结果，具体包括：该电子设备对比该第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，得到该干预效果评估结果。
结合第二方面的一些实现方式，在一些实现方式中，该电子设备所在的健康管理系统中的其他设备包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材。
在上述实现方式中，健康管理系统中的其他设备可以包括智能穿戴设备、和/或健康检测设备、和/或智能健身器材，健康管理系统中的设备共同促进用户完成干预计划，有效提升了用户达成主动健康管理目标的可能性。
结合第二方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备基于该干预效果评估结果，结合该第一周期执行过程中的实际执行数据和/或健康指标的值，生成对该第一干预计划中一项或多项计划的评价，作为对该第一干预计划的评价结果。
在上述实现方式中，可以结合实际执行数据和/或健康指标的值生成对第一干预计划的评价结果，从而能更准确的评价该第一干预计划是否适合该第一用户。
结合第二方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备根据该对该第一干预计划的评价结果，调整第一干预计划中第二周期的干预计划，该第二周期为该第一周期的下一个周期。
在上述实现方式中，可以基于对第一干预计划的评价结果，调整该第一干预计划中下个周期的干预计划，实现健康管理的闭环，提高用户体验，达到长期的健康促进作用。
结合第二方面的一些实现方式，在一些实现方式中，该电子设备对比该第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，得到干预效果评估结果，具体包括：该电子设备对比该第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，以及第一干预计划中第一周期完成后预测用户评估年龄与实际完成该第一周期的干预计划后的用户评估年龄的吻合度，得到该干预效果评估结果。
在上述实现方式中，干预效果评估过程中可以加入用户评估年龄参数，由于用户评估年龄能整体反映用户的健康状态，因此这样能使得干预效果评估结果更加准确。
结合第二方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备获取根据第一用户的用户数据生成的该第一干预计划；该电子设备获取第一干预计划的部分和/或全部完成后健康指标的预测值，具体包括：该电子设备预测该第一干预计划的部分和/或全部完成后健康指标的预测值。
在上述实现方式中，电子设备可以生成第一干预计划并预测该干预计划的部分和/或全部 完成后健康指标的预测值，提高了用户执行该干预计划的信心和主观能动性，促进了用户主动健康管理目标的达成。
第三方面，本申请提供了一种评估用户年龄的方法，包括：电子设备获取第一用户的用户数据；该用户数据包括用户行为数据和/或用户健康数据；该电子设备根据该第一用户的用户数据，评估该第一用户的年龄，作为该第一用户的用户评估年龄。
在上述实现方式中，电子设备可以根据用户数据评估得到第一用户的用户评估年龄，可简洁的反映用户整体的健康风险状态。
结合第三方面的一些实现方式，在一些实现方式中，该电子设备根据该第一用户的用户数据，评估该第一用户的年龄，作为该第一用户的用户评估年龄，具体包括：该电子设备根据该用户数据，识别用户健康危险因素；该电子设备根据该用户数据和该用户健康危险因素，评估该第一用户的年龄，作为该第一用户的用户评估年龄。
在上述实现方式中，电子设备评估第一用户的用户评估年龄的过程中需要先识别出用户健康危险因素，使得得到的用户评估年龄能更准确的体现用户的整体健康水平。
结合第三方面的一些实现方式，在一些实现方式中，该方法还包括：该电子设备针对该用户健康危险因素，生成第一干预计划；该电子设备预测该第一干预计划的部分和/或全部完成后该第一用户的预测用户评估年龄。
在上述实现方式中，电子设备可以预测第一干预计划完成后用户的预测用户评估年龄，提高了用户执行该干预计划的主观能动性，促进了用户主动健康管理目标的达成。
结合第三方面的一些实现方式，在一些实现方式中，该电子设备根据该用户数据和该用户健康危险因素，评估该第一用户的年龄，作为该第一用户的用户评估年龄，具体包括：确定与该用户数据中用户基本信息对应的第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率；其中，无风险暴露情况是指假设群体中的每个人都没有健康风险因素的情况；确定第一群体的群体预期寿命；根据该用户健康危险因素和该第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率，确定用户预期寿命；根据该用户预期寿命、该第一群体的群体预期寿命以及该第一用户的实际年龄，确定该第一用户的用户评估年龄。
在上述实现方式中，在确定用户评估年龄的过程中不仅参考了第一用户专属的用户健康风险因素和实际年龄，而且参考了第一用户所属的第一群体的群体预期寿命，使得评估得到的第一用户的用户评估年龄能更准确的体现出用户的整体健康状况。
第四方面，本申请实施例提供了一种电子设备，该电子设备包括：一个或多个处理器和存储器；该存储器与该一个或多个处理器耦合，该存储器用于存储计算机程序代码，该计算机程序代码包括计算机指令，该一个或多个处理器调用该计算机指令以使得该电子设备执行如第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实现方式描述的方法。
第五方面，本申请提供了一种芯片系统，该芯片系统应用于电子设备，该芯片系统包括一个或多个处理器，该处理器用于调用计算机指令以使得该电子设备执行如第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实现方式描述的方法。
第六方面，本申请提供了一种包含指令的计算机程序产品，当上述计算机程序产品在电子设备上运行时，使得上述电子设备执行如第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实 现方式描述的方法。
第七方面，本申请提供了一种计算机可读存储介质，包括指令，当上述指令在电子设备上运行时，使得上述电子设备执行如第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实现方式描述的方法。
第八方面，本申请提供了一种用户健康管理系统，该健康管理系统包括智能穿戴设备、健康检测设备或智能健身器材中的至少一个和该电子设备；该电子设备，用于执行如第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实现方式描述的方法。
可以理解地，上述第四方面提供的电子设备、第五方面提供的芯片系统、第六方面提供的计算机程序产品、第七方面提供的计算机存储介质和第八方面提供的用户健康管理系统均用于执行本申请第一方面以及第一方面中任一可能的实现方式、第二方面以及第二方面中任一种可能的实现方式、第三方面以及第三方面中任一种可能的实现方式所提供的方法。因此，其所能达到的有益效果可参考对应方法中的有益效果，此处不再赘述。
附图说明
图1是本申请实施例提供的一种疾病风险评估方式；
图2是本申请实施例提供的一种健康管理方式；
图3是本申请实施例提供的健康管理系统的一个硬件组成场景的示例性示意图；
图4是本申请实施例中健康管理方法一个示例性信息交互示意图；
图5是本申请实施例中各类用户数据中信息类别的示例和相应采集方式的示例性示意图；
图6是本申请实施例中生成用户健康危险因素的过程中的一个示例性信息流向示意图；
图7是本申请实施例中一种用户健康危险因素识别方法的一个示例性流程示意图；
图8是本申请实施例中一种健康危险因素的筛选方式的示例性示意图；
图9是本申请实施例中生成个性化的干预计划的一个示例性信息流向示意图；
图10是本申请实施例的干预计划中类型划分的一个示例性示意图；
图11是本申请实施例中收益预测模型训练过程的一个示例性信息流向示意图；
图12是本申请实施例中群体基础模型的训练过程的一个示例性示意图；
图13是本申请实施例中干预计划收益预测方法的一个流程示意图；
图14是本申请实施例中将不同类型的干预计划下发到不同设备的示例性示意图；
图15是本申请实施例中一个生成干预计划效果评估的过程的示例性信息流向示意图；
图16是本申请实施例中一个干预计划效果评估流程示意图；
图17是本申请实施例中评估用户年龄过程中信息流向示例性示意图；
图18是本申请实施例中一个评估用户年龄的方法流程示意图；
图19是本申请实施例中一个干预计划效果评估信息流向示意图；
图20是本申请实施例中干预计划收益预测方法的另一个流程示意图；
图21是本申请实施例中另一个生成干预计划效果评估的过程的示例性信息流向示意图；
图22是本申请实施例中另一个干预计划效果评估方法流程示意图；
图23是本申请实施例中健康管理系统的一个示例性软件模块构架示意图；
图24是本申请实施例中电子设备的一个示例性硬件结构示意图。
具体实施方式
本申请以下实施例中所使用的术语只是为了描述特定实施例的目的，而并非旨在作为对本申请的限制。如在本申请的说明书和所附权利要求书中所使用的那样，单数表达形式“一个”、“一种”、“所述”、“上述”、“该”和“这一”旨在也包括复数表达形式，除非其上下文中明确地有相反指示。还应当理解，本申请中使用的术语“和/或”是指并包含一个或多个所列出项目的任何或所有可能组合。
以下，术语“第一”、“第二”仅用于描述目的，而不能理解为暗示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征，在本申请实施例的描述中，除非另有说明，“多个”的含义是两个或两个以上。
健康管理涉及到很多的领域，例如，健康风险评估、健康干预、健康数据监测等。健康管理也有非常多不同的实现方式：
在一种健康管理的实现方式中，基于大量的流行病学研究，疾病防治指南中给出了单病种相关的危险因素项和风险评估量表，这给发展出来的单一疾病风险评估工具提供了依据。
但是这种健康管理方式缺少用户整体健康状态风险识别和评价方法。单病种风险评估不能关联到各种正常死因相关危险因素，无法直接得出对死亡的影响，并且这种评估缺少对整体健康风险水平的度量。
例如，在如图1所示的疾病风险评估方式中，其主要是对影响健康的慢病风险进行评估和预测，具体流程为：通过纵向研究得到健康大数据，对数据进行整理，根据疾病的名称对疾病的各项指标阈值范围进行定义，同时根据疾病的各项指标阈值范围，利用统计分析软件建立对应疾病的队列。建立队列之后，利用Cox单因素回归分析方法对每个变量均进行变量选择，最后将纳入Cox回归模型的变量进行多因素回归分析，构建Cox比例风险模型。对Cox比例风险模型进行内部验证和外部验证，得到疾病风险识别和预测模型。输入新个体体征指标和基本信息，根据疾病预测模型和疾病风险评估双曲线，生成疾病风险评估结果。
然而这种疾病风险评估方式主要是基于单病种的纵向跟踪研究，构建Cox比例风险模型；或者一些风险评估工具的实现直接基于疾病防治指南里面提供的风险评估量表(如中国糖尿病风险评分表)。在大众健康管理中会存在以下问题：
1)单一疾病风险评估模型和方法是针对该单病种得到的风险等级和危险因素项，各类疾病发病机制和影响因素差异很大，虽然评估结果可以对心脑血管疾病、糖尿病这些患病率高的慢病起到预防作用，但是评估结果不能反映用户整体的健康状态和影响健康的危险项全集，也不能反映危险因素对用户健康寿命的影响，这就会导致对用户整体健康状态的评估不准确，从而导致采取的干预措施不准确或者不适宜。
2)该模型的建立往往是基于一个小范围的跟踪研究，人群特征差异显著。在其他人群中由于地区、年龄、性别等的差异，该评估模型算法准确度不高。
在另一种健康管理的实现方式中，在进行健康评估后，可以采用很多干预手段，包括运动干预、饮食干预等。这些干预大多是基于权威组织对全人群的广泛建议，如：世界卫生组织建议18-64岁成人每周应进行至少150-300分钟的中等强度有氧运动；中国营养学会建议饮食避免或控制高油、盐、糖。
但由于个体特征千差万别，这些方案做不到有针对性，用户对合适自己的量也很难掌控。
例如，在如图2所示的健康管理方式中，主要包括健康促进和慢病管理，其流程包括3部分：
1)健康测评和风险评估：首先获取用户的基本信息，通过标准问卷收集用户的不适症状、运动习惯、日常体力活动和生活方式等数据，采集必要的体征体检数据(体成分、血压、血糖、血脂)，通过疾病风险评估模型评估个体的疾病风险等级，识别疾病危险因素。
2)健康干预：根据评估对象的慢病发病风险和疾病高危人群风险对应的危险因素，对可控危险项(绝大部分是不良生活方式和行为习惯)，制定个性化干预措施和实施计划，如运动计划(方式、频率、强度、时间)。
3)执行监测和反馈：将计划推送给评估对象，通过穿戴式设备监测或用户主观感受反馈计划落实情况，跟踪日常可检测体征指标的变化情况。
然而这种健康管理方式主要是集中在慢病管理，从评估到生成干预策略，再到干预计划执行监测，依然会存在以下问题：
1)该健康管理方式仅能根据前期的用户输入和相关信息制定干预计划，无法准确的对下一步干预计划进行动态调整，从而使得整体干预效果不好，或后期制定的干预计划无法执行。
2)该健康管理方式主要是集中在慢病管理，但是在这种情况下一些结构性的病变已不可逆转，采取的干预计划在这些人群中干预效果会大大降低。
在这些健康管理方式中，都缺少对干预计划执行前的健康收益预测，以及实际执行完成后效果的综合评价和解读。干预计划的调整缺少有效输入，干预管理达不到闭环，因此对用户健康长期改善的效果不显著。所以用户体验较差，忠诚度低，难以形成真正的主动健康管理。
本申请提供了另一种健康管理方法，可以基于用户体征数据和日常行为数据帮助用户评估当前暴露的健康危险因素，评测用户整体健康风险因素，针对其中与个体健康息息相关的可控风险项，提供个性化综合干预管理方案、目标计划对健康收益的预测，在执行完一阶段的干预计划后，还可以提供干预效果评估和运动饮食联合评价方法，从而促进用户健康生活的养成以及主动健康管理目标的达成。
本申请的健康管理方法应用于健康管理系统。如图3所示，为该健康管理系统的一个硬件组成场景的示例性示意图。该示例性的健康管理系统300可以包括：电子设备301(例如手机、平板电脑等)，智能穿戴设备302(例如智能手环，智能腕表等)，健康检测设备303(例如体脂称、血压计、血糖仪、血脂检测仪等)，智能健身器材304(例如，智能跑步机、智能划船器等)，该健康管理系统可以包括云基础设施(例如云服务器等)组成的云端305，其中：
电子设备301：主要负责采集用户基本信息、健康问卷信息和行为数据，同步健康管理系统中其他设备的数据，完成储存和处理；并负责用户健康风险评估、干预方案生成、健康收益预测和执行效果评价，还可以将用户授权的数据上传至云端305；
