Health data processing method and device and electronic equipment
By obtaining health data in real time and using indicator monitoring models for processing, the problem that traditional health management models are difficult to meet comprehensive and continuous health monitoring and personalized management is solved, and the provision of personalized health management services is realized.
Patent Information
- Application Number
- CN202411204398.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2024-12-06
AI Technical Summary
The traditional health management model mainly relies on regular health examinations and screening mechanisms, which is difficult to meet people's needs for comprehensive and continuous health monitoring and personalized health management.
By obtaining users' health data in real time, using pre-trained indicator monitoring models to monitor and process health indicators, and pushing users personalized content based on abnormal indicator data to provide personalized health management services.
Real-time and uninterrupted health data acquisition and continuous analysis are realized, ensuring that any health changes can be captured in a timely manner, and personalized health management services are provided based on abnormal indicator data to meet personalized health management needs.
Smart Images
Figure CN119092038A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to a health data processing method, device and electronic device. Background Art
[0002] In the current medical and health field, the traditional health management model mainly relies on regular health examinations and screening mechanisms, such as annual comprehensive physical examinations and specific disease screening every six months or less. Although these methods can monitor the health status of individuals to a certain extent, they are difficult to meet people's needs for comprehensive and continuous health monitoring and personalized health management. Summary of the invention
[0003] In view of this, the present disclosure provides a health data processing method, device and electronic device, the main purpose of which is to solve the problem that the current traditional health management model mainly relies on regular health examinations and screening mechanisms, which is difficult to meet people's needs for comprehensive, continuous health monitoring and personalized health management.
[0004] According to a first aspect of the present disclosure, a health data processing method is provided, the method comprising:
[0005] Acquire the user's health data in real time, wherein the health data includes indicator data;
[0006] The indicator data is input into a pre-trained indicator monitoring model for health indicator monitoring processing, and personalized content is pushed to the user based on the health data and / or abnormal indicator data in the monitored indicator data.
[0007] According to a second aspect of the present disclosure, a health data processing device is provided, the device comprising:
[0008] An acquisition module, used to acquire the user's health data in real time, wherein the health data includes indicator data;
[0009] The monitoring module is used to input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to users based on the health data and / or abnormal indicator data in the monitored indicator data.
[0010] According to a third aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method of the aforementioned first aspect.
[0011] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the aforementioned first aspect.
[0012] Compared with the prior art, the health data processing method, device and electronic device provided by the present disclosure can first obtain the user's health data in real time, and the health data includes indicator data; input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to the user based on the abnormal indicator data in the health data and / or the monitored indicator data. Through the scheme of the present disclosure, by integrating modern wearable devices, smartphone applications, etc., the present application can obtain the user's health data in real time and uninterruptedly, ensuring the comprehensiveness and timeliness of the data. Using the pre-trained indicator monitoring model, the real-time acquired health data is continuously analyzed and monitored to ensure that any health changes can be captured in time. When abnormal indicator data is monitored, personalized content can be pushed to the user based on the health data and / or the monitored abnormal indicator data, thereby providing personalized health management services.
[0013] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0015] 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 or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0016] Figure 1 A flowchart of a health data processing method provided by an embodiment of the present disclosure;
[0017] Figure 2 A flowchart of another health data processing method provided by another embodiment of the present disclosure;
[0018] Figure 3 A schematic diagram of the structure of a health data processing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, in the absence of conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.
[0020] The following describes the health data processing method, device and electronic device of the embodiments of the present disclosure with reference to the accompanying drawings.
[0021] The present disclosure provides a health data processing method, device and electronic device, the main purpose of which is to solve the problem that the current traditional health management model mainly relies on regular health examinations and screening mechanisms, which is difficult to meet people's needs for comprehensive and continuous health monitoring and personalized health management.
[0022] like Figure 1 As shown, an embodiment of the present disclosure provides a health data processing method, including:
[0023] Step 101: Obtain the user's health data in real time, where the health data includes indicator data.
[0024] Among them, health data can be a collection of all data related to personal health status and medical care activities. Health data can include indicator data, physical examination reports, medical records, prescription medication, etc., without specific limitation.
[0025] For the embodiments of the present disclosure, the executing entity may be a health management system, which may be connected to medical and health big data models to provide customers with human-computer interaction services and intelligent reminder services, so as to perform health management more quickly and in real time.
[0026] Specifically, it can be connected to smart devices and use the smart devices to monitor user indicator data in real time.
[0027] Smart devices can be devices that monitor user health data in real time through technologies such as sensors, network connections and intelligent algorithms; smart devices can include but are not limited to smart wearable devices such as smart watches, smart bracelets, electrocardiogram clothes, chest patches, and smart medical devices such as smart blood pressure monitors and smart blood glucose meters.
