Health management method and system based on big data

Abstract

The invention provides a health management method and system based on big data, and belongs to the technical field of big data and health management, the method is realized based on cluster equipment, and the method comprises the following steps: step S100: determining a first cluster to which a first user currently belongs; s200, judging whether a cluster updating time point is reached or not, and if yes, updating the first cluster; s300, obtaining updated data of the updated first cluster; and S400, based on the update data, obtaining an updated health management strategy. The health management strategy comprises one or any combination of a diet management strategy, an exercise activity management strategy, a physical sign data measurement management strategy and a health target management strategy. The system comprises a cluster device, wherein the cluster device comprises M clusters formed by N different user terminals; according to the technical scheme, a health management strategy based on big data is more active and flexible, the user experience is improved, and meanwhile the user stickiness and the participation degree are enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of big data and health management technology, and in particular relates to a health management method and system based on big data, a computer-readable storage medium for implementing the method, a computer program product, and an electronic device. Background Art

[0002] Health big data refers to health-related data generated in the process of disease prevention and health management. With the consent of users and in compliance with relevant laws and regulations (for example, data has been anonymized to a certain extent, and patients share data with medical institutions due to treatment needs), health big data can be open for sharing and utilization to give full play to its value and provide more common reference opinions for each individual.

[0003] In practice, users can collect their own health data through smart devices such as smart watches and health monitoring belts, and upload them to the system through mobile applications or online platforms. The system will provide personalized fitness plans and dietary recommendations based on the user's data analysis results. For example, for users who want to lose weight, the system may recommend a low-calorie, high-protein diet and aerobic exercise plan; for users who need to gain muscle, a high-protein diet and strength training plan may be recommended. For example, a health management method and system based on big data intelligent algorithms proposed in the Chinese invention patent with publication number CN109285606A fully utilizes the advantages of mobile, intelligent, and convenient Internet technology, allowing individuals to quickly learn about the improvement of disease risks, and can further promote individuals to implement nutritional health intervention plans.

[0004] However, individual health management strategies or goals are not static. The above-mentioned existing technologies only provide current health intervention plans from a single dimension or based on a single model, and their plans lack initiative and flexibility. In addition, the health management methods proposed by the relevant existing technologies also lack dynamic and autonomous adjustment mechanisms, and the user experience is poor. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a health management method and system based on big data, a computer-readable storage medium, a computer program product and an electronic device for implementing the method.

[0006] In a first aspect of the present invention, a health management method based on big data is proposed. The method is implemented based on cluster devices and comprises the following steps: S100: Determine a first cluster to which a first user currently belongs; S200: Determine whether a cluster update time point has been reached, and if so, update the first cluster; S300: Acquire updated data of the updated first cluster; S400: Acquire an updated health management strategy based on the updated data; The cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; N and M are both integers greater than 2; The health management strategy includes one of a diet management strategy, an exercise activity management strategy, a physical sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

[0007] Before step S100, the method further includes: Each user joins a corresponding cluster based on the currently set health management policy, and each cluster is updated according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update.

[0008] After step S400, the method further includes: S500: Acquire updated user terminals of the first cluster; S600: Pushing the updated health management policy to the updated user terminals of the first cluster.

[0009] The cluster update time point in step S200 includes a cluster user data update time point and a cluster health management strategy update time point; The step S200 specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

[0010] The step S400 updates the health management strategy based on the updated data, including: When the update data includes a cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and health-related data shared by each user.

[0011] In a second aspect of the present invention, in order to implement the method described in the first aspect, a health management system based on big data is proposed, wherein the system includes a cluster device, wherein the cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; and both N and M are integers greater than 2; The system further comprises: A user terminal registration unit, each user terminal registers with the corresponding cluster based on the currently set health management policy; A cluster updating unit, which updates each cluster according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update; A user belonging cluster determination unit, used to determine the cluster to which each user currently belongs; The health management strategy push unit is used to push the updated health management strategy of the cluster to which each user belongs to, wherein the health management strategy includes one of a diet management strategy, an exercise activity management strategy, a vital sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

[0012] The cluster updating unit updates each cluster according to a preset period, specifically including: For each cluster, determine whether the cluster update time point has been reached. If so, update the cluster.