智能穿戴设备302：主要负责用户运动、睡眠、压力数据的监测和采集；
健康检测设备303：主要负责用户健康指标(体成分、血压、血糖、血脂)的检测和采集；
智能健身器材304：可以接收电子设备301发送的运动方案并自动执行，执行完成后可 以回传运动数据到电子设备301；
云端305：主要负责健康收益预测群体基础模型的训练，干预知识库的管理及更新等。
可以理解的是，在实际应用中，该健康管理系统300中可以包括更多或更少的设备，此处不作限定。
下面结合该示例性的健康管理系统300，对本申请实施例中的健康管理方法进行描述：
如图4所示，为本申请实施例中健康管理方法一个示例性信息交互示意图。为便于理解，可以将本申请实施例中的健康管理方法分为6个阶段：
(1)评估阶段，包括步骤S401～S402：电子设备301基于用户数据，自动从整体健康危险因素中评估识别出用户当前暴露的可控健康危险因素，得到用户健康危险因素；
(2)干预计划生成阶段，包括步骤S403：电子设备301针对识别出的用户健康危险因素，生成多个周期的个性化干预计划。例如可以生成3个月的干预计划，其中每7天的干预计划是一个周期。
(3)收益预测阶段，包括步骤S404：电子设备301根据收益预设模型预测干预计划完成后健康指标的变化趋势并显示给用户；
(4)干预执行阶段，包括步骤S405：电子设备301下发干预计划到健康管理系统300中的其他设备，执行干预计划；
(5)数据监测阶段，包括步骤S406～S410：健康管理系统300中各设备监测干预计划执行过程中的实际执行数据和/或健康指标的值，并汇总到电子设备301；
(6)效果评估和干预计划调整阶段，包括步骤S411～S412：电子设备301评估本周期干预计划的执行效果，并据此调整下个周期的干预计划。
下面对各个阶段的步骤进行具体描述：
(1)评估阶段：
S401、健康管理系统中各设备采集用户数据，汇总到电子设备；
为便于描述，本申请中可以将用户数据分类为：
用户基本信息：包括用户确定性的基本信息的数据；
用户行为数据：包括与用户行为相关的数据；
用户健康数据：包括与用户健康状态相关的数据。
图5为本申请实施例中各类用户数据中信息类别的示例和相应采集方式的示例性示意图。
示例性的，对于用户基本信息510：
在一些实施例中，用户基本信息可以包括用户的出生年月(年龄)和/或性别。在一些实施例中，用户基本信息还可以包括身高等信息，此处不作限定。
在一些实施例中，这些用户基本信息可以由用户直接手动录入到电子设备301中；在一些实施例中，电子设备301也可以直接根据用户在该电子设备301中存储的资料、填写的账户信息等，识别提取出这些用户基本信息，此处不作限定。
示例性的，对于用户行为数据520：
在一些实施例中，用户行为数据可以包括用户的运动数据521、压力数据522、睡眠数据523、饮食数据524、饮酒数据525以及吸烟数据526中的至少一类数据。
对于其中的运动数据521：
在一些实施例中，可以由电子设备301或智能穿戴设备302采集；在一些实施例中，运动数据还可以由智能健身器材记录后，联网发送给电子设备301或由用户手动录入电子设备301。
可以理解的是，用户行为数据中的各类数据中还可以包括有在更细分的不同方面的数据：
例如，运动数据中可以包括运动时长、运动步数、运动距离、运动心率、运动强度等级、体力活动水平等方面的数据：
采用带有加速度计、陀螺仪传感器的具有定位功能的电子设备301或智能穿戴设备302可以采集用户的运动时长、运动步数和运动距离；
采用带有光体积变化描记图法(photo plethysmo graphy，PPG)心率监测功能的智能穿戴设备302可以采集用户静息心率和运动心率HRe。其中，PPG是一种简单的光学技术，用于检测周围血管循环中血液的体积变化。这是一种低成本且无创的方法，可以在皮肤表面进行测量。广泛用于穿戴设备中心率监测。
在采集到用户的运动心率HRe后，参考美国运动医学会(american college of sports medicine，ACSM)指南，电子设备301或智能穿戴设备302可以通过将220减去年龄估算出用户最大心率HRmax，根据该用户最大心率HRmax和运动心率HRe可以推算出用户的运动强度等级：属于低强度运动、中等强度运动还是高强度运动。例如，一种推算和分级方法可以为：低强度运动为：HRe＜65％*HRmax；中等强度运动为：65％*HRmax≤HRe≤75％*HRmax；高强度运动为HRe＞75％*HRmax。
基于用户的运动时长和运动强度，利用国际体力活动问卷(international physical activity questionnaire，IPAQ)中体力活动水平的计算方法，电子设备301或智能穿戴设备302可以得到最近7天用户的运动量和体力活动水平(低、中、高)。
对于其中的压力数据522：
可以通过智能穿戴设备302采集用户每日压力值。
对于其中的睡眠数据523：
采用基于PPG信号分析技术的智能穿戴设备302或基于超声呼吸信号分析技术的电子设备301可以采集睡眠分期和睡眠质量数据。
电子设备301或智能穿戴设备302还可以接收用户手动记录的夜间开始睡眠时间，通过对比用户手动记录的入睡时间和设备自己识别的用户入睡时间可以判断出用户有无入睡困难和晚睡的不良习惯。
在一些实施例中，睡眠数据也可以由用户将自己的睡眠情况手动录入电子设备301。
对于其中的饮食数据524：
可以由用户在饮食时进行拍照识别生成，也可以由用户基于自己的饮食情况手动录入电子设备301。
具体的，电子设备301通过用户手动录入或拍照识别采集的饮食摄入情况，可以包括：食材类型、摄入量等。电子设备301还可以通过饮食库中每种食材的营养元素占比，得到用户摄入的营养元素。
对于饮酒数据525和吸烟数据526：
可以由用户在饮酒或吸烟后，手动录入电子设备301。
例如，电子设备301可以以问卷调查的形式收集用户吸烟和饮酒习惯(频次和平均每次的量)。
示例性的，对于用户健康数据530：
在一些实施例中，用户健康数据可以包括体重、体成分、血压、血糖、血脂中的至少一种。
这些用户健康数据可以由健康检测设备303记录后，联网发送到电子设备301，或用户手动录入电子设备301，此处不作限定。
例如，用户可以每周使用智能体脂称采集体重和体成分数据。
例如，电子设备301还可以利用体重(kg)除以身高的平方(m2)得到体重指数BMI；进一步的，电子设备301可以将用户体重分为偏瘦(BMI<18.5)、正常(18.5≤BMI≤24)、超重(24＜BMI≤28)、肥胖(BMI＞28)。
例如，用户可以每日使用智能血压计采集收缩压和舒张压，得到血压数据。
例如，用户可以每日使用智能血糖仪采集空腹血糖FPG，得到血糖数据。
例如，用户可以每周利用血脂检测仪采集血脂指标(总胆固醇、甘油三脂、低密度脂蛋白胆固醇、高密度脂蛋白胆固醇)，得到血脂数据。
例如，用户可以通过手动录入或上传最近一次体检报告图片(OCR技术识别具体内容)到电子设备301，电子设备301可以获取用户的体检信息。
此外，电子设备301还可以以问卷调查的形式收集用户个人疾病史、疾病家族史等。
可以理解的是，在上述数据采集过程中，用户手动录入数据到电子设备301不仅限于用户直接输入相关数据的形式，还可以包括电子设备301显示相关数据选项供用户选择、电子设备301显示调查问卷供用户填写、电子设备301通过打卡任务记录用户的打卡情况等等需要用户手动介入操作从而使得电子设备301得到相关数据的形式，此处不作限定。
健康管理系统中的其他各设备(智能穿戴设备302、健康检测设备303和/或智能健身器材304)采集到相关用户数据：用户行为数据、用户健康数据后，可以联网(例如通过蓝牙连接或无线网络)将采集到的数据上传同步到电子设备301。
S402、电子设备根据采集的用户数据，识别用户健康危险因素；
电子设备301得到健康管理系统300中各设备采集的用户数据后，可以根据这些用户数据以及影响健康的危险因素全集，识别出与用户生活方式有关且可控的用户健康危险因素。
具体的识别用户健康危险因素可以采用多种不同的方式，下面以其中一种方式为例对本申请实施例中的用户健康危险因素识别方法进行详细描述：
图6为本申请实施例中生成用户健康危险因素的过程中的一个示例性信息流向示意图；
图7为本申请实施例中一种用户健康危险因素识别方法的一个示例性流程示意图；
该用户健康危险因素识别方法包括：
分群体数据库建立过程：步骤SS701～SS703，用于进行用户健康危险因素识别的数据准备；本申请的一些实施例中，该分群体数据库也可以被称为多个群体的危险因素对应关系。
危险因素识别过程：步骤SS704～SS705，用于基于分群体数据库和采集的用户数据确定用户健康危险因素。
下面结合图6和图7，对这两个过程中的步骤分别进行详细描述：
SS701、根据人口健康和疾病负担数据库，获取影响健康的危险因素全集；
该人口健康和疾病负担数据库可以为一些大型国际或国内组织发布的用于展示全球或某 国人口健康和疾病负担的数据库，例如可以为全球疾病负担(Global Burden of Disease，GBD)数据库，或一些同类的其他数据库。
以采用GBD数据库为例：具体的，可以基于GBD数据库查询得到所有与死亡相关疾病的危险因素，总共60多项，再加上该数据库中未统计的睡眠、压力等常见可能影响健康的因素，得到影响健康的危险因素全集。
SS702、从影响健康的危险因素全集中筛选出与生活方式和日常行为相关且用户日常可控的健康危险因素，得到生活和行为相关的可控危险因素集合；
可以理解的是，步骤SS701中得到的影响健康的危险因素全集中存在很多因素是与环境污染、遗传等有关，这些因素在用户居家场景中很难采集，也很难从个人层面管控，故需要排除出去。因此最终会选择与生活方式和日常行为相关的可控危险因素项目(可以称为生活和行为相关的可控危险因素集合)，至少可以含有：最近7天运动量、每日饮食摄入(水果、蔬菜、红肉、谷物、盐)、每日饮酒量、每日吸烟量，以及体脂肪率、收缩压、舒张压、空腹血糖、血脂指标。
步骤SS702的具体的筛选方式可以有很多种，例如可以基于很多不同的标准或规则来确定哪些危险因素是可控的，或可以基于很多不同的标准或规则来确定哪些危险因素是与生活方式和日常行为相关的等。
图8为本申请实施例中一种健康危险因素的筛选方式的示例性示意图。可以先从GBD数据库中所有监测的危险因素中挑选出中国人群中突出的危险因素的集合A，同时可以从所有监测的危险因素中挑选出有研究文献数据能够支撑进行相对危险度计算的危险因素的集合B。将集合A和集合B中共有的危险因素筛选出来，可以得到用户日常可控的危险因素的集合C。再从该集合C中筛选出一些与用户生活方式和日常行为有关的危险因素，作为生活和行为相关的可控危险因素。
可以理解的是，在一些实施例中，还可以采用其他的方式从影响健康的危险因素全集中筛选出与生活方式和日常行为相关且用户日常可控的健康危险因素，此处不作限定。
SS703、基于生活和行为相关的可控危险因素集合，建立分群体健康危险因素项目和阈值数据库；
围绕步骤SS702中筛选得到的危险因素，通过指南、专家共识、文献等证据的检索、提炼和评估，可以得到每一个危险因素影响健康的划分阈值。因此可以建立健康危险因素数据库，该数据库的对应关系中可以包括：危险因素类别和类别划分标准。由于其中有些危险因素的划分标准与性别或年龄有关，因此该数据库的对应关系中还可以包括性别和年龄。若将不同的性别和年龄范围作为一个不同的群体，因此也可以将健康危险因素项目和阈值数据库称为分群体健康危险因素项目和阈值数据库。
如下表1所示，为本申请实施例中分群体健康危险因素项目和阈值数据库的一个示例性示意表：
性别 年龄范围 危险因素 危险范围值
- - 体力活动不足 体力活动水平等级为“低”
男 ≤55 体脂肪率高 ＞20％
男 ＞55 体脂肪率高 ＞20％+(2％*(age-55)/5))
女 ≤50 体脂肪率高 ＞30％
女 ＞50 体脂肪率高 ＞30％+(2％*(age-50)/5))
- ≤35 ······ ······
- ＞30 ······ ······
男 - ······ ······
女 - ······ ······
······ ······ ······ ······
- - 每日蔬菜摄入量不足 每日蔬菜摄入量＜300克
表1
在该表1中，使用“-”表示全性别或全年龄段。在实际应用中，可以采用其他的形式来标识全性别或全年龄段，例如使用特定数值“0”或“1”等，此处不作限定。
可以理解的是，上表1仅是分群体健康危险因素项目和阈值数据库的一个示例性示意，在实际应用中，可以采用其他的形式来存储该分群体健康危险因素项目和阈值数据库，此处不作限定。在一些实施例中，该分群体健康危险因素项目和阈值数据库存储的也可以不是危险因素对应的危险范围值，而是危险因素对应的健康范围值；在一些实施例中，也可以既存储危险因素对应的危险范围值，也存储危险因素对应的健康范围值，此处不作限定。
在该分群体健康危险因素项目和阈值数据库建立完成后，可以保存在云端305，也可以直接保存在电子设备301中，此处不作限定。
本申请的一些实施例中，健康危险因素项目和阈值数据库也可以被称为危险因素对应关系，同理，该分群体健康危险因素项目和阈值数据库也可以被称为多个群体的危险因素对应关系，其中不同群体的危险因素对应关系对应的年龄范围和/或性别不同。一个群体的危险因素对应关系中包括一个或多个健康危险因素与其相应的预设条件之间的对应关系。例如上表1中，与健康危险因素“体力活动不足”对应的预设条件为“体力活动水平等级为“低””；再如在“男、≤55”群体的危险因素对应关系中，与健康危险因素“体脂肪率高”对应的预设条件为“＞20％”。
可选的，在一些实施例中，对于危险因素对应关系中的任一个健康危险因素，该健康危险因素对应的预设条件中可以包含有更细分的子预设条件，来呈现其对应的健康危险因素的危险程度。
示例性的，在“男、≤55”群体的危险因素对应关系中，与健康危险因素“体脂肪率高”对应的预设条件为“＞20％”。在该预设条件中，还可以包括3个子预设条件：轻度：＞20％且≤25％；普通：＞25％且≤28％；严重：＞28％。
示例性的，在危险因素对应关系中，与健康危险因素“每日蔬菜摄入量不足”对应的预设条件为“每日蔬菜摄入量＜300克”。在该预设条件中，还可以包括2个子预设条件：不足：250克≤每日蔬菜摄入量＜300克；严重不足：每日蔬菜摄入量＜250克。
SS704、获取用户基本信息对应的第一群体的健康危险因素项目和阈值数据库；
在一些实施例中，电子设备301可以根据步骤S401中采集到的用户基本信息，根据云端305中存储的分群体健康危险因素项目和阈值数据库，从云端305获取该用户基本信息对应的第一群体的健康危险因素项目和阈值数据库。
在一些实施例中，若电子设备301中存储有分群体健康危险因素项目和阈值数据库，则 电子设备301可以基于步骤S401中采集到的用户基本信息，从该分群体健康危险因素项目和阈值数据库中确定出与该用户基本信息对应的第一群体的健康危险因素项目和阈值数据库。