[0028] Index data may be physiological indicators, biochemical indicators, sleep quality, exercise records and other data that are monitored and recorded in real time through smart devices or other medical means to reflect the individual's health status, physiological functions and disease risks. Physiological indicators may include but are not limited to heart rate, blood pressure, blood oxygen saturation, electrocardiogram (ECG), respiratory rate, body temperature, etc.; biochemical indicators may include but are not limited to blood sugar, blood lipids, etc.; sleep quality may include but are not limited to sleep cycles, deep sleep time, REM (rapid eye movement) sleep stage, etc.; exercise records may include but are not limited to user steps, exercise distance, exercise intensity, calorie consumption, etc.
[0029] The user's physical examination report, medical records, prescription medication, etc. can be obtained through the medical institution information system, user self-recording, etc. The medical institution information system can be an information system used to record the health data (such as physical examination reports, medical records, prescription medication, etc.) generated when the user receives physical examination, diagnosis and treatment, etc. in a medical institution (such as a hospital, clinic).
[0030] User self-records refer to health data (such as physical examination reports, medical records, prescription medication status, etc.) that users can record independently through mobile phone apps, health management software and other tools.
[0031] Among them, medical records may be user medical data recorded by medical staff during the diagnosis and treatment process, which may include information such as user medical history, diagnosis, treatment process, clinical changes, and prognosis.
[0032] Prescription medication information may be information about prescription medications issued based on the user's condition and diagnosis results, and may include information such as the drug name, specifications, usage and dosage, and precautions.
[0033] Step 102: Input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to the user based on the health data and / or abnormal indicator data in the monitored indicator data.
[0034] Among them, the indicator monitoring model can be a model used to monitor and evaluate whether the indicator data is normal.
[0035] For the embodiments of the present disclosure, as a possible implementation method, the indicator data can be input into a pre-trained indicator monitoring model for health indicator monitoring processing, and the abnormal indicator data in the indicator data can be identified by the built-in abnormality detection algorithm of the indicator monitoring model. The abnormality detection algorithm may include but is not limited to statistical methods, standard deviation methods, density methods, distance methods, clustering methods, machine learning methods, etc.
[0036] Among them, the statistical method can be to set the upper and lower limits of normal indicator data as thresholds, and any data exceeding these thresholds is regarded as abnormal indicator data.
[0037] The standard deviation method can be used to calculate the data fluctuation range by using the standard deviation of historical data. Data that exceeds a certain multiple (such as 2 or 3 times) of the standard deviation is considered abnormal indicator data.
[0038] The density method assumes that the data is in a high-dimensional space and the abnormal data is located in the low-density area. By estimating the density distribution of the data, the data in the low-density area is regarded as abnormal indicator data.
[0039] The distance method can be to calculate the distance between data points and regard the points far away from most data points as abnormal indicator data. For example, it can be implemented using the K-nearest neighbor (KNN) algorithm or the isolation forest algorithm.
[0040] The clustering method can be to divide the data into several classes (or clusters), and then regard the data points that do not belong to any class or are far away from the cluster center as abnormal indicator data.
[0041] Machine learning methods can use unsupervised learning algorithms (such as autoencoders, isolation forests) or supervised learning algorithms (such as classifiers) to automatically identify abnormal indicator data without explicitly labeling abnormal data.
[0042] For the embodiments of the present disclosure, the user's physical health status, risk factors, and potential needs can be inferred based on the health data and / or abnormal indicator data in the monitored indicator data, and personalized health content can be customized for the user accordingly. Personalized health content may include prevention knowledge of specific diseases, healthy lifestyle recommendations, exercise plans suitable for individuals, diet adjustment plans, medication reminders, etc., without specific limitation.
[0043] In summary, according to the health data processing method provided by the present disclosure, compared with the prior art, the present disclosure can first obtain the user's health data in real time, and the health data includes indicator data; input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to the user based on the abnormal indicator data in the health data and / or the monitored indicator data. Through the scheme of the present disclosure, by integrating modern wearable devices, smartphone applications, etc., the present application can obtain the user's health data in real time and uninterruptedly, ensuring the comprehensiveness and timeliness of the data. Using the pre-trained indicator monitoring model, the real-time acquired health data is continuously analyzed and monitored to ensure that any health changes can be captured in time. When abnormal indicator data is monitored, personalized content can be pushed to the user based on the health data and / or the monitored abnormal indicator data, thereby providing personalized health management services.
[0044] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 2 The specific method shown includes:
[0045] Step 201: Acquire the user's health data in real time, where the health data includes indicator data.
[0046] For the implementation process of the embodiment of the present disclosure, please refer to the relevant description in step 101 of the embodiment, which will not be repeated here.