[0013] The cluster update time point includes the cluster user data update time point and the cluster health management strategy update time point; For each cluster, determining whether the cluster update time point has been reached, and if so, updating the cluster, specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

[0014] When the update data includes cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and the health-related data shared by each user.

[0015] The aforementioned big data-based health management method can be automatically implemented through various forms of electronic devices through computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0016] Therefore, in the third aspect of the present invention, a computer-readable storage medium is also provided for storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes a health management method based on big data according to the first aspect.

[0017] In the fourth aspect of the present invention, a computer device is also proposed, which includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the computer device executes a health management method based on big data of the first aspect mentioned above.

[0018] In the fifth aspect of the present invention, a computer program product is also proposed, which includes a computer program. When the computer program is executed, a health management method based on big data in the first aspect mentioned above is implemented.

[0019] The technical solution of the present invention makes the health management strategy based on big data more proactive and flexible, improves the user experience, and enhances user stickiness and participation. The system can collect updated data of multiple users in a group in real time, adjust or update the belonging group or health management strategy according to the actual performance of each user, and ensure that users can achieve their health goals in the most suitable way for themselves. This dynamic adjustment mechanism makes the personalized fitness and health management system more flexible and efficient.

[0020] Further advantages of the present invention will be further reflected in detail in the specific embodiments section in conjunction with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a main flow diagram of a health management method based on big data according to an embodiment of the present invention; Figure 2 It is a main flow diagram of a health management method based on big data according to another preferred embodiment of the present invention; Figure 3It is a schematic diagram of a user joining (registering to) a health management cluster for the first time; Figure 4 It is a schematic diagram of the adjustment of health management strategies and dynamic updates of health management clusters; Figure 5 It is a functional structure diagram of a health management system based on big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] First of all, it should be pointed out that Figure 1 or Figure 2 The embodiment of the big data-based health management method shown can be implemented through a computer program on an electronic device or system configured with a memory and a processor. The electronic device or system can be in the form of a physical machine, a virtual machine, a server, a cluster, or any combination thereof.

[0024] Preferably, the specific form of the electronic device can also be a human-computer interaction terminal, which can be a desktop terminal, an intelligent handheld terminal, a mobile terminal, etc. with a human-computer interaction interface, such as a desktop PC, a portable PDA, a smart phone, a car terminal, etc.

[0025] The subsequent embodiments of the present invention are described by taking a human-computer interaction terminal as an example. The human-computer interaction terminal is collectively referred to as a user terminal in the subsequent embodiments, such as a smart phone used by a user. Figure 1 , Figure 1 It is a main flow chart of a health management method based on big data according to an embodiment of the present invention.

[0026] Figure 1 A health management method based on big data is shown, and the method is implemented based on cluster devices.

[0027] exist Figure 1 In the implementation, the cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; and N and M are both integers greater than 2.

[0028] Specifically, Figure 1 The method includes steps S100-S400, and each step is specifically implemented as follows: S100: Determine a first cluster to which a first user currently belongs; S200: Determine whether a cluster update time point has been reached, and if so, update the first cluster; S300: Acquire updated data of the updated first cluster; S400: Acquire an updated health management strategy based on the updated data; The health management strategy includes one of a diet management strategy, an exercise activity management strategy, a physical sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

[0029] Diet management strategies can be daily menu combinations, meal schedules, etc.; Exercise activity management strategies can be the type of exercise per day, exercise schedule, etc.; The vital sign data measurement management strategy can be the measurement method of each type of vital sign indicator (blood pressure, pulse, stretch force, etc.), and the arrangement schedule of measurement time points (morning, evening / before meals, after meals, etc.); Health goal management strategies can be strategies for setting health management goals that are expected to be achieved (such as blood pressure control, weight control, etc.).