示例性的，若步骤S401中采集到的用户基本信息为男、40岁、身高170cm。且步骤SS703中建立的分群体健康危险因素项目和阈值数据库为上表1所示，则电子设备301获取到的与该用户基本信息对应的第一群体的健康危险因素项目和阈值数据库可以如下表2所示：
性别 年龄范围 危险因素 危险范围值
- - 体力活动不足 体力活动水平等级为“低”
男 ≤55 体脂肪率高 ＞20％
- ＞30 ······ ······
男 - ······ ······
······ ······ ······ ······
- - 每日蔬菜摄入量不足 每日蔬菜摄入量＜300克
表2
示例性的，若步骤S401中采集到的用户基本信息为女、32岁、身高150cm。且步骤SS703中建立的分群体健康危险因素项目和阈值数据库为上表1所示，则电子设备301获取到的与该用户基本信息对应的第一群体的健康危险因素项目和阈值数据库可以如下表3所示：
性别 年龄范围 危险因素 危险范围值
- - 体力活动不足 体力活动水平等级为“低”
女 ≤50 体脂肪率高 ＞30％
女 ＞50 体脂肪率高 ＞30％+(2％*(age-50)/5))
- ≤35 ······ ······
- ＞30 ······ ······
女 - ······ ······
······ ······ ······ ······
- - 每日蔬菜摄入量不足 每日蔬菜摄入量＜300克
表3
SS705、根据用户行为数据、用户健康数据和该第一群体的健康危险因素项目和阈值数据库，确定用户健康危险因素。
电子设备301获取到该第一群体的健康危险因素项目和阈值数据库后，可以基于步骤S401中采集到的用户数据中的用户行为数据和用户健康数据，确定该用户已经暴露的健康危险因素，即用户健康危险因素。
示例性的，若用户的第一群体的健康危险因素项目和阈值数据库为上表2所示，且步骤S401中采集到的用户数据中的用户行为数据中得到的体力活动水平为“低”，符合“体力活动不足”这个危险因素的危险值范围，则“体力活动不足”这个危险因素为该用户已暴露的一项健康危险因素，应包括在该用户的用户健康危险因素中。
示例性的，若用户的第一群体的健康危险因素项目和阈值数据库为上表2所示，且步骤S401中采集到的用户数据中的用户健康数据中体脂率为25％，大于20％，符合“体脂肪率高” 这个危险因素的危险值范围，则“体脂肪率高”这个危险因素也为该用户已暴露的一项健康危险因素，应包括在该用户的用户健康危险因素中。
示例性的，若用户的第一群体的健康危险因素项目和阈值数据库为上表2所示，且步骤S401中采集到的用户数据中的用户行为数据中每日蔬菜摄入量为150g，小于300g，符合“每日蔬菜摄入量不足”这个危险因素的危险值范围，则“每日蔬菜摄入量不足”这个危险因素也为该用户已暴露的一项健康危险因素，应包括在该用户的用户健康危险因素中。
需要说明的是，电子设备301中可以预先存储或获取到用户行为数据与用户健康数据中不同种类的数据与健康危险因素项目和阈值数据库中危险因素的对应关系。从而可以基于采集到的用户行为数据与用户健康数据，去第一群体的健康危险因素项目和阈值数据库中查询确定各危险因素是否为用户已暴露的风险因素，将其添加到用户的用户健康危险因素中。
可以理解的是，在一些实施例中，若危险因素对应关系的预设条件中包含有更细分的用来呈现对应的健康危险因素的危险程度的子预设条件，则电子设备根据用户行为数据、用户健康数据和该第一群体的危险因素对应关系，不仅可以确定用户健康危险因素，而且可以确定出用户健康危险因素中一个或多个健康危险因素的暴露水平(也即危险程度)。
本申请实施例中的用户健康危险因素识别方法可以反映用户整体的健康风险状态，暴露影响健康的危险因素全集，使得能更精准的对用户健康情况进行管理。
(2)干预计划生成阶段
S403、电子设备针对用户健康危险因素生成多个周期的个性化干预计划；
如图9所示为本申请实施例中生成个性化的干预计划的一个示例性信息流向示意图。在步骤S402中识别出用户健康危险因素(例如：体力活动不足、营养不均衡或者不健康饮食习惯、睡眠不足或睡眠障碍、压力过大、吸烟、过度饮酒等)之后，结合基于采集到的用户数据得到的用户的个性化信息(例如：用户年龄、性别、运动偏好、体能测评结果、膳食习惯、疾病史等)，通过生活方式干预方案推荐规则库可以生成具体的干预计划。
例如：针对体力活动不足，可以生成运动方案和每周运动计划；
例如，针对营养不均衡或者不健康饮食习惯，可以生成饮食方案和每日膳食计划；
例如，针对睡眠不足或睡眠障碍，可以生成每日早睡打卡和睡眠改善计划；
例如，针对压力过大，可以生成每日呼吸训练打卡和减压计划；
例如，针对吸烟和过度饮酒，可以生成戒烟和限酒方案和计划。
可以理解的是，可以预先设定步骤S401中采集的用户数据中哪些类型的数据属于用户的个性化信息，也可以由电子设备基于预设算法自动从采集的用户数据中识别出用户的个性化信息，此处不作限定。
可选的，生活方式干预推荐规则库中的规则可以预先设定，也可以为一种深度学习算法，此处不作限定。
示例性的，一个生活方式干预推荐规则库的设定方式可以为：
<1>基于用户当前的体重指数(body mass index，BMI)、血压指标、血糖指标、血脂指标，同时查询用户既往病史数据库，确定运动方案和饮食方案的类型，类型如下：体重管理(BMI＞24kg/m2)、血压管理(收缩压SBP＞130mmHg或舒张压DBP＞80mmHg或已确诊高血压)、血糖管理(空腹血糖(fasting plasma glucose，FPG)＞6.1mmol/L或已确诊糖尿病)；如果该用户各项指标都在正常范围之内且没有既往高血压、糖尿病病史，运动方案和 饮食方案的类型为健康促进。如果满足多项，则确定的运动方案和饮食方案的类型优先级排序是：血糖管理、血压管理、体重管理、健康促进。
<2>每种运动方案和饮食方案类型对应的目标是对应指标能够达到正常值，对于健康促进这种类型方案的目标是保持健康(运动达标、饮食营养均衡)。
<3>针对每种运动方案和饮食方案类型和目标，通过指南、专家共识、文献等证据的检索，建立体重管理运动干预知识库、血压管理运动干预知识库、血糖管理运动干预知识库、健康促进运动干预知识库。结合《中国居民膳食营养素参考摄入量》标准，建立体重管理饮食推荐知识库、血压管理饮食推荐知识库、血糖管理饮食推荐知识库、健康促进饮食推荐知识库。
<4>对于运动，基于每种运动方案和饮食方案类型和目标，以及用户运动偏好和体适能，通过运动干预知识库生成运动计划和方案，可以包括：运动类型、运动频率、运动强度(体现为运动心率范围)、运动时间、运动禁忌等。
<5>对于饮食，基于每种运动方案和饮食方案类型和目标，以及用户饮食习惯，通过饮食推荐知识库，可以生成每日膳食计划，包括每日每餐能量摄入推荐、营养摄入比例和总量，食物类型和重量、食物推荐以及避免摄入的食物类型或食物禁忌等。
<6>对于晚睡(超过23:00)或睡眠时间严重不足(＜6小时)，可以生成早睡、规律起床两个打卡任务，并生成睡眠改善计划(例如，冥想、深度睡眠练习、睡前舒缓放松等)，还可以提供助眠音乐服务帮助用户改善睡眠障碍，养成正常的作息时间。
<7>对于压力过大，可以生成每日呼吸训练打卡任务，并提供舒眠、正念减压服务。
<8>对于不良习惯(吸烟、酗酒)，根据吸烟的频次和每次的量，可以生成戒烟计划(例如：逐日减量或立即戒断、每日推迟等)和戒烟提醒打卡任务。根据饮酒的频次和每次的量，可以生成限酒计划(例如：逐日减量等)和限酒提醒打卡任务。另外，还可以每周推送吸烟、酗酒等不良习惯带来危害的资讯消息，提高对该类人群的宣教。
可以理解的是，若电子设备在确定用户健康危险因素时，也确定出了其中一个或多个健康危险因素的暴露水平，则也可以将健康危险因素的暴露水平也作为生成干预计划的一项参考，加入到生成干预计划的输入数据中，从而对危险程度更高的健康危险因素生成更有针对性的干预计划。
具体的，电子设备可以从步骤S401采集的用户数据中提取出用户个性化信息，再根据该用户个性化信息和步骤S402中识别出的用户健康危险因子，查找生活方式干预推荐规则库，得到第一干预计划，该第一干预计划中可以包括第一运动计划、第一饮食计划和第一健康习惯打卡类任务集合。
该第一干预计划中可以包括多个周期的干预方案。例如，该第一干预计划中可以包括3个月的干预方案，其中每周(7天)的干预方案为一个周期，则该第一干预计划中包括有12个周期的干预方案。
需要说明的是，该生成的干预计划中可以包括有多种不同类型的计划。示例性的，如图10所示，为本申请实施例的干预计划中类型划分的一个示例性示意图。干预计划100可以划分为运动计划101，饮食计划102和打卡任务103，其中：
运动计划101，可用于规划一段时间的运动情况。
例如可以包括：运动类型、运动频率、运动强度、运动时间、运动禁忌等。
在一些实施例中，该运动计划101中还可以包含智能健身器材的运动方案配置信息，智 能健身器材可以根据该运动方案配置信息直接运行，而不再需要用户手动配置。
可以理解的是，运动计划101中的内容在用户实际执行时一般可以由智能穿戴设备或电子设备监测到，或者智能健身器材也可以记录一些运动计划101的执行过程或执行结果。
饮食计划102，可用于规划一段时间的饮食情况。
例如，可以包括每日每餐能量摄入推荐、营养摄入比例和总量，食物类型和重量、食物推荐以及避免摄入的食物类型或食物禁忌等。
打卡任务103，可用于规划一段时间内电子设备无法直接监测到数据的健康事项。
例如，可以包括睡眠打卡任务，减压打卡任务，戒烟打卡任务、限酒打卡任务、健康指标检测打卡任务等。
具体的，睡眠打卡任务可以包括提醒睡觉打卡和起床打卡；减压打卡任务可以包括呼吸训练打卡、正念训练打卡、冥想打卡等；戒烟打卡任务可以包括每日未吸烟打卡；限酒打卡任务可以包括每日未饮酒打卡；健康指标检测打卡任务可以包括定时提醒进行健康指标检测打卡等。
本申请实施例中的干预计划生成阶段，基于用户习惯和群体特征，提供了个性化可达成的健康干预计划，充分利用了穿戴式设备和便携式居家检测设备跟踪用户行为和健康指标，为健康管理系统中健康收益评估和效果评价提供了有效的输入。
(3)收益预测阶段
S404、电子设备根据收益预测模型，预测干预计划完成后健康指标的变化趋势并显示给用户；
电子设备可以根据收益预测模型预测出干预计划完成后健康指标的预测值。
电子设备生成多个周期的个性化干预计划后，可以根据收益预测模型预测干预计划中的部分和/或全部完成后健康指标的变化趋势，并将该变化趋势显示给用户。
可选的，该变化趋势可以是文本形式，也可以是图表形式，还可以是动画形式等，此处不作限定。
可选的，电子设备预测得到的干预计划部分和/或全部完成后健康指标的变化趋势，是由预测得到的该多个周期的干预计划中的部分和/或全部周期分别完成后的健康指标的预测值组成的。
示例性的，对于总时长3个月，总12个周期的第一干预计划，电子设备可以分别预测得到这12个周期的干预计划分别完成后健康指标的预测值，再以折线图的形式将这12个健康指标的预测值组成变化趋势显示给用户。也可以预测得到前6个周期的干预计划分别完成后的健康指标的预测值，以折线图的形式将这6个健康指标的预测值组成变化趋势显示给用户，此处不作限定。
图11为本申请实施例中收益预测模型训练过程的一个示例性信息流向示意图。要完成该过程，需要通过机器学习算法建立两个健康收益预测模型：一个是在云端建立的群体基础模型，另一个是在电子设备建立的个体健康收益预测模型。
群体基础模型的建立过程可以为：
<1>在用户知情并同意的情况下，将电子设备上的用户数据、干预计划、干预计划执行情况、干预计划执行前健康指标的值以及干预计划执行后健康指标的值等做匿名化处理后，上 传到云端；
<2>对大量属于不同用户的电子设备上传到云端的数据整理和清洗，根据年龄段、性别划分为针对不同群体的群体训练数据；
<3>如图12所示，为群体基础模型的训练过程的一个示例性示意图。
可以将针对一个群体的群体训练数据分为训练数据集和验证数据集，利用机器学习神经网络或回归算法有监督训练该群体基础模型：自变量为该群体用户每周干预计划执行情况(每日平均的运动时长、运动心率、饮食热量摄入、三大营养元素摄入量、睡眠时长、吸烟量和饮酒量等)、干预计划执行前健康指标的值(体重、BMI、体脂率、收缩压、舒张压、空腹血糖、总胆固醇、甘油三脂等)；因变量是干预计划的部分和/或全部执行后健康指标的值；机器学习神经网络或回归算法的目标函数可以为干预计划执行前后健康指标的值的均方差。
可以使用训练数据集中真实的干预计划执行后的健康指标的值去优化目标函数，使得机器学习神经网络或回归算法预测出的干预计划执行后健康指标的预测值与真实的干预计划执行后健康指标的实际值的差距在预设范围，得到待验证的群体基础模型。
然后可以使用验证数据集中的数据去验证该待验证的群体基础模型，若预测的干预计划执行后健康指标的预测值与真实的干预计划执行后的健康指标的实际值的差距在预设范围，则可以确定得到了该群体的群体基础模型。
可以理解的是，对于不同的群体，可以训练得到不同的群体基础模型，此处不作限定。
可以基于群体基础模型，建立用户的个体健康收益预测模型，其建立过程可以为：
<1>从云端获取到与电子设备的用户基本信息匹配的第一群体的群体基础模型；
<2>将电子设备中用户历史干预计划执行情况、用户执行干预计划前健康指标的值和用户执行干预计划的部分和/或全部后健康指标的值作为用户的个体训练数据，去训练得到的该第一群体的群体基础模型，对该群体基础模型进行参数微调，从而可以得到用户的个体健康收益预测模型。
下面结合上述群体基础模型和个体健康收益预测模型的建立过程，对本申请实施例中的干预计划收益预测方法进行详细描述：
图13为本申请实施例中干预计划收益预测方法的一个流程示意图。
SS1301、使用各群体训练数据，利用机器学习算法训练得到各群体基础模型；
如上群体基础模型的建立过程中的描述，云端可以使用收集的各群体训练数据，利用机器学习算法训练得到各群体基础模型。
可以理解的是，各群体中性别和年龄的划分可以与步骤SS703中的不同群体保持一致，也可以与步骤SS703中的不同群体不同，此处不作限定。
示例性的，如下表4为训练得到的多个不同群体基础模型的示例：
性别 年龄 群体基础模型
男 ≤18 群体基础模型A
男 ＞18且≤30 群体基础模型B
女 ≤18 群体基础模型C
女 ＞18且≤30 群体基础模型D
······ ······ ······
表4
在一些实施例中，各群体基础模型也可以为一个整体性的模型，性别和年龄为其中的两个变量，输入这两个变量，该整体性的模型就可以作为特定群体的群体基础模型，此处不作限定。
可以理解的是，在一些实施例中，步骤SS1301由云端服务器执行，在一些实施例中，也可以由电子设备从云端服务器获取相关数据后执行，此处不作限定。
SS1302、获取与用户基本信息匹配的第一群体的群体基础模型；