[0047] Step 202: Use the indicator monitoring model to determine whether the indicator data is within the preset normal indicator range; if the indicator data is within the preset normal indicator range, determine that the indicator status of the indicator data is a normal indicator; if the indicator data is not within the preset normal indicator range, determine that the indicator status of the indicator data is an abnormal indicator.
[0048] For the embodiments of the present disclosure, the indicator monitoring model may be trained. As a possible implementation method, a large amount of historical indicator data may be first collected as a training set, wherein the historical indicator data may include historical normal indicator data and historical abnormal indicator data.
[0049] The collected historical indicator data can be cleaned to remove noise, error values, missing values, etc., and the cleaned historical indicator data can be standardized or normalized to ensure that data of different dimensions can be compared in the same model.
[0050] The historical health indicator characteristics and the corresponding historical indicator status are extracted from the processed historical indicator data, the historical health indicator characteristics are used as the input features of the indicator monitoring model, and the historical indicator status is used as the training label of the indicator monitoring model. The indicator monitoring model is iteratively trained until the loss function value of the indicator monitoring model is less than the preset loss function threshold, and the indicator monitoring model training is determined to be completed.
[0051] Among them, the historical indicator status may include historical normal indicators and historical abnormal indicators;
[0052] The indicator monitoring model can be logistic regression, decision tree, random forest, neural network, etc.
[0053] For the embodiments of the present disclosure, as a possible implementation method, the indicator data can be input into a trained indicator monitoring model for prediction. If the indicator data is within the preset normal indicator range, the indicator status of the indicator data is determined to be a normal indicator; if the indicator data is not within the preset normal indicator range, the indicator status of the indicator data is determined to be an abnormal indicator.
[0054] Step 203: Push a health warning notification to the user based on the abnormal indicator data indicated by the abnormal indicator in the monitored indicator data.
[0055] Among them, the health warning notification can be a notification reminder used to remind users to pay attention to their own health status.
[0056] For the disclosed embodiment, when the health management system monitors abnormal indicator data in the user's indicator data, such as high blood pressure, abnormal heart rate, low blood sugar, etc., these abnormal indicator data will trigger the system to generate a health warning notification. The health warning notification may include detailed information on the abnormal indicators, possible health risks, and recommended countermeasures or suggestions for further examinations, etc., without specific limitations, and the health warning notification will be pushed to the user through mobile phone text messages, emails, in-application notifications, etc., to ensure that the notification can be conveyed to the user in a timely and accurate manner so that the user can take corresponding measures in a timely manner.
[0057] Step 204: Pushing dietary advice reminders to the user based on the physical examination report, medical records and / or abnormal indicator data indicated by the abnormal indicators, wherein the health data includes the physical examination report and / or medical records.
[0058] For the embodiments of the present disclosure, by collecting the user's physical examination report, medical records and any data showing abnormal health indicators, a comprehensive understanding of the user's health status is obtained, including whether there are chronic diseases (such as diabetes, hypertension, hyperlipidemia, etc.), allergy history, special dietary needs (such as vegetarian, gluten-free, etc.) and current physical indicators (such as weight, BMI, body fat percentage, etc.).
[0059] As a possible implementation method, the collected user health status information can be input into a nutritional formula model. The nutritional formula model is based on a large amount of nutritional research and data, and conducts scientific nutritional analysis by comprehensively considering the customer's various physical indicators and needs.
[0060] The nutritional formula model can calculate the appropriate daily intake of various nutrients (such as carbohydrates, proteins, fats, vitamins, minerals, etc.) required by users based on the input data, and generate personalized nutritional food recommendation plans. The nutritional food recommendation plans are used to ensure that customers can get a balanced diet, that is, the proportion and intake of various nutrients are in line with the body's needs. It may include recommended staple foods, protein sources (such as meat, eggs, beans, etc.), vegetables and fruits, and appropriate amounts of fat and sugar.
[0061] The recommended nutritional food recommendation plan can be diversified, that is, different kinds of staple foods, meats, vegetables and fruits are recommended to ensure that users can consume rich nutrients.
[0062] For customers with special dietary needs (such as diabetics who need to control their sugar intake, hypertensive patients who need to reduce their salt intake, etc.), the recommended plan should take these needs into special consideration and provide corresponding alternative foods or suggestions.
[0063] In addition to recommending nutritious foods, scientific cooking method suggestions can also be provided to maximize the retention of nutrients in food and avoid adding excessive oil, salt and sugar during the cooking process.
[0064] In a specific application scenario, taking users with high blood lipids and blood sugar as an example, their daily healthy and nutritious food recommendations may include:
[0065] Buckwheat noodles, oatmeal, whole grain steamed bread or fruit corn can be recommended as the staple food for breakfast, with low-fat milk or soy milk, boiled eggs, and fresh vegetables (such as tomatoes, cucumbers, etc.) and fruits (such as apples, oranges, etc.). Lean meat (such as chicken breast, fish, etc.) with green leafy vegetables (such as spinach, celery, etc.) and a small amount of coarse grain staple food (such as brown rice or whole wheat bread) can be recommended for lunch. Dinner can be similar to lunch, but the intake of staple food can be appropriately reduced and the proportion of vegetables can be increased. At the same time, some foods rich in dietary fiber (such as sweet potatoes, pumpkins, etc.) can be recommended as substitutes for staple foods.