[0030] In a specific implementation, before step S100, the method further includes: S000: Each user joins a corresponding cluster based on the currently set health management policy, and each cluster is updated according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update.

[0031] The cluster update time point in step S200 includes a cluster user data update time point and a cluster health management strategy update time point; The step S200 specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

[0032] The step S400 updates the health management strategy based on the updated data, including: When the update data includes a cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and health-related data shared by each user.

[0033] exist Figure 1 Based on the examples, see Figure 2 , Figure 2 It is a main flow chart of a health management method based on big data according to another preferred embodiment of the present invention.

[0034] Figure 2Preferred embodiments include Figure 1 The entire contents of the embodiment, and on this basis only further include ( Figure 2 The embodiment only marks the step numbers of the further steps including: After step S400, the method further includes: S500: Acquire updated user terminals of the first cluster; S600: Pushing the updated health management policy to the updated user terminals of the first cluster.

[0035] In order to better describe the preferred implementation of each step of the above embodiment, Figure 3 and Figure 4 A further explanation is given below.

[0036] S000: Each user joins a corresponding cluster based on the currently set health management policy, and each cluster is updated according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update.

[0037] See also Figure 3 , Figure 3 A diagram showing a user joining (registering to) a health management cluster for the first time.

[0038] exist Figure 3 In the APP, after user A registers for a health management APP (he agrees to share data in accordance with regulations when registering), he can choose one of the multiple clusters displayed on the APP platform according to his own situation, for example, join the corresponding cluster according to the currently set health management strategy; of course, the APP platform can recommend a target cluster to user A based on user A’s basic registration data (such as height, weight, preferences, etc.).

[0039] exist Figure 3 In the example, assuming that user A currently expects to manage his diet and hopes to obtain relevant diet management strategies, such as breakfast recipes, post-dinner food supplement and exercise plans, etc., he can choose to join cluster A (or the APP platform recommends cluster A to user A).

[0040] exist Figure 3 At the time node of , cluster A is currently named "diet management group", which means that the health management strategy currently provided by cluster A includes the diet management strategy; cluster A currently includes user 1, user 3 and user 5, which means that at the current time node, user 1, user 3 and user 5 belong to cluster A (diet management group); At this time, after user A joins cluster A, the cluster user data of cluster A (diet management group) includes at least: 1) The current number of users belonging to the cluster (user 1, user 3, user 5, and user A); 2) Health-related data shared by each user, including: Basic personal information and dietary information of User 1, User 3, User 5 and User A, such as gender, height, blood type, blood pressure, weight provided by User 3, and relevant dietary recipe data that User 3 believes is helpful in controlling weight and blood pressure and has been verified.

[0041] However, unlike the prior art, the relevant groups (clusters) in this embodiment are dynamically updated, that is, the cluster user data included in each cluster will be dynamically adjusted and updated according to a preset period, including updates to the current number of users belonging to the cluster and information updates on health-related data shared by each user, thereby more flexibly meeting user needs.

[0042] As an example, Figure 3 The current first time node, cluster A is a diet management group (the health management strategy is a diet management strategy), which includes 4 users (user 1, user 3, user 5 and user A). The data shared in the group include gender, height, blood type, blood pressure, weight and related diet recipe data that relevant users believe is helpful in controlling weight and blood pressure and has been verified.

[0043] At a second time node after the first time node, the health management strategy of cluster A may change (for example, from diet management to exercise activity management), and the users it includes may also change (for example, from including user 1, user 3, user 5 and user A to only including user 1 and user 2, etc.), that is, each cluster is updated according to a preset period, and the update includes cluster user data update and / or cluster health management policy update.

[0044] In specific implementation, the data acquisition operation / cluster update operation is automatically and regularly executed by the APP platform according to a preset order and a preset period.