在一些实施例中，若群体基础模型是如上表4所示的按年龄和性别划分的不同群体的群体基础模型，则根据步骤S401中采集到的用户基本信息，电子设备可以获取到与该用户基本信息匹配的第一群体的群体基础模型。
示例性的，若多群体的群体基础模型如上表4所示，用户基本信息中，性别为男、年龄为25岁，则电子设备可以获取到与该信息匹配的群体基础模型B，作为该用户所属的第一群体的群体基础模型。
示例性的，若多群体的群体基础模型如上表4所示，用户基本信息中，性别为女、年龄为13岁，则电子设备可以获取到与该信息匹配的群体基础模型C，作为该用户所属的第一群体的群体基础模型。
SS1303、根据电子设备中用户的个体训练数据，训练该第一群体的群体基础模型，得到第一个体健康收益预测模型；
电子设备中用户的个体训练数据可以包括用户历史干预计划完成数据、用户执行干预计划前的健康指标数据以及用户执行干预计划后的健康指标数据。
可以理解的是，若是一个新的电子设备，其中没有用户的个体训练数据，则此时可以直接将该第一群体的群体基础模型作为第一个体健康收益预测模型。
SS1304、根据用户第一干预计划的部分和/或全部、用户当前健康指标的值和该第一个体健康收益预测模型，预测得到第一干预计划的部分和/或全部执行后健康指标的预测值。
在步骤S403中生成用户个性化的待执行干预计划后，例如生成第一干预计划后，电子设备可以将该第一干预计划的部分和/或全部和用户当前健康指标的值输入到该第一个体健康收益预测模型中，从而得到该第一干预计划执行后健康指标的预测值。
具体的，其中可以包括该第一干预计划中各周期分别执行完成后健康指标的预测值。
该健康指标的预测值可以以不同的方式来呈现，例如可以以健康指标的变化趋势来呈现，也可以以健康指标的数值来呈现等，此处不作限定。
采用本申请实施例中的干预计划收益预测方法，电子设备在制定第一干预计划后，可以直接预测出执行该第一干预计划后健康指标的预测值后，并可以将其显示在电子设备上，供用户查看，使用户了解执行该干预计划后可能的效果，从而有更强的动力去执行该干预计划。大大提高了用户执行干预计划的信心和主观能动性，同时为干预后效果评估提供了对比的输入。此外，采用端云协同的方式从粗到细建立健康收益模型也在充分保障用户隐私安全的前提下，得到更精确的端侧模型。
需要说明的是，上述第一个体健康收益预测模型的训练过程仅是本申请中的一个示例， 在实际应用中，还可以有其他的训练得到该第一个体健康收益预测模型的方式，例如可以直接使用第一用户的个体训练数据，利用机器学习算法进行模型训练得到该第一个体健康收益预测模型等，此处不作限定。
(4)干预执行阶段
S405、电子设备将干预计划下发给健康管理系统中的其他设备；
可选的，电子设备可以将步骤S403中生成的第一干预计划直接发送给健康管理系统中的各其他设备，由各设备根据自己的能力确定对该第一干预计划中的哪些类型的计划进行监控。可选的，也可以由电子设备根据该健康管理系统中各设备的能力，将第一干预计划中与各设备能力匹配的计划发送相应的设备，此处不作限定。
示例性的，健康管理系统中的其他设备可以包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材：
该电子设备可以将该第一干预计划中的穿戴干预子计划下发给该智能穿戴设备；该穿戴干预子计划为该第一干预计划中的部分或全部；该穿戴干预子计划中包括该第一运动计划中的部分或全部、和/或第一饮食计划中的部分或全部、和/或第一健康习惯打卡类任务集合中的部分或全部；该穿戴干预子计划为该第一干预计划中要通过该智能穿戴设备执行的计划；
该电子设备将该第一干预计划中的检测干预子计划下发给该健康检测设备；该检测干预子计划为该第一干预计划中的部分或全部；该检测干预子计划中包括该第一健康习惯打卡类任务集合中的健康指标检测任务中的部分或全部；该检测干预子计划为该第一干预计划中要通过该健康检测设备执行的计划；
该电子设备将该第一干预计划中的健身干预子计划下发给该智能健身器材；该健身干预子计划为该第一干预计划中的部分或全部；该健身干预子计划中包括该第一运动计划中的部分或全部；该健身干预子计划为该第一干预计划中要通过该智能健身器材执行的计划。
可选的，电子设备可以直接将第一干预计划中所有周期的干预计划都发送给各其他设备，也可以先发送第一干预计划中部分周期的干预计划给各其他设备(例如，可以仅发送第一干预计划中第一周期的干预计划给各其他设备)，此处不作限定。
示例性的，图14为本申请实施例中将不同类型的干预计划下发到不同设备的示例性示意图。若步骤S403中生成了第一干预计划，该第一干预计划中包括运动计划101，饮食计划102和打卡任务103三个类型的计划：
电子设备可以将该第一干预计划中的第一运动计划中的部分或全部发送给健康管理系统中的智能健身器材，以使得智能健身器材按照第一运动计划进行运动配置，并记录用户执行该第一运动计划的实际执行数据。
电子设备可以将该第一干预计划中的第一健康习惯打卡类任务集合中的健康指标检测任务的部分或全部发送给健康管理系统中的健康检测设备，以使得健康检测设备按照该健康指标检测任务提醒用户进行健康检测，并记录测量出的用户的健康数据。
电子设备可以将第一干预计划中的第一运动计划、第一饮食计划和第一健康习惯打卡类任务集合中的部分或全部均发送给健康管理系统中的智能穿戴设备，以使得智能穿戴设备提醒用户执行该第一干预计划，并记录干预计划的实际执行数据，监测干预计划执行过程中的用户的健康数据。
可以理解的是，在生成干预方案后，(3)收益预测阶段和(4)干预执行阶段可以没有明确的先后执行顺序：在一些实施例中，可以先执行(3)收益预测阶段，再执行(4)干预执行阶段；在一些实施例中，可以先执行(4)干预执行阶段，再执行(3)收益预测阶段；在一些实施例中，也可以(3)收益预测阶段和(4)干预执行阶段同时执行，此处不作限定。
(5)数据监测阶段
S406、智能穿戴设备监测干预计划的实际执行数据和健康数据；
示例性的，电子设备将第一干预计划发送给智能穿戴设备后，智能穿戴设备可以监测该第一干预计划的实际执行数据和健康数据：
具体的，对于第一干预计划中的第一运动计划，智能穿戴设备可以提醒用户运动，并监测运动过程中的实际执行数据。若该智能穿戴设备中有健康检测模块，还可以获取到用户执行干预计划前后的健康指标的值。
具体的，对于第一干预计划中的第一饮食计划，智能穿戴设备可以提醒用户按照该第一饮食计划饮食。
具体的，对于第一干预计划中的第一健康习惯打卡类任务集合，智能穿戴设备可以提醒用户完成打卡任务内容。对于智能穿戴设备能监测到完成情况的打卡类任务，智能穿戴设备可以在检测到用户完成该任务后，自动完成该打卡任务，此处不作限定。
S407、健康检测设备测量健康数据；
示例性的，电子设备将第一干预计划中的健康指标检测任务发送到健康检测设备后，健康检测设备可以按照该健康指标检测任务提醒用户测量健康指标，并记录每次测量的健康指标的值。
S408、智能健身器材监测干预计划的实际执行数据；
示例性的，电子设备将第一干预计划中的第一运动计划发送给智能健身器材后，智能健身器材可以按照该第一运动计划进行每次的运动配置，例如按照该第一运动计划配置好每次运动的运动类型、运动频率、运动强度、运动时间等，并可以监测执行该第一运动计划的实际执行情况。智能健身器材还可以根据该第一运动计划定时提醒用户开始运动。
S409、电子设备监测干预计划的实际执行数据和健康数据；
示例性的，电子设备也可以监测该第一干预计划的实际执行情况和健康指标的值：
具体的，对于第一干预计划中的第一运动计划，电子设备可以提醒用户运动，并监测运动过程中的实际执行数据。若该电子设备中有健康检测模块，还可以获取到用户执行干预计划前后的健康指标的值。
具体的，对于第一干预计划中的第一饮食计划，电子设备可以提醒用户按照该第一饮食计划饮食，并记录饮食情况。
具体的，对于第一干预计划中的第一健康习惯打卡类任务集合，电子设备可以提醒用户完成打卡任务内容。对于电子设备能监测到完成情况的打卡类任务，电子设备可以在检测到用户完成该任务后，自动完成该打卡任务；对于电子设备不能监测到完成情况的打卡类任务，电子设备可以接受用户的操作，完成打卡任务。
S410、智能穿戴设备、健康检测设备和智能健身器材均将监测或检测到的实际执行数据和健康数据发送给电子设备；
在第一干预计划执行过程中，健康管理系统中各设备均能将监测到的实际执行情况和健康指标的值发送给电子设备。
在电子设备确定第一干预计划中的一个周期已执行完成，获取到第一干预计划中一个周期的实际执行情况和健康指标的值后，可以执行(6)效果评估和干预计划调整阶段。
(6)效果评估和干预计划调整阶段
S411、电子设备基于收集到的干预计划执行一周期的实际执行数据和健康数据，运行效果评价模型对本周期的干预计划的实际执行情况进行效果评估和评价，并给出建议；
下面结合图15所示的生成干预计划效果评估的过程的示例性信息流向示意图和图16所示的干预计划效果评估流程示意图，对本申请实施例中的干预计划效果评估方法进行描述：
SS1601、电子设备对比第一干预计划中一周期干预计划完成后健康指标的预测值与实际完成该周期干预计划后健康指标的值的吻合度，得到干预效果评估等级；
本申请的一些实施例中，该干预效果评估等级为一种干预效果评估结果。该干预效果评估结果也可以采用除了等级外的其他形式体现，例如分值，百分比等等，此处不作限定。
基于步骤S404，在生成第一干预计划后，电子设备可以得到第一干预计划中各周期的干预计划完成后健康指标的预测值。
基于步骤S410，在执行完第一干预计划中一个周期的干预计划后，电子设备可以汇总得到该周期的干预计划执行前健康指标的值，以及该周期的干预计划实际完成后健康指标的值。
示例性的，在执行完第一干预计划中第一周期的干预计划后，电子设备可以对比该第一周期的干预计划完成后健康指标的预测值(例如，体重、BMI、体脂率、收缩压、舒张压、空腹血糖、总胆固醇、甘油三脂等)和实际完成该第一周期的干预计划后的健康指标的实际值的吻合度，得到干预效果评估等级。
具体的干预效果评估等级可以有很多种不同的等级表示方式，例如星级、百分制分数、数值等级等，不同等级也可以有很多种不同的预设划分方式，此处不作限定。
示例性的，下面以其中一种干预效果评估等级的表示方式和划分方式为例进行说明：
干预效果评估等级可以分为：S、A、B、C、D五个等级：
S的触发条件是实际健康指标达到或优于预测健康指标；
A的触发条件是实际健康指标优于执行干预计划前的健康指标，但是改善的变化值相比预测的小30％以内。可以理解为：(实际健康指标-执行干预计划前的健康指标)＜(预测健康指标-执行干预计划前的健康指标)*(1-30％)；
B的触发条件是实际健康指标优于执行干预计划前的健康指标，但是改善的变化值相比预测的小60％以内。可以理解为：(实际健康指标-执行干预计划前的健康指标)＜(预测健康指标-执行干预计划前的健康指标)*(1-60％)；
C的条件是实际健康指标优于执行干预计划前的健康指标，但是未达到B及以上的收益效果；
D的条件是实际健康指标比执行干预计划前的健康指标还要差。
SS1602、电子设备基于干预效果评估等级，结合该周期的干预计划和该周期干预计划实 际完成的行为数据，生成对第一干预计划中一项或多项计划的评价和建议。
电子设备得到第一干预计划中该周期的干预效果评估等级后，可以结合该周期的干预计划和该周期干预计划实际完成的行为数据，根据预设的评价规则库，生成对第一干预计划中一项或多项计划的评价和建议。
具体的，基于SS1602中得到的干预效果评估等级，结合对比对该周期的干预计划实际完成的行为数据和干预计划，可以针对一条或多条计划给予正向和负向的评价和建议，并收集用户没有按照计划完成的因素。并通过基于吃动平衡理论的运动饮食联合评价规则库生成运动和饮食相关细项的评价和建议。
本申请实施例中的干预效果评估方法，通过对比健康收益预测模型的推理结果和健康指标实际变化的吻合度，结合运动饮食联合评价规则库，可以对干预计划完成的实际效果进行评价，解决了健康管理无法闭环的问题。实现了健康管理的闭环，提高了用户体验，达到了长期的健康促进作用。
S412、电子设备基于评价的结果调整下个周期的干预计划；
具体的，电子设备可以根据对第一干预计划中每一项计划的评价和建议，以及用户输入的没有完成该周期的干预计划的原因，调整第一干预计划中下一周期的干预计划，从而确保干预计划可实现和效果最优。
可以理解的是，调整下一周期的干预计划可以有很多种不同的方式，可以有很多种不同的预设调整规则。这些预设调整规则的原则为：1、用户可执行；2、效益最大化。
示例性的，下面以几个调整规则为例进行说明：
调整策略1：基于用户主诉运动完成以后的主观感受(利用主观感受评估运动训练负荷，从1-10的范围内进行选择，其中，1表示非常容易，10表示非常疲劳)，用R表示用户选择的结果，调整下一周期的运动心率范围为：现阶段设定的运动心率范围+2*(R-5)。若现阶段每周运动次数小于或等于4次且R≥8，将下一周期每周运动次数调整为5次，减少每次的运动时长，但是周运动总时长不变；
调整策略2：若评估的结果是用户减脂进入平台期(即使用户完成了设定的干预计划，但是连续两周以上出现体重不下降甚至有反弹趋势)，在原本有氧运动干预的基础上增加抗阻运动课程，同时根据用户的心肺耐力水平，对有较高心肺耐力水平的低运动风险用户加入高强度间歇性训练(HIIT)计划。
可以理解的是，调整下个周期的干预计划后，电子设备可以基于调整后的干预计划，再次执行步骤S404～S412。
本申请实施例中，通过在干预计划执行前进行健康收益的预测大大增加了用户执行干预计划的主观能动性和依从性。并且能通过预测健康指标和实际执行完干预计划后的实际健康指标，以及用户行为数据，去评估生成的干预计划，进而调整干预计划，使健康管理达到了真正意义上的闭环模式，可以在有效改善用户健康的同时提高用户依从性，实现良性循环的过程。
可以理解的是，上述实施例中是以健康管理系统中各设备的用户是一个为例进行的说明。在实际应用中，若健康管理系统中各设备有多个用户(例如电子设备有多个用户)，那么上述实施例中的各步骤可以是针对不同用户的，对每个用户，可以分别执行上述步骤S401～S411，则其中采集的数据、生成的干预计划等都是只针对于多个用户中的某个用户的，不同用户的 相关数据可以以不同的用户标识区分开，此处不作限定。
上面实施例中，可以通过识别出用户健康危险因素来反映用户当前的健康情况，再生成帮助改善用户健康情况的干预计划。本申请实施例还提供了一种评估用户年龄的方法，可以采用用户评估年龄来整体性评估用户的健康风险等级，从而能对用户健康情况进行更准确的管理。
下面结合图17所示评估用户年龄过程中信息流向示例性示意图和图18所示的评估用户年龄的方法流程示意图，对本申请实施例中的评估用户年龄的方法进行描述：
可以先进行的数据准备：
数据准备(1)：可以基于疾病负担数据库(如GBD数据库)中的死因及危险因素监测数据，可以得到全国历年人群死因和人口数据，包括：