[0045] In an implementation Figure 1 In the scenario of the method, the cluster device includes M clusters consisting of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster at the same time point / time period (at least two different time points constitute a time period); N and M are both integers greater than 2.

[0046] based on Figure 3 , back to Figure 1 Step S100: Determine a first cluster to which a first user currently belongs.

[0047] Figure 1The embodiment is introduced from the perspective of any one of the multiple users included in the multiple clusters, that is, the method can be applied to the multiple users included in the multiple clusters, and the execution process is similar for each user, so that the method can actually be applied to multiple users at the same time.

[0048] For the convenience of description, the first user is used as the Figure 3 User A is taken as an example for introduction.

[0049] Step S100: determining the first cluster to which the first user currently belongs, that is, determining the first cluster to which user A currently belongs; Assume that user A has just registered. At this time, the first cluster to which user A belongs is cluster A. Cluster A is a diet management group (the health management strategy is the diet management strategy), which includes 4 users (user 1, user 3, user 5 and user A). The data shared in the group includes gender, height, blood type, blood pressure, weight, and related diet recipe data that relevant users believe is helpful in controlling weight and blood pressure and has been verified.

[0050] Next, the process proceeds to step S200: determining whether the cluster update time point has been reached, and if so, updating the first cluster.

[0051] The cluster update time point in step S200 includes a cluster user data update time point and a cluster health management policy update time point.

[0052] Assume that the update strategy of the first cluster is: When a new user joins the first cluster, the cluster user data is updated once, and then the health management strategy is updated once every first preset period, and the cluster user data is updated once every second preset period; Preferably, the first preset period of time is shorter than the second preset period of time.

[0053] by Figure 3 For example, the first cluster is cluster A. Before user A joins, the user data of cluster A includes: 1) The current number of users belonging to the cluster (user 1, user 3, user 5); 2) Health-related data shared by each user, including: Basic personal information and dietary information of User 1, User 3, and User 5, such as gender, height, blood type, blood pressure, weight provided by User 3, and relevant dietary recipe data that User 3 believes is helpful in controlling weight and blood pressure and has been verified.

[0054] After user A joins, cluster A updates the cluster user data. At this time, the updated cluster user data of cluster A (diet management group) includes at least: 1) The current number of users belonging to the cluster (user 1, user 3, user 5, and user A); 2) Health-related data shared by each user, including: Basic personal information and dietary information of User 1, User 3, User 5 and User A, such as gender, height, blood type, blood pressure, weight provided by User 3, and relevant dietary recipe data that User 3 believes is helpful in controlling weight and blood pressure and has been verified.

[0055] After 5×24h (5 days), the health management strategy is updated. At this time, the health management strategy of cluster A has changed to "exercise activity management"; after 7×24h (a week), the cluster user data is updated. At this time, see Figure 4 , the users of cluster A are only user A, user 2 and user 3.

[0056] This is because, during this stage, the APP judged based on the relevant data shared by User A, User 2 and User 3 that User A, User 2 and User 3 had completed the originally planned diet management strategy, and the next goal should be exercise activity management. Therefore, User A, User 2 and User 3 were classified into Group A, and the label of the group (Cluster A) was changed from "Diet Management" to "Exercise Activity Management". The original users of Cluster A (User 1, User 5) did not complete the originally planned diet management strategy and have been removed from Cluster A (transferred to other groups).

[0057] At this time, for cluster A, step S300 is performed: obtaining updated data of the updated first cluster.

[0058] Continuing with the above example, obtaining the updated data of the updated first cluster, that is, obtaining the updated data of cluster A after the update, includes: 1) The current number of users belonging to the cluster (user 2, user 3, user A); 2) Health-related data shared by each user, including: Basic personal information of User 2, User 3, and User A, as well as information on physical exercise and fitness activities, such as gender, height, blood type, blood pressure, weight provided by User 2, and relevant physical exercise types, physical exercise time, physical exercise equipment, etc. that User 2 believes are helpful in controlling weight and blood pressure and have been verified.

[0059] Next, the process proceeds to step S400: based on the updated data, obtaining an updated health management strategy.

[0060] Based on the above example, for the first cluster (cluster A), at this time, the label of cluster A has been updated from "diet management" to "exercise activity management", and the user data of cluster A is already "basic personal information of user 2, user 3, and user A, as well as physical exercise and fitness activity information, such as the gender, height, blood type, blood pressure, weight provided by user 2, and the relevant physical exercise types, physical exercise time, physical exercise equipment, etc. that user 2 believes are helpful in controlling weight and blood pressure and have been verified", so for cluster A, the updated health management strategy at this time should be related to exercise activities, that is, to generate an exercise activity management strategy. Specifically, the updated health management strategy can be obtained based on the updated data (the updated data here includes the data before and after the update).

[0061] Since the pre-update data and post-update data of cluster A have been obtained, at this time, based on big data analysis technology, the pre-update data and post-update data of cluster A can be processed accordingly to obtain the specific health management strategy corresponding to cluster A after the update.

[0062] Here, the data update and health management strategy generation related processing can use various existing technologies, including big data intelligent algorithm mining (such as the existing technologies mentioned in the background technology), neural network model calculation and deep learning model, etc. The present invention does not specifically expand on this one by one, and this is not the focus of the present invention. The following only lists some of the documents and introduces their basic principles.

[0063] In one example, the pre-update and post-update data of cluster A also include the collection and management of personal basic information, living habits, past medical history, family medical history, physical examination information, health assessment intervention and other health information; based on personal health examinations, health assessments, and disease diagnosis and treatment information, and based on the establishment of a health map for the entire population using medical big data technology, personal health intervention plans and population stratification and classification intervention plans covering diet, exercise, lifestyle, and spirit are generated. According to user needs, health management plans are customized for users from aspects such as dietary guidelines, exercise guidelines, lifestyle, and mental health. At the same time, specific health tasks are set for users. The system provides tools to set time, content, rules, reminders for execution, and user check-in feedback to develop good health habits.

[0064] In another example, for user A, based on the relevant policies that have been verified to be effective by other users in cluster A, the basic information data of other users in cluster A can be compared for similarity with the basic information data of user A, and the relevant policies that have been verified to be effective by a certain user with the greatest similarity can be used as the updated specific health management policy of user A; or, for all users, the specific health management policy that has been used the most times among the relevant policies that have been verified to be effective can be used as the updated specific health management policy of cluster A, etc. In other words, users in the same cluster can receive exactly the same health management policy, or they can receive separate personalized differentiated health management policies. Since the embodiments of the present application have adopted a dynamic adjustment mechanism when building a cluster to ensure that the health management goals of cluster members are the same, it is preferred that users in the same cluster can receive exactly the same health management policy.

[0065] Related literature includes: [1] Wang Yi. Research and application of visualization algorithm based on medical big data[D]. Tianjin University of Technology, 2018. [2] Chen Zhengfei, Zhang Le, Jin Ying, et al. Research progress on exercise management model for patients with gestational diabetes supported by information technology[J]. Nursing and Rehabilitation, 2021, 20(11): 36-39. [3] Liu Huadong. Comparative analysis of domestic and foreign health data platforms under the big data environment[D]. Heilongjiang University, 2017. at this time, Figure 1 The method may further preferably be Figure 2 In the embodiment, that is, after step S400, the method further includes: S500: Acquire updated user terminals of the first cluster; S600: Pushing the updated health management policy to the updated user terminals of the first cluster.

[0066] by Figure 4 For example, in step S500, the updated user terminals of the first cluster are obtained as user terminal 2, user terminal 3, and user terminal A; Step S600 is to push the updated health management policy to user terminal 2, user terminal 3, and user terminal A.