正常死因和死亡人数：用于表示每种死因的死亡人数；
群体危险因素：用于表示不同群体中平均暴露的危险因素集合；下文用J表示；
人群归因分数(Population Attributable Fraction,PAF)：用于表示暴露危险因素对人群发生疾病作用大小的统计指标，表示人群中某疾病归因于某种危险因素引起的发病占总人群全部发病的比例，也可理解为消除某危险因素后可使人群中该病的发病降低的比重；下文出现的PAF jo表示危险因素j和死因疾病o的PAF；
危险因素中介效应权重(Mediation Factor,MF)：用于表示第一危险因素在第二危险因素对疾病因果路径中起作用及其作用大小；下文出现的MF ijo表示危险因素i对危险因素j和死因疾病o的MF；
危险因素相对危险度(Relative Risk,RR)：用于表示一个群体暴露在危险因素下与未暴露在该危险因素下某疾病发生率的比值。相对危险度越大，表明暴露的效应越大，即暴露与结局的关联强度越大；RR jo表示危险因素j对死因疾病o的RR。
进一步的，可以将这些数据划分为不同群体的数据，例如一种群体划分方式可以如下表5所示：
性别 年龄 群体
男 ≤18 群体1
男 ＞18且≤30 群体2
女 ≤18 群体3
女 ＞18且≤30 群体4
······ ······ ······
表5
数据准备(2)：可以根据每个群体的数据，可以计算得到该群体的每一种死因疾病(例如：冠心病、脑卒中、糖尿病、肺癌等)的群体平均死亡率(Mortality Rate,MR o)：
例如，若对于群体2，总人数为10万人，死于死因冠心病的死亡人数为500人，则该群体的冠心病的群体平均死亡率为500/100000＝0.5％。
数据准备(3)：可以根据各群体中各死因疾病的群体平均死亡率MR o，计算出各群体的群体预期寿命SE：
<1>根据群体平均死亡率MR o，可以计算出群体因为各死因疾病在往后每一年(最长考 虑120年寿命)的死亡概率q o；
示例性的，一种死亡概率q o的计算方式可以如下公式(1)所示：
Figure PCTCN2022113387-appb-000001
其中o表示单个死因疾病；
<2>综合每一种死因疾病的死亡概率q o，可以得到群体在往后每一年的全因死亡概率Q；
示例性的，一种全因死亡概率Q的计算方式可以如下公式(2)所示：
Figure PCTCN2022113387-appb-000002
其中M表示死因疾病的集合；
<3>根据群体在往后每一年的全因死亡概率Q，通过数学期望方法可以算出群体预期寿命SE。
示例性的，一种群体预期寿命SE的计算方式可以如下：
先使用如下公式(3)计算出该群体在第i岁没有死亡的概率*i的值：
t i＝(1-Q a)×(1-Q a+1)×(1-Q a+2)×…×(1-Q i-1)×Q i×i···公式(3)
其中a是用户实际年龄，i表示群体寿命(范围为a～120)，t i表示该群体活到i岁的概率乘以i岁，活到i岁的另一种表达是a岁～i-1岁没有死亡，在i岁死亡。
再使用如下公式(4)累加t a至t 120得到群体预期寿命SE：
SE＝t a+t a+1+t a+2+…+t 120···公式(4)
数据准备(4)：可以根据各群体的每一种死因疾病的群体平均死亡率MR o、人群归因分数PAF jo以及危险因素中介效应权重MF ijo，确定各群体无风险暴露情况下每一种死因的基线死亡率BD o；其中，无风险暴露情况是指假设群体中的每个人都没有健康风险因素的情况。
示例性的，一种群体无风险暴露情况下每一种死因的基线死亡率的计算方式可以如下：
可以先按照如下公式(5)计算出暴露的危险因素集合的人群归因分数PAF Jo：
Figure PCTCN2022113387-appb-000003
其中J为群体暴露的危险因素集合；I为危险因素j对死因疾病o作用有关联的危险因素集合；
再按照如下公式(6)计算出群体无风险暴露情况下每一种死因的基线死亡率BD o：
BD o＝MR o×(1-PAF Jo)···公式(6)
数据准备(5)：可以根据各群体无风险暴露情况下每一种死因的基线死亡率BD o，确定因为各群体无风险暴露情况下因为每一种死因在往后每一年(最长考虑120年寿命)的死亡概率
Figure PCTCN2022113387-appb-000004
示例性的，一种因为各死因在往后每一年的死亡概率
Figure PCTCN2022113387-appb-000005
的计算方式可以如下公式(7)所示：
Figure PCTCN2022113387-appb-000006
可以理解的是，上述数据准备可以在云端完成，也可以由电子设备获取相关设备后完成，可以全部完成，也可以仅完成其中的部分，此处不作限定。
在准备好一些数据后，可以执行如下步骤：
S1801、确定用户所属的第一群体的每一种死因疾病的群体平均死亡概率；
具体的，可以先基于采集到的用户基本信息，确定用户所属的第一群体，再获取该第一群体的每一种死因疾病的群体平均死亡概率。
示例性的，若用户基本信息中性别为男，年龄为25，且群体划分方式采用上表5所示的群体划分方式，则可以确定用户属于群体2：18～30岁，男性。然后获取该群体2对应的每一种死因疾病的群体平均死亡概率。
若云端已经预先准备好各群体的每一种死因疾病的群体平均死亡概率，则电子设备可以直接从云端获取该群体2的每一种死因疾病的群体平均死亡概率。
若云端没有预先准备好各群体的每一种死因疾病的群体平均死亡概率，则电子设备可以获取需要的数据后，按照上述数据准备(3)中的计算方式，计算出该群体2的每一种死因疾病的群体平均死亡概率。
S1802、确定第一群体无风险暴露情况下每一种死因疾病的基线死亡概率；
若云端已经预先准备好各群体无风险暴露情况下每一种死因疾病的基线死亡概率，则电子设备可以直接从云端获取该第一群体无风险暴露情况下每一种死因疾病的基线死亡概率。
若云端没有预先准备好各群体的每一种死因疾病的群体平均死亡概率，则电子设备可以获取需要的数据后，按照上述数据准备(4)中的计算方式，计算出该第一群体无风险暴露情况下每一种死因疾病的基线死亡概率。
S1803、确定第一群体的群体预期寿命；
若云端已经预先准备好各群体的群体预期寿命，则电子设备可以直接从云端获取该第一群体的群体预期寿命。
若云端没有预先准备好各群体的群体预期寿命，则电子设备可以获取需要的数据后，按照上述数据准备(3)中的计算方式，计算出该第一群体的群体预期寿命。
S1804、根据用户健康危险因素和该第一群体的基线死亡概率，确定用户预期寿命；
可以理解的是，由于用户健康危险因素不同，用户健康危险因素中各危险因素的数值或暴露等级不同，因此，可以确定出不同的用户预期寿命。
示例性的，一种计算过程可以如下所示：
<1>先根据识别出的用户健康危险因素，确定用户整体暴露的危险因素集合与各个死因疾病的相对危险度(在本申请中可简称：用户整体危险因素相对危险度)RR Ko；
可选的，该用户健康危险因素可以采用上述步骤S402中的方式识别出来，此处不作限定。
示例性的，一种计算用户整体危险因素相对风险度RR Ko的计算方式可以如下公式(8) 所示：
Figure PCTCN2022113387-appb-000007
其中，K为用户健康危险因素中危险因素的集合；L为危险因素k对死因疾病o作用有关联的危险因素集合；m k为危险因素k在用户中暴露情况相对该危险因素理论最小风险暴露水平(Theoretical minimum risk exposure level,TMREL)的暴露权重；
可选的，还可以根据公式(9)计算用户健康危险因素中各危险因素和全死因疾病对应的相对危险度(RR kM)。基于RR kM从高到低的顺序对用户暴露的危险因素进行排序。
Figure PCTCN2022113387-appb-000008
<2>可以基于用户整体危险因素相对风险度RR Ko，计算出用户因为各死因疾病往后每一年(最长考虑120年寿命)的死亡概率q′ 0；
示例性的，一种根据用户整体危险因素相对风险度RR Ko计算出用户因为各死因疾病往后每一年的死亡概率q′ o的计算方式可以如下公式(10)所示：
Figure PCTCN2022113387-appb-000009
<3>可以综合每一种死因疾病的用户死亡概率q′ o，得到用户在往后每一年的全因死亡概率Q′；
示例性的，一种根据每一种死因疾病的用户死亡概率q′ o计算出用户在往后每一年的全因死亡概率Q′的计算方式可以如下公式(11)所示：
Figure PCTCN2022113387-appb-000010
其中，M表示死因的集合。
<4>可以根据用户在各年内的全因死亡概率Q′，通过数学期望方法算出用户预期寿命AE；
示例性的，一种用户预期寿命AE的计算方式可以如下公式(12)和公式(13)所示：
t′ i＝(1-Q′ a)×(1-Q′ a+1)×(1-Q a+2)×…×(1-Q′ i-1)×Q′ i×i···公式(12)
其中，a是用户实际年龄，i表示用户寿命(范围为a～120)，t′ i表示该用户活到i岁的概率乘以i岁，活到i岁的另一种表达是a岁～i-1岁没有死亡，在i岁死亡。
公式(12)中1-Q′ i-1表示用户在i-1岁没有死亡的概率，Q′ i表示用户在i岁死亡的概率。
如下公式(13)所示，累加t′ a至t′ 120得到用户预期寿命AE：
AE＝t′ a+t′ a+1+t′ a+2+…+t′ 120···公式(13)
S1805、根据第一群体的群体预期寿命和用户预期寿命，确定用户健康年龄。
示例性的，一种计算方式可以为：可以将用户实际年龄a加上该第一群体预期寿命，再减去用户预期寿命，得到用户健康年龄。
例如，若用户实际年龄为25岁，计算出的第一群体的群体预期寿命为70岁，计算出的用户预期寿命为60岁，则用户健康年龄为25+70-60＝35岁。
本申请实施例中，通过确定出用户健康年龄，可以在识别暴露用户危险因素的同时，给用户整体健康风险提供可度量的风险水平。且能建立用户健康危险因素排序，为健康管理中干预策略的优先级提供依据。
本申请的一些实施例中，该用户健康年龄也可以被称为用户评估年龄。
上述用户健康年龄评估方法可以应用在本申请健康管理方法的各个阶段：
在图4所示的健康管理方法的(1)评估阶段：
电子设备可以根据采集的用户数据，识别用户健康危险因素以及用户健康年龄。
在图4所示的健康管理方法的(2)干预计划生成阶段：
电子设备可以根据用户健康危险因素及其中各危险因素的排序，生成多个周期的个性化干预计划。
在图4所示的健康管理方法的(3)收益预测阶段：
电子设备输入收益健康模型的数据可以包括(1)评估阶段识别出的用户健康年龄，预测得到的健康指标中可以包括干预计划执行后的用户健康年龄。
可以理解的是，在执行了干预计划后，用户健康危险因素中各危险因素的值会发生变化，因此，有用户健康危险因素参与计算的用户健康年龄也会相应的变化。
下面结合图19所示的信息流向示意图，对本申请实施例中另一个干预计划收益预测方法进行具体说明：
在收益预测模型的训练过程中：
对于在云端建立的群体基础模型，其过程可以为：
<1>在用户知情并同意的情况下，将电子设备上的用户数据、干预计划、干预计划执行情况、干预计划执行前健康指标的值、干预计划执行前的用户健康年龄以及干预计划执行后的健康指标的值、干预计划执行后的用户健康年龄等做匿名化处理后，上传到云端；
<2>对由大量移动属于不同用户的电子设备上传到云端的数据整理和清洗，根据年龄段、性别划分为针对不同群体的群体训练数据；
<3>如图12所示，为群体基础模型的训练过程的一个示例性示意图。
可以将针对一个群体的群体训练数据分为训练数据集和验证数据集，利用机器学习神经网络或回归算法有监督训练该群体基础模型：自变量为该群体用户每周干预计划执行情况(每日平均的运动时长、运动心率、饮食热量摄入、三大营养元素摄入量、睡眠时长、吸烟量和饮酒量等)、干预计划执行前健康指标的值(体重、BMI、体脂率、收缩压、舒张压、空腹血糖、总胆固醇、甘油三脂等)、干预计划执行前的用户健康年龄；因变量是干预计划执行后的健康指标的值、干预计划执行后的用户健康年龄；机器学习神经网络或回归算法的目标函数可以为干预计划执行前后的用户健康年龄的均方差。
可以使用训练数据集中真实的干预计划执行后的用户健康指标的值和用户健康年龄去优化目标函数，使得机器学习神经网络或回归算法预测出的干预计划执行后的用户健康指标的 预测值和用户健康年龄与真实的干预计划执行后的用户健康指标的值和用户健康年龄的差距在预设范围，得到待验证的群体基础模型。
然后可以使用验证数据集中的数据去验证该待验证的群体基础模型，若预测的干预计划执行后的用户健康指标的预测值和预测用户健康年龄与真实的干预计划执行后的用户健康指标的值和用户健康年龄的差距在预设范围，则可以确定得到了该群体的群体基础模型。
可以理解的是，对于不同的群体，可以训练得到不同的群体基础模型，此处不作限定。
基于群体基础模型，对于在电子设备建立的个体健康收益预测模型，其过程可以为：
<1>从云端获取到与电子设备的用户基本信息匹配的第一群体的群体基础模型；
<2>将电子设备中用户历史干预计划执行情况、用户执行干预计划前健康指标的值、用户执行干预计划前的用户健康年龄、用户执行干预计划后健康指标的值和用户执行干预计划后的用户健康年龄作为用户的个体训练数据，去训练得到的该第一群体的群体基础模型，对该群体基础模型进行参数微调，从而可以得到用户的个体健康收益预测模型。
下面结合上述群体基础模型和个体健康收益预测模型的建立过程，对本申请实施例中的干预计划收益预测方法进行详细描述：
图20为本申请实施例中干预计划收益预测方法的另一个流程示意图。
SS2001、使用各群体训练数据，利用机器学习算法训练得到各群体基础模型；
可以理解的是，如上群体基础模型的训练过程中所述，各群体训练数据中可以包括干预计划执行前的用户健康年龄以及干预计划执行后的用户健康年龄。因此，训练出的各群体基础模型的输入中可以包括干预计划执行前的用户健康年龄，输出中可以包括干预计划执行后的用户健康年龄。
其他过程与步骤SS1301中类似，此处不作赘述。
SS2002、获取与用户基本信息匹配的第一群体的群体基础模型；
与步骤SS1302中类似，此处不作赘述。
SS2003、根据电子设备中用户的个体训练数据，训练该第一群体的群体基础模型，得到第一个体健康收益预测模型；
可以理解的是，如上个体健康收益预测模型的训练过程中所述，用户的个体训练数据中可以包括干预计划执行前的用户健康年龄以及干预计划执行后的用户健康年龄。因此，训练出的第一个体健康收益预测模型的输入中可以包括干预计划执行前的用户健康年龄，输出中可以包括干预计划执行后的用户健康年龄。
其他过程与步骤SS1303中类似，此处不作赘述。
SS2004、根据该第一个体健康收益预测模型、用户当前健康指标的值和当前用户健康年龄，预测得到第一干预计划执行后健康指标的预测值和预测用户健康年龄。
在步骤S403中生成用户个性化的待执行干预计划后，例如生成第一干预计划后，电子设备可以将该第一干预计划、用户当前健康指标的值以及当前用户健康年龄输入到该第一个体健康收益预测模型中，从而得到该第一干预计划执行后健康指标的预测值和预测用户健康年龄。
其他过程与步骤SS1304中类似，此处不作赘述。
本申请实施例中，在收益预测模型中加入了用户健康年龄的这个参数，由于用户健康年龄能整体性的反映用户的健康情况，从而可以使得对干预计划执行效果的预测更加准确。
在图4所示的健康管理方法的(6)效果评估和干预计划调整阶段：
电子设备在进行干预计划效果评估时不仅可以对比一周期干预计划完成后的预测健康指标与实际完成该周期干预计划后的实际健康指标的吻合度，而且可以对比该周期干预计划完成后的预测用户健康年龄与实际完成该周期干预计划后的实际用户健康年龄的吻合度，从而得到对干预计划的效果评估结果。
下面结合图21所示的干预计划效果评估信息流向示意图，以及图22所述的干预计划效果评估方法流程示意图，对本申请实施例中另一个干预计划效果评估方法进行描述：
SS2201、电子设备对比第一干预计划中一周期干预计划完成后健康指标的预测值与实际完成该周期干预计划后健康指标的值的吻合度，以及该周期干预计划完成后的预测用户健康年龄和实际完成该周期干预计划后的实际用户健康年龄的吻合度，得到干预效果评估等级；
可以理解的是，根据结合健康指标与用户健康年龄的吻合度来确定干预效果评估等级，具体的确定方式可以有很多：例如可以设定不同的健康指标吻合度分别对应不同的干预效果评估等级，不同的用户健康年龄也分别对应不同的干预评估等级，选择两者之间较低的干预评估等级作为最终的干预效果评估等级。例如，若由健康指标吻合度确定的干预效果评估等级为B，由用户健康年龄吻合度确定的干预效果评估等级为C，则可以确定最终的干预效果评估等级为C。再如，可以为不同的干预效果评估等级分别对应健康指标的吻合度和用户健康年龄的吻合度，只有同时满足该健康指标的吻合度和用户健康年龄的吻合度，才被确定为属于相应的干预效果评估等级。还可以有很多其他的确定方式，此处不作限定。
其他过程与步骤SS1601中类似，此处不作赘述。
SS2202、电子设备基于干预效果评估等级，结合该周期的干预计划和该周期干预计划实际完成的行为数据，生成对第一干预计划中一项或多项计划的评价和建议。
与步骤SS1602类似，此处不作赘述。
本申请实施例中的干预效果评估方法，在评估过程中采用的用户健康年龄，由于用户健康年龄能整体性反映用户健康情况，因此使得对干预计划的评估的结果更加准确。
下面结合上述步骤，以及图23所示的软件模块构架示意图，介绍本申请实施例中健康管理系统的一种示例性软件系统组成构架：
该健康管理系统可以包括：数据采集模块、健康危险因素识别和健康年龄评估模块、个性化生活方式干预方案生成模块、健康收益预测模块、干预方案执行监测模块、基于行为的依从性管理模块以及实际执行效果联合评价模块：
其中，该数据采集模块可以采集用户数据，包括：用户基本信息，例如年龄、性别；用户健康数据，例如血压、血糖、血脂、体成分、压力、睡眠情况等；用户行为数据，例如运动、饮食、睡眠习惯、饮酒、吸烟、健康问卷数据等。该数据采集模块可以执行上述步骤S401。
该健康危险因素识别和健康年龄评估模块，可以识别用户健康危险因素，也可以计算出用户健康年龄，还可以对用户健康危险因素按危险度排序。该健康危险因素识别和健康年龄 评估模块可以执行上述步骤S402和图18所示的本申请实施例中的用户健康年龄评估方法。
该个性化生活方式干预方案生成模块，可以生成各种有益于用户健康的干预计划，例如运动干预方案和计划、饮食干预方案和计划、睡眠干预方案、心理和压力干预方案、吸烟、过度饮酒等不良生活方式干预方案等。该个性化生活方式干预方案生成模块可以执行上述步骤S403。
该健康收益预测模块，可以预测干预计划完成后的健康指标，以及干预计划完成后的用户健康年龄。该健康收益预测模块可以执行图13和图22所示的本申请实施例中的干预计划收益预测方法。
该干预方案执行检测模块，可以对干预计划执行前后以及执行过程中的运动、饮食等行为、以及相关健康指标进行检测，得到干预计划的实际执行数据和健康指标。该干预方案执行检测模块可以执行上述步骤S406～S410。
该基于行为的依从性管理模块，可以在检测到用户按时按量执行了干预计划后，对用户进行积分奖励。
该实际执行效果联合评价模块，可以对干预计划的执行效果进行效果评估，并与饮食情况连接进行综合评价，还可以基于评价结果动态调整干预计划。该实际执行效果联合评价模块可以执行上述步骤S411～S412以及本申请实施例中的干预计划效果评估方法。
下面介绍本申请实施例提供中健康管理系统中的示例性电子设备100。
图24是本申请实施例提供的电子设备100的结构示意图。
下面以电子设备100为例对实施例进行具体说明。应该理解的是，电子设备100可以具有比图中所示的更多的或者更少的部件，可以组合两个或多个的部件，或者可以具有不同的部件配置。图中所示出的各种部件可以在包括一个或多个信号处理和/或专用集成电路在内的硬件、软件、或硬件和软件的组合中实现。
电子设备100可以包括：处理器110，外部存储器接口120，内部存储器121，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194以及用户标识模块(subscriber identification module，SIM)卡接口195等。其中传感器模块180可以包括压力传感器180A，陀螺仪传感器180B，气压传感器180C，磁传感器180D，加速度传感器180E，距离传感器180F，接近光传感器180G，指纹传感器180H，温度传感器180J，触摸传感器180K，环境光传感器180L，骨传导传感器180M等。
可以理解的是，本申请实施例示意的结构并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，存储器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。
其中，控制器可以是电子设备100的神经中枢和指挥中心。控制器可以根据指令操作码和时序信号，产生操作控制信号，完成取指令和执行指令的控制。
处理器110中还可以设置存储器，用于存储指令和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或数据。如果处理器110需要再次使用该指令或数据，可从所述存储器中直接调用。避免了重复存取，减少了处理器110的等待时间，因而提高了系统的效率。
在一些实施例中，处理器110可以包括一个或多个接口。接口可以包括集成电路(inter-integrated circuit，I2C)接口，集成电路内置音频(inter-integrated circuit sound，I2S)接口，脉冲编码调制(pulse code modulation，PCM)接口，通用异步收发传输器(universal asynchronous receiver/transmitter，UART)接口，移动产业处理器接口(mobile industry processor interface，MIPI)，通用输入输出(general-purpose input/output，GPIO)接口，用户标识模块(subscriber identity module，SIM)接口，和/或通用串行总线(universal serial bus，USB)接口等。
I2C接口是一种双向同步串行总线，包括一根串行数据线(serial data line，SDA)和一根串行时钟线(derail clock line，SCL)。在一些实施例中，处理器110可以包含多组I2C总线。处理器110可以通过不同的I2C总线接口分别耦合触摸传感器180K，充电器，闪光灯，摄像头193等。例如：处理器110可以通过I2C接口耦合触摸传感器180K，使处理器110与触摸传感器180K通过I2C总线接口通信，实现电子设备100的触摸功能。
I2S接口可以用于音频通信。在一些实施例中，处理器110可以包含多组I2S总线。处理器110可以通过I2S总线与音频模块170耦合，实现处理器110与音频模块170之间的通信。在一些实施例中，音频模块170可以通过I2S接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。
PCM接口也可以用于音频通信，将模拟信号抽样，量化和编码。在一些实施例中，音频模块170与无线通信模块160可以通过PCM总线接口耦合。在一些实施例中，音频模块170也可以通过PCM接口向无线通信模块160传递音频信号，实现通过蓝牙耳机接听电话的功能。所述I2S接口和所述PCM接口都可以用于音频通信。
UART接口是一种通用串行数据总线，用于异步通信。该总线可以为双向通信总线。它将要传输的数据在串行通信与并行通信之间转换。在一些实施例中，UART接口通常被用于连接处理器110与无线通信模块160。例如：处理器110通过UART接口与无线通信模块160中的蓝牙模块通信，实现蓝牙功能。在一些实施例中，音频模块170可以通过UART接口向无线通信模块160传递音频信号，实现通过蓝牙耳机播放音乐的功能。
MIPI接口可以被用于连接处理器110与显示屏194，摄像头193等外围器件。MIPI接口包括摄像头串行接口(camera serial interface，CSI)，显示屏串行接口(display serial interface，DSI)等。在一些实施例中，处理器110和摄像头193通过CSI接口通信，实现电子设备100的拍摄功能。处理器110和显示屏194通过DSI接口通信，实现电子设备100的显示功能。
GPIO接口可以通过软件配置。GPIO接口可以被配置为控制信号，也可被配置为数据信号。在一些实施例中，GPIO接口可以用于连接处理器110与摄像头193，显示屏194，无线通信模块160，音频模块170，传感器模块180等。GPIO接口还可以被配置为I2C接口，I2S接口，UART接口，MIPI接口等。
SIM接口可以被用于与SIM卡接口195通信，实现传送数据到SIM卡或读取SIM卡中数据的功能。
USB接口130是符合USB标准规范的接口，具体可以是Mini USB接口，Micro USB接口，USB Type C接口等。USB接口130可以用于连接充电器为电子设备100充电，也可以用于电子设备100与外围设备之间传输数据。也可以用于连接耳机，通过耳机播放音频。该接口还可以用于连接其他电子设备，例如AR设备等。
可以理解的是，本申请实施例示意的各模块间的接口连接关系，只是示意性说明，并不构成对电子设备100的结构限定。在本申请另一些实施例中，电子设备100也可以采用上述实施例中不同的接口连接方式，或多种接口连接方式的组合。
充电管理模块140用于从充电器接收充电输入。其中，充电器可以是无线充电器，也可以是有线充电器。
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，内部存储器121，外部存储器，显示屏194，摄像头193，和无线通信模块160等供电。
电子设备100的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160，调制解调处理器以及基带处理器等实现。
天线1和天线2用于发射和接收电磁波信号。电子设备100中的每个天线可用于覆盖单个或多个通信频带。不同的天线还可以复用，以提高天线的利用率。例如：可以将天线1复用为无线局域网的分集天线。在另外一些实施例中，天线可以和调谐开关结合使用。
移动通信模块150可以提供应用在电子设备100上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。移动通信模块150可以由天线1接收电磁波，并对接收的电磁波进行滤波，放大等处理，传送至调制解调处理器进行解调。移动通信模块150还可以对经调制解调处理器调制后的信号放大，经天线1转为电磁波辐射出去。在一些实施例中，移动通信模块150的至少部分功能模块可以被设置于处理器110中。在一些实施例中，移动通信模块150的至少部分功能模块可以与处理器110的至少部分模块被设置在同一个器件中。
调制解调处理器可以包括调制器和解调器。其中，调制器用于将待发送的低频基带信号调制成中高频信号。解调器用于将接收的电磁波信号解调为低频基带信号。随后解调器将解调得到的低频基带信号传送至基带处理器处理。低频基带信号经基带处理器处理后，被传递给应用处理器。应用处理器通过音频设备(不限于扬声器170A，受话器170B等)输出声音信号，或通过显示屏194显示图像或视频。在一些实施例中，调制解调处理器可以是独立的器件。在另一些实施例中，调制解调处理器可以独立于处理器110，与移动通信模块150或其他功能模块设置在同一个器件中。
无线通信模块160可以提供应用在电子设备100上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块160可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块160经由天线2接收电磁波，将电磁波信号调频以及滤波处理，将处理后的信号发送到处理器110。无线通信模块160还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线2转为电磁波辐射出去。
在一些实施例中，电子设备100的天线1和移动通信模块150耦合，天线2和无线通信 模块160耦合，使得电子设备100可以通过无线通信技术与网络以及其他设备通信。所述无线通信技术可以包括全球移动通讯系统(global system for mobile communications，GSM)，通用分组无线服务(general packet radio service，GPRS)，码分多址接入(code division multiple access，CDMA)，宽带码分多址(wideband code division multiple access，WCDMA)，时分码分多址(time-division code division multiple access，TD-SCDMA)，长期演进(long term evolution，LTE)，BT，GNSS，WLAN，NFC，FM，和/或IR技术等。所述GNSS可以包括全球卫星定位系统(global positioning system，GPS)，全球导航卫星系统(global navigation satellite system，GLONASS)，北斗卫星导航系统(beidou navigation satellite system，BDS)，准天顶卫星系统(quasi-zenith satellite system，QZSS)和/或星基增强系统(satellite based augmentation systems，SBAS)。