[0067] It can be seen that the above embodiment uses clusters as units when pushing health management strategies, thus avoiding the transmission impact of large data streams. While taking into account individual flexibility, the health management strategy is more proactive and flexible.

[0068] As a supplement, Figure 4Another scenario is also shown, that is, for user A, his original group A (also including user 1, user 3 and user 5), after a certain period of time, the APP judges based on the relevant data shared by user A, user 1, user 3 and user 5 that user 1, user 3 and user 5 have completed the originally planned diet management strategy and should proceed to the next goal (such as exercise activity management).

[0069] Therefore, user 1, user 3, and user 5 continue to belong to group A, and the label of their group (cluster A) is changed from "diet management" to "exercise activity management". User A needs to continue to implement the diet management strategy, so user A is transferred to another cluster B labeled "diet management group".

[0070] At this time, for user A, obtaining the updated health management policy is to obtain the updated health management policy of cluster B; similarly, cluster B, like cluster A, is updated according to a preset period, and the update includes cluster user data update and / or cluster health management policy update.

[0071] To uniformly describe the above process, a more preferred embodiment also includes: A health management method based on big data, the method is implemented based on a cluster device, the cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; N and M are both integers greater than 2; the method includes the following steps: S1: Determine whether the cluster update time point has been reached, and if so, update the M clusters included in the cluster device; S2: Get the updated data of the updated M clusters; S3: Based on the updated data, obtaining updated health management policies of the M clusters and user terminals corresponding to each of the M clusters; S4: Push the updated health management policies of the M clusters to the user terminals corresponding to each cluster in the M clusters.

[0072] Taking the above-mentioned user A subsequently belonging to cluster B as an example, at this time cluster B includes user A and user B, then the updated health management policy of cluster B is pushed to user A and user B.

[0073] In the above embodiment, for each cluster, since different update strategies (including update sequence, update cycle, etc.) can be set, there are also different cluster user data update time points and cluster health management strategy update time points. In order to avoid data delay caused by different update strategies, the step S1 or the step S200 specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

[0074] Furthermore, the step S400 or the step S3 further comprises: when the update data includes a cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and health-related data shared by each user.

[0075] Based on this, a real-time and flexible dynamic adjustment mechanism is realized. This dynamic adjustment mechanism makes the personalized fitness and health management system more flexible and efficient.

[0076] After introducing the above method embodiments of the present application, Figure 5 A functional structure diagram of a health management system based on big data according to an embodiment of the present invention is given.

[0077] Figure 5 A health management system based on big data includes a cluster device, wherein the cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals ( Figure 5 1 shows user terminal X and user terminal Y, the cluster is open and its members change dynamically); each user terminal belongs to only one cluster in the same time period; N and M are both integers greater than 2; The system further comprises: A user terminal registration unit, each user terminal registers with the corresponding cluster based on the currently set health management policy; A cluster updating unit, which updates each cluster according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update; A user belonging cluster determination unit, used to determine the cluster to which each user currently belongs; The health management strategy push unit is used to push the updated health management strategy of the cluster to which each user belongs to, wherein the health management strategy includes one of a diet management strategy, an exercise activity management strategy, a vital sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

[0078] The cluster updating unit updates each cluster according to a preset period, specifically including: For each cluster, determine whether the cluster update time point has been reached. If so, update the cluster.

[0079] The cluster update time point includes the cluster user data update time point and the cluster health management strategy update time point; For each cluster, determining whether the cluster update time point has been reached, and if so, updating the cluster, specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

[0080] When the update data includes cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and the health-related data shared by each user.

[0081] The aforementioned big data-based health management method can be automatically implemented through various forms of electronic devices through computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0082] Therefore, more embodiments further provide a computer-readable storage medium for storing computer instructions, which, when executed on an electronic device, causes the electronic device to execute Figure 1 or Figure 2 The embodiment provides a health management method based on big data.

[0083] More embodiments further provide a computer device, the computer device comprising a processor and a memory, the memory being used to store instructions, the processor being used to call the instructions in the memory so that the computer device executes Figure 1 or Figure 2 The embodiment provides a health management method based on big data.

[0084] More embodiments further provide a computer program product, the product comprising a computer program, when the computer program is executed, the Figure 1 or Figure 2 A health management method based on big data is implemented in the embodiment.

[0085] At the same time, in the specific implementation of this application, if user-related data is involved, when the embodiment of this application is applied to a specific product or technology, the user's permission or consent must be obtained, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0086] Other technologies, principles, algorithms or models not elaborated in detail in this application can be referred to in the prior art. The prior art cited in the embodiments is an integral part of the disclosure of the present invention.

[0087] The foregoing has shown and described the method embodiments and system of the present invention, but it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A health management method based on big data, the method is implemented based on cluster devices, characterized in that: The method comprises the following steps: S100: Determine a first cluster to which a first user currently belongs; S200: Determine whether a cluster update time point has been reached, and if so, update the first cluster; S300: Acquire updated data of the updated first cluster; S400: Acquire an updated health management strategy based on the updated data; The cluster device includes M clusters composed of N different user terminals; each cluster includes at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; N and M are both integers greater than 2; The health management strategy includes one of a diet management strategy, an exercise activity management strategy, a physical sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

2. A health management method based on big data as claimed in claim 1, characterized in that: Before step S100, the method further includes: Each user joins a corresponding cluster based on the currently set health management policy, and each cluster is updated according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update.

3. A health management method based on big data as claimed in claim 1, characterized in that: After step S400, the method further includes: S500: Acquire updated user terminals of the first cluster; S600: Pushing the updated health management policy to the updated user terminals of the first cluster.

4. A health management method based on big data as claimed in claim 2, characterized in that: The cluster update time point in step S200 includes a cluster user data update time point and a cluster health management strategy update time point; The step S200 specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

5. A health management method based on big data as claimed in claim 2, characterized in that: The step S400 updates the health management strategy based on the updated data, including: When the update data includes a cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and health-related data shared by each user.

6. A health management system based on big data, the system comprising a cluster device, the cluster device comprising M clusters consisting of N different user terminals; each cluster comprises at least 2 user terminals; each user terminal belongs to only one cluster in the same time period; N and M are both integers greater than 2; It is characterized in that The system further comprises: A user terminal registration unit, each user terminal registers with the corresponding cluster based on the currently set health management policy; A cluster updating unit, which updates each cluster according to a preset period, wherein the update includes cluster user data update and / or cluster health management policy update; A user belonging cluster determination unit, used to determine the cluster to which each user currently belongs; The health management strategy push unit is used to push the updated health management strategy of the cluster to which each user belongs to, wherein the health management strategy includes one of a diet management strategy, an exercise activity management strategy, a vital sign data measurement management strategy, and a health goal management strategy, or any combination thereof.

7. A health management system based on big data as claimed in claim 6, characterized in that: The cluster updating unit updates each cluster according to a preset period, specifically including: For each cluster, determine whether the cluster update time point has been reached. If so, update the cluster.

8. A health management system based on big data as claimed in claim 7, characterized in that: The cluster update time point includes the cluster user data update time point and the cluster health management strategy update time point; For each cluster, determining whether the cluster update time point has been reached, and if so, updating the cluster, specifically includes: Determine whether a cluster user data update time point has been reached, and if so, update the cluster user data, wherein the cluster user data includes the current number of users belonging to the cluster and health-related data shared by each user; and / or, Determine whether the cluster health management policy update time point has been reached, and if so, update the cluster health management policy.

9. A health management system based on big data as claimed in claim 8, characterized in that: When the update data includes cluster health management policy update, the current cluster applies the updated cluster health management policy; When the update data does not include cluster health management policy updates, an updated health management policy is generated based on the current number of users belonging to the cluster and the health-related data shared by each user.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the big data-based health management method according to any one of claims 1 to 5 is implemented.

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