电子设备100通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。GPU用于执行数学和几何计算，用于图形渲染。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。在一些实施例中，电子设备100可以包括1个或N个显示屏194，N为大于1的正整数。
电子设备100可以通过ISP，摄像头193，视频编解码器，GPU，显示屏194以及应用处理器等实现拍摄功能。
ISP用于处理摄像头193反馈的数据。例如，拍照时，打开快门，光线通过镜头被传递到摄像头感光元件上，光信号转换为电信号，摄像头感光元件将所述电信号传递给ISP处理，转化为肉眼可见的图像。ISP还可以对图像的噪点，亮度，肤色进行算法优化。ISP还可以对拍摄场景的曝光，色温等参数优化。在一些实施例中，ISP可以设置在摄像头193中。
摄像头193用于捕获静态图像或视频。物体通过镜头生成光学图像投射到感光元件。感光元件可以是电荷耦合器件(charge coupled device，CCD)或互补金属氧化物半导体(complementary metal-oxide-semiconductor，CMOS)光电晶体管。感光元件把光信号转换成电信号，之后将电信号传递给ISP转换成数字图像信号。ISP将数字图像信号输出到DSP加工处理。DSP将数字图像信号转换成标准的RGB，YUV等格式的图像信号。在一些实施例中，电子设备100可以包括1个或N个摄像头193，N为大于1的正整数。
数字信号处理器用于处理数字信号，除了可以处理数字图像信号，还可以处理其他数字信号。例如，当电子设备100在频点选择时，数字信号处理器用于对频点能量进行傅里叶变换等。
视频编解码器用于对数字视频压缩或解压缩。电子设备100可以支持一种或多种视频编解码器。这样，电子设备100可以播放或录制多种编码格式的视频，例如：动态图像专家组(moving picture experts group，MPEG)1，MPEG2，MPEG3，MPEG4等。
NPU为神经网络(neural-network，NN)计算处理器，通过借鉴生物神经网络结构，例如借鉴人脑神经元之间传递模式，对输入信息快速处理，还可以不断的自学习。通过NPU可以实现电子设备100的智能认知等应用，例如：图像识别，人脸识别，语音识别，文本理解等。
内部存储器121可以包括一个或多个随机存取存储器(random access memory，RAM)和一个或多个非易失性存储器(non-volatile memory，NVM)。
随机存取存储器可以包括静态随机存储器(static random-access memory，SRAM)、动态随机存储器(dynamic random access memory，DRAM)、同步动态随机存储器(synchronous dynamic random access memory,SDRAM)、双倍资料率同步动态随机存取存储器(double data rate synchronous dynamic random access memory,DDR SDRAM，例如第五代DDR SDRAM一般称为DDR5SDRAM)等；
非易失性存储器可以包括磁盘存储器件、快闪存储器(flash memory)。
快闪存储器按照运作原理划分可以包括NOR FLASH、NAND FLASH、3D NAND FLASH等，按照存储单元电位阶数划分可以包括单阶存储单元(single-level cell,SLC)、多阶存储单元(multi-level cell,MLC)、三阶储存单元(triple-level cell,TLC)、四阶储存单元(quad-level cell,QLC)等，按照存储规范划分可以包括通用闪存存储(英文：universal flash storage，UFS)、嵌入式多媒体存储卡(embedded multi media Card，eMMC)等。
随机存取存储器可以由处理器110直接进行读写，可以用于存储操作系统或其他正在运行中的程序的可执行程序(例如机器指令)，还可以用于存储用户及应用程序的数据等。
非易失性存储器也可以存储可执行程序和存储用户及应用程序的数据等，可以提前加载到随机存取存储器中，用于处理器110直接进行读写。
外部存储器接口120可以用于连接外部的非易失性存储器，实现扩展电子设备100的存储能力。外部的非易失性存储器通过外部存储器接口120与处理器110通信，实现数据存储功能。例如将音乐，视频等文件保存在外部的非易失性存储器中。
电子设备100可以通过音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，以及应用处理器等实现音频功能。例如音乐播放，录音等。
音频模块170用于将数字音频信息转换成模拟音频信号输出，也用于将模拟音频输入转换为数字音频信号。音频模块170还可以用于对音频信号编码和解码。在一些实施例中，音频模块170可以设置于处理器110中，或将音频模块170的部分功能模块设置于处理器110中。
扬声器170A，也称“喇叭”，用于将音频电信号转换为声音信号。电子设备100可以通过扬声器170A收听音乐，或收听免提通话。
受话器170B，也称“听筒”，用于将音频电信号转换成声音信号。当电子设备100接听电话或语音信息时，可以通过将受话器170B靠近人耳接听语音。
麦克风170C，也称“话筒”，“传声器”，用于将声音信号转换为电信号。当拨打电话或发送语音信息时，用户可以通过人嘴靠近麦克风170C发声，将声音信号输入到麦克风170C。电子设备100可以设置至少一个麦克风170C。在另一些实施例中，电子设备100可以设置两个麦克风170C，除了采集声音信号，还可以实现降噪功能。在另一些实施例中，电子设备100还可以设置三个，四个或更多麦克风170C，实现采集声音信号，降噪，还可以识别声音来源，实现定向录音功能等。
耳机接口170D用于连接有线耳机。耳机接口170D可以是USB接口130，也可以是3.5mm的开放移动电子设备平台(open mobile terminal platform，OMTP)标准接口，美国蜂窝电信工业协会(cellular telecommunications industry association of the USA，CTIA)标准接口。
压力传感器180A用于感受压力信号，可以将压力信号转换成电信号。在一些实施例中， 压力传感器180A可以设置于显示屏194。压力传感器180A的种类很多，如电阻式压力传感器，电感式压力传感器，电容式压力传感器等。电容式压力传感器可以是包括至少两个具有导电材料的平行板。当有力作用于压力传感器180A，电极之间的电容改变。电子设备100根据电容的变化确定压力的强度。当有触摸操作作用于显示屏194，电子设备100根据压力传感器180A检测所述触摸操作强度。电子设备100也可以根据压力传感器180A的检测信号计算触摸的位置。在一些实施例中，作用于相同触摸位置，但不同触摸操作强度的触摸操作，可以对应不同的操作指令。例如：当有触摸操作强度小于第一压力阈值的触摸操作作用于短消息应用图标时，执行查看短消息的指令。当有触摸操作强度大于或等于第一压力阈值的触摸操作作用于短消息应用图标时，执行新建短消息的指令。
陀螺仪传感器180B可以用于确定电子设备100的运动姿态。在一些实施例中，可以通过陀螺仪传感器180B确定电子设备100围绕三个轴(即，x，y和z轴)的角速度。陀螺仪传感器180B可以用于拍摄防抖。示例性的，当按下快门，陀螺仪传感器180B检测电子设备100抖动的角度，根据角度计算出镜头模组需要补偿的距离，让镜头通过反向运动抵消电子设备100的抖动，实现防抖。陀螺仪传感器180B还可以用于导航，体感游戏场景。
气压传感器180C用于测量气压。在一些实施例中，电子设备100通过气压传感器180C测得的气压值计算海拔高度，辅助定位和导航。
磁传感器180D包括霍尔传感器。电子设备100可以利用磁传感器180D检测翻盖皮套的开合。在一些实施例中，当电子设备100是翻盖机时，电子设备100可以根据磁传感器180D检测翻盖的开合。进而根据检测到的皮套的开合状态或翻盖的开合状态，设置翻盖自动解锁等特性。
加速度传感器180E可检测电子设备100在各个方向上(一般为三轴)加速度的大小。当电子设备100静止时可检测出重力的大小及方向。还可以用于识别电子设备姿态，应用于横竖屏切换，计步器等应用。
距离传感器180F，用于测量距离。电子设备100可以通过红外或激光测量距离。在一些实施例中，拍摄场景，电子设备100可以利用距离传感器180F测距以实现快速对焦。
接近光传感器180G可以包括例如发光二极管(LED)和光检测器，例如光电二极管。发光二极管可以是红外发光二极管。电子设备100通过发光二极管向外发射红外光。电子设备100使用光电二极管检测来自附近物体的红外反射光。当检测到充分的反射光时，可以确定电子设备100附近有物体。当检测到不充分的反射光时，电子设备100可以确定电子设备100附近没有物体。电子设备100可以利用接近光传感器180G检测用户手持电子设备100贴近耳朵通话，以便自动熄灭屏幕达到省电的目的。接近光传感器180G也可用于皮套模式，口袋模式自动解锁与锁屏。
环境光传感器180L用于感知环境光亮度。电子设备100可以根据感知的环境光亮度自适应调节显示屏194亮度。环境光传感器180L也可用于拍照时自动调节白平衡。环境光传感器180L还可以与接近光传感器180G配合，检测电子设备100是否在口袋里，以防误触。
指纹传感器180H用于采集指纹。电子设备100可以利用采集的指纹特性实现指纹解锁，访问应用锁，指纹拍照，指纹接听来电等。
温度传感器180J用于检测温度。在一些实施例中，电子设备100利用温度传感器180J检测的温度，执行温度处理策略。例如，当温度传感器180J上报的温度超过阈值，电子设备100执行降低位于温度传感器180J附近的处理器的性能，以便降低功耗实施热保护。在另一 些实施例中，当温度低于另一阈值时，电子设备100对电池142加热，以避免低温导致电子设备100异常关机。在其他一些实施例中，当温度低于又一阈值时，电子设备100对电池142的输出电压执行升压，以避免低温导致的异常关机。
触摸传感器180K，也称“触控面板”。触摸传感器180K可以设置于显示屏194，由触摸传感器180K与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180K用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180K也可以设置于电子设备100的表面，与显示屏194所处的位置不同。
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。电子设备100可以接收按键输入，产生与电子设备100的用户设置以及功能控制有关的键信号输入。
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。例如，作用于不同应用(例如拍照，音频播放等)的触摸操作，可以对应不同的振动反馈效果。作用于显示屏194不同区域的触摸操作，马达191也可对应不同的振动反馈效果。不同的应用场景(例如：时间提醒，接收信息，闹钟，游戏等)也可以对应不同的振动反馈效果。触摸振动反馈效果还可以支持自定义。
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。
SIM卡接口195用于连接SIM卡。SIM卡可以通过插入SIM卡接口195，或从SIM卡接口195拔出，实现和电子设备100的接触和分离。电子设备100可以支持1个或N个SIM卡接口，N为大于1的正整数。SIM卡接口195可以支持Nano SIM卡，Micro SIM卡，SIM卡等。同一个SIM卡接口195可以同时插入多张卡。所述多张卡的类型可以相同，也可以不同。SIM卡接口195也可以兼容不同类型的SIM卡。SIM卡接口195也可以兼容外部存储卡。电子设备100通过SIM卡和网络交互，实现通话以及数据通信等功能。
其中，该处理器110可以通过调用内部存储器121中存储的计算机指令，以使得该电子设备100执行本申请实施例中的健康管理方法、干预计划收益预测方法、干预计划效果评估方法以及用户健康年龄评估方法。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。
上述实施例中所用，根据上下文，术语“当…时”可以被解释为意思是“如果…”或“在…后”或“响应于确定…”或“响应于检测到…”。类似地，根据上下文，短语“在确定…时”或“如果检测到(所陈述的条件或事件)”可以被解释为意思是“如果确定…”或“响应于确定…”或“在检测到(所陈述的条件或事件)时”或“响应于检测到(所陈述的条件或事件)”。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算 机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如DVD)、或者半导体介质(例如固态硬盘)等。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，该流程可以由计算机程序来指令相关的硬件完成，该程序可存储于计算机可读取存储介质中，该程序在执行时，可包括如上述各方法实施例的流程。而前述的存储介质包括：ROM或随机存储记忆体RAM、磁碟或者光盘等各种可存储程序代码的介质。

Claims (34)

  1. 一种健康管理方法，其特征在于，所述方法包括：
    电子设备获取根据第一用户的用户数据生成的第一干预计划；
    所述电子设备预测所述第一干预计划的部分和/或全部完成后健康指标的预测值。
  2. 根据权利要求1所述的方法，其特征在于，所述电子设备获取根据第一用户的用户数据生成的第一干预计划，具体包括：
    所述电子设备获取所述第一用户的用户数据；
    所述电子设备根据所述用户数据，识别用户健康危险因素；
    所述电子设备针对所述用户健康危险因素，生成所述第一干预计划。
  3. 根据权利要求1或2所述的方法，其特征在于，所述用户数据包括用户基本信息、用户行为数据和/或用户健康数据。
  4. 根据权利要求3所述的方法，其特征在于，所述用户基本信息包括年龄和/或性别；
    所述用户行为数据包括运动数据、压力数据、睡眠数据、饮食数据、饮酒数据和吸烟数据中的至少一种；
    所述用户健康数据包括体重数据、体成分数据、血压数据、血糖数据、血脂数据中的至少一种。
  5. 根据权利要求2所述的方法，其特征在于，所述用户数据包括用户基本信息，还包括用户行为数据和/或用户健康数据，所述用户基本信息包括年龄和/或性别，所述电子设备根据所述用户数据，识别用户健康危险因素，具体包括：
    所述电子设备从多个群体的危险因素对应关系中，获取与所述用户基本信息对应的第一群体的危险因素对应关系，其中不同群体对应的年龄范围和/或性别不同；所述第一群体的危险因素对应关系包括一个或多个健康危险因素与其相应的预设条件之间的对应关系，其中包括第一健康危险因素与第一预设条件之间的对应关系；
    所述电子设备根据所述用户行为数据和/或所述用户健康数据，结合所述第一群体的危险因素对应关系，确定所述用户健康危险因素，其中，在所述用户行为数据和/或所述用户健康数据符合所述第一预设条件的情况下，所述用户健康危险因素中包括所述第一健康危险因素。
  6. 根据权利要求1至5中任一项所述的方法，其特征在于，所述电子设备预测所述第一干预计划的部分和/或全部完成后健康指标的预测值，具体包括：
    所述电子设备根据所述第一用户的个体训练数据进行模型训练，得到第一个体健康收益预测模型；所述第一用户的个体训练数据包括：所述第一用户历史干预计划的执行情况、所述第一用户历史干预计划执行前健康指标的值以及所述第一用户历史干预计划的部分和/或全部执行后健康指标的值；
    所述电子设备将所述用户数据中的用户健康数据和所述第一干预计划的部分和/或全部输入所述第一个体健康收益预测模型，以预测得到所述第一干预计划的部分和/或全部完成后健康指标的预测值。
  7. 根据权利要求1至6中任一项所述的方法，其特征在于，所述健康指标包括体重、体重指数、体脂率、收缩压、舒张压、空腹血糖、总胆固醇、甘油三脂中的至少一种。
  8. 根据权利要求1至7中任一项所述的方法，其特征在于，所述方法还包括：
    所述电子设备显示所述第一干预计划的部分和/或全部完成后健康指标的预测值。
  9. 根据权利要求8所述的方法，其特征在于，所述第一干预计划中包括N个周期的干预计划，所述N为大于1的正整数；所述第一干预计划的部分和/或全部完成后健康指标的预测值包括所述第一干预计划中N个周期的干预计划中的部分和/或全部周期分别完成后的健康指标的预测值；所述电子设备显示所述第一干预计划的部分和/或全部完成后健康指标的预测值，具体包括：
    所述电子设备显示所述第一干预计划的部分和/或全部完成后健康指标的变化趋势，所述健康指标的变化趋势由预测得到的所述第一干预计划中N个周期的干预计划中的部分和/或全部周期分别完成后的健康指标的预测值组成。
  10. 根据权利要求1至9中任一项所述的方法，其特征在于，所述电子设备所在的健康管理系统中还包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材；
    所述方法还包括：
    所述电子设备将所述第一干预计划中的穿戴干预子计划下发给所述智能穿戴设备；所述穿戴干预子计划为所述第一干预计划中要通过所述智能穿戴设备执行的计划；
    所述电子设备将所述第一干预计划中的检测干预子计划下发给所述健康检测设备；所述检测干预子计划为所述第一干预计划中要通过所述健康检测设备执行的计划；
    所述电子设备将所述第一干预计划中的健身干预子计划下发给所述智能健身器材；所述健身干预子计划为所述第一干预计划中要通过所述智能健身器材执行的计划。
  11. 根据权利要求10所述的方法，其特征在于，所述第一干预计划包括第一运动计划、和/或第一饮食计划、和/或第一健康习惯打卡类任务集合；
    所述穿戴干预子计划中包括所述第一运动计划中的部分或全部、和/或第一饮食计划中的部分或全部、和/或第一健康习惯打卡类任务集合中的部分或全部；
    所述检测干预子计划中包括所述第一健康习惯打卡类任务集合中的健康指标检测任务中的部分或全部；
    所述健身干预子计划中包括所述第一运动计划中的部分或全部。
  12. 根据权利要求10或11所述的方法，其特征在于，所述第一干预计划中包括N个周期的干预计划，所述N为大于1的正整数；
    所述穿戴干预子计划为所述第一干预计划中一个周期、或全部周期的穿戴干预子计划；
    所述检测干预子计划为所述第一干预计划中一个周期、或全部周期的检测干预子计划；
    所述健身干预子计划为所述第一干预计划中一个周期、或全部周期的健身干预子计划。
  13. 根据权利要求1至12中任一项所述的方法，其特征在于，所述第一干预计划中包括N个周期的干预计划，所述N为大于1的正整数；所述方法还包括：
    所述电子设备获取所述第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值；所述实际执行数据和/或健康指标的值由所述电子设备和/或所述电子设备所在的健康管理系统中的其他设备监测得到。
  14. 根据权利要求13所述的方法，其特征在于，所述电子设备所在的健康管理系统中的其他设备包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材。
  15. 根据权利要求13或14所述的方法，其特征在于，所述第一干预计划完成后健康指标的预测值包括所述第一干预计划中第一周期完成后健康指标的预测值；所述方法还包括：
    所述电子设备对比所述第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，得到干预效果评估结果。
  16. 根据权利要求15所述的方法，其特征在于，所述方法还包括：
    所述电子设备基于所述干预效果评估结果，结合所述第一周期执行过程中的实际执行数据和/或健康指标的值，生成对所述第一干预计划中一项或多项计划的评价，作为对所述第一干预计划的评价结果。
  17. 根据权利要求16所述的方法，其特征在于，所述方法还包括：
    所述电子设备根据所述对所述第一干预计划的评价结果，调整第一干预计划中第二周期的干预计划，所述第二周期为所述第一周期的下一个周期。
  18. 根据权利要求17所述的方法，其特征在于，在调整第一干预计划中第二周期的干预计划后，所述方法还包括：
    所述电子设备预测调整后的第一干预计划的部分和/或全部完成后健康指标的预测值。
  19. 根据权利要求1至18中任一项所述的方法，其特征在于，所述方法还包括：
    所述电子设备根据所述用户数据，评估所述第一用户的年龄，作为所述第一用户的用户评估年龄；所述用户数据包括用户行为数据和/或用户健康数据；
    所述电子设备预测所述第一干预计划的部分和/或全部完成后所述第一用户的预测用户评估年龄。
  20. 根据权利要求19所述的方法，其特征在于，所述方法还包括：
    所述电子设备显示所述用户评估年龄和所述预测用户评估年龄。
  21. 一种干预计划评价方法，其特征在于，所述方法包括：
    电子设备获取第一干预计划的部分和/或全部完成后健康指标的预测值；
    所述电子设备获取所述第一干预计划执行过程中的实际执行数据和/或健康指标的值；
    所述电子设备对比所述第一干预计划的部分和/或全部完成后健康指标的预测值与实际 完成所述第一干预计划的部分和/或全部后的健康指标的值的吻合度，得到干预效果评估结果。
  22. 根据权利要求21所述的方法，其特征在于，所述第一干预计划中包括N个周期的干预计划，所述N为大于1的正整数；第一干预计划的部分和/或全部完成后健康指标的预测值中包括所述第一干预计划中第一周期完成后健康指标的预测值；
    所述电子设备获取所述第一干预计划执行过程中的实际执行数据和/或健康指标的值，具体包括：
    所述电子设备获取所述第一干预计划中第一周期执行过程中的实际执行数据和/或健康指标的值；所述实际执行数据和/或健康指标的值由所述电子设备和/或所述电子设备所在的健康管理系统中的其他设备监测得到；
    所述电子设备对比所述第一干预计划的部分和/或全部完成后健康指标的预测值与实际完成所述第一干预计划的部分和/或全部后的健康指标的值的吻合度，得到干预效果评估结果，具体包括：
    所述电子设备对比所述第一干预计划中第一周期完成后健康指标的预测值与实际完成该第一周期的干预计划后的健康指标的值的吻合度，得到所述干预效果评估结果。
  23. 根据权利要求22所述的方法，其特征在于，所述电子设备所在的健康管理系统中的其他设备包括：智能穿戴设备、和/或健康检测设备、和/或智能健身器材。
  24. 根据权利要求22或23所述的方法，其特征在于，所述方法还包括：
    所述电子设备基于所述干预效果评估结果，结合所述第一周期执行过程中的实际执行数据和/或健康指标的值，生成对所述第一干预计划中一项或多项计划的评价，作为对所述第一干预计划的评价结果。
  25. 根据权利要求24所述的方法，其特征在于，所述方法还包括：
    所述电子设备根据所述对所述第一干预计划的评价结果，调整第一干预计划中第二周期的干预计划，所述第二周期为所述第一周期的下一个周期。
  26. 根据权利要求21至25中任一项所述的方法，其特征在于，所述方法还包括：
    所述电子设备获取根据第一用户的用户数据生成的所述第一干预计划；
    所述电子设备获取第一干预计划的部分和/或全部完成后健康指标的预测值，具体包括：
    所述电子设备预测所述第一干预计划的部分和/或全部完成后健康指标的预测值。
  27. 一种评估用户年龄的方法，其特征在于，包括：
    电子设备获取第一用户的用户数据；所述用户数据包括用户行为数据和/或用户健康数据；
    所述电子设备根据所述第一用户的用户数据，评估所述第一用户的年龄，作为所述第一用户的用户评估年龄。
  28. 根据权利要求27所述的方法，其特征在于，所述电子设备根据所述第一用户的用户数据，评估所述第一用户的年龄，作为所述第一用户的用户评估年龄，具体包括：
    所述电子设备根据所述用户数据，识别用户健康危险因素；
    所述电子设备根据所述用户数据和所述用户健康危险因素，评估所述第一用户的年龄，作为所述第一用户的用户评估年龄。
  29. 根据权利要求28所述的方法，其特征在于，所述方法还包括：
    所述电子设备针对所述用户健康危险因素，生成第一干预计划；
    所述电子设备预测所述第一干预计划的部分和/或全部完成后所述第一用户的预测用户评估年龄。
  30. 根据权利要求28或29所述的方法，其特征在于，所述电子设备根据所述用户数据和所述用户健康危险因素，评估所述第一用户的年龄，作为所述第一用户的用户评估年龄，具体包括：
    确定与所述用户数据中用户基本信息对应的第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率；其中，无风险暴露情况是指假设群体中的每个人都没有健康风险因素的情况；
    确定第一群体的群体预期寿命；
    根据所述用户健康危险因素和所述第一群体在无风险暴露情况下每一种死因疾病的基线死亡概率，确定用户预期寿命；
    根据所述用户预期寿命、所述第一群体的群体预期寿命以及所述第一用户的实际年龄，确定所述第一用户的用户评估年龄。
  31. 一种电子设备，其特征在于，所述电子设备包括：一个或多个处理器和存储器；
    所述存储器与所述一个或多个处理器耦合，所述存储器用于存储计算机程序代码，所述计算机程序代码包括计算机指令，所述一个或多个处理器调用所述计算机指令以使得所述电子设备执行权利要求1-30中任一项所述的方法。
  32. 一种芯片系统，所述芯片系统应用于电子设备，所述芯片系统包括一个或多个处理器，所述处理器用于调用计算机指令以使得所述电子设备执行如权利要求1-30中任一项所述的方法。
  33. 一种计算机可读存储介质，包括指令，其特征在于，当所述指令在电子设备上运行时，使得所述电子设备执行如权利要求1-30中任一项所述的方法。
  34. 一种用户健康管理系统，其特征在于，所述健康管理系统包括智能穿戴设备、健康检测设备或智能健身器材中的至少一个和所述电子设备；所述电子设备，用于执行如权利要求1-30中任一项所述的方法。
PCT/CN2022/113387 2021-08-27 2022-08-18 健康管理方法、系统和电子设备 WO2023025037A1 (zh)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102077228A (zh) * 2008-06-27 2011-05-25 微软公司 基于用户行为的广告定标
US20180211727A1 (en) * 2017-01-24 2018-07-26 Basehealth, Inc. Automated Evidence Based Identification of Medical Conditions and Evaluation of Health and Financial Benefits Of Health Management Intervention Programs
CN109727676A (zh) * 2018-12-29 2019-05-07 中国科学院合肥物质科学研究院 一种青少年骨量峰值促进系统及方法
CN109785964A (zh) * 2018-12-13 2019-05-21 平安科技（深圳）有限公司 一种健康评估方法、健康评估装置及计算机可读存储介质
JP6901169B1 (ja) * 2020-02-25 2021-07-14 日新ビジネス開発株式会社 年齢学習装置、年齢推定装置、年齢学習方法及び年齢学習プログラム

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102077228A (zh) * 2008-06-27 2011-05-25 微软公司 基于用户行为的广告定标
US20180211727A1 (en) * 2017-01-24 2018-07-26 Basehealth, Inc. Automated Evidence Based Identification of Medical Conditions and Evaluation of Health and Financial Benefits Of Health Management Intervention Programs
CN109785964A (zh) * 2018-12-13 2019-05-21 平安科技（深圳）有限公司 一种健康评估方法、健康评估装置及计算机可读存储介质
CN109727676A (zh) * 2018-12-29 2019-05-07 中国科学院合肥物质科学研究院 一种青少年骨量峰值促进系统及方法
JP6901169B1 (ja) * 2020-02-25 2021-07-14 日新ビジネス開発株式会社 年齢学習装置、年齢推定装置、年齢学習方法及び年齢学習プログラム

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