[0066] Dietary advice reminders can be pushed to users via mobile phone text messages, emails, in-app notifications, etc., to ensure that users receive the information in a timely manner.
[0067] Step 205: Pushing exercise suggestion reminders to the user based on the physical examination report, medical records and / or abnormal indicator data indicated by the abnormal indicator.
[0068] For the disclosed embodiment, the user's physical examination report, medical records, and any data showing abnormal health indicators, such as heart rate, blood pressure, blood sugar, body fat percentage, bone density, etc., may be collected first.
[0069] As a possible method, the collected physical examination reports, medical records and / or abnormal indicator data can be input into the sports big model for in-depth analysis to identify the user's major health problems, such as cardiovascular disease risk, diabetes risk, obesity, osteoporosis, etc., and evaluate the user's exercise ability and exercise habits.
[0070] Then, personalized exercise recommendations can be made for users based on their age, gender, physical condition, existing health problems, exercise ability, exercise habits, etc. The exercise recommendations may include exercise type, exercise intensity and time, as well as precautions.
[0071] Among them, exercise types may include aerobic exercise (such as brisk walking, jogging, swimming, cycling, etc.), strength training, flexibility training, etc., without specific limitations.
[0072] Users with specific health problems, such as cardiovascular disease, should avoid high-intensity exercise and choose low-intensity, long-term aerobic exercise.
[0073] You can set the appropriate exercise intensity and time according to the user's physical condition and exercise goals.
[0074] Precautions can be used to remind users to warm up before exercise to avoid sports injuries; to replenish water and electrolytes during exercise; to stretch and relax after exercise to promote muscle recovery, etc.
[0075] In specific application scenarios, taking elderly users as an example, the user's physical condition is high blood sugar and difficulty in movement. They can plan to do moderate-intensity upper limb health exercises half an hour after meals every day, and use corresponding operation videos as guidance. Users can follow the video to perform standardized and systematic exercises, which ensures the effect of exercise while avoiding possible injuries caused by improper movements.
[0076] For the embodiments of the present disclosure, a timed reminder function can also be set according to the user's exercise habits and schedule, such as reminding the user to exercise every morning or evening, and pushing exercise suggestion reminders to the user via mobile phone text messages, emails, in-application notifications, etc., to ensure that the user can receive the information in a timely manner.
[0077] The system can encourage users to record their exercise status and provide feedback to the system so that the system can adjust exercise recommendations based on the user's actual exercise results. At the same time, the system can also provide functions such as exercise check-in and community communication to increase user participation and enthusiasm.
[0078] The system can also regularly evaluate the user's exercise effects and improvements in health indicators, and adjust exercise recommendations based on the evaluation results to ensure that users can continue to receive effective exercise guidance.
[0079] Step 206: Push medication reminders to the user based on the prescription medication status, where the health data includes the prescription medication status.
[0080] For the embodiments of the present disclosure, the system can automatically generate medication reminders based on prescription medication conditions, such as drug name, dosage, medication time, etc., and remind users to take medications at the specified time through push notifications, text messages, or phone calls, thereby preventing users from forgetting to take medications due to various circumstances.
[0081] As a possible method, users can set specific medication time reminders based on their lifestyle and medication needs. For example, reminders can be set after breakfast, before lunch, or before bedtime.
[0082] For medications that need to be taken in multiple doses, users can set a dosage reminder for each dose. The system can remind users to take the correct dosage before each dose.
[0083] In addition to basic medication time reminders, you can also set medication precautions reminders. For example, remind users to avoid foods, drinks or activities before and after taking medication.
[0084] Step 207: receiving interactive question information input by the user; generating corresponding answers and / or medical advice according to the interactive question information, and presenting the answers and / or medical advice to the user in a target form.
[0085] Among them, the interactive question information can be questions or queries related to medicine, health, disease, treatment, prevention, etc. raised by the user to the system through text input, voice input, etc.; the target form can be voice, text, graphics, etc.
[0086] As a possible implementation method for the disclosed embodiment, natural language processing (NLP) technology may be used to parse the interactive question information input by the user to identify key words and context information in the interactive question information.
[0087] Key words and context information are input into the medical health model, and logical reasoning and decision analysis are performed based on the retrieved question-related information and the user's physical health condition (such as age, gender, medical history, etc.) to determine the most appropriate answer and / or medical advice, and the answer and / or medical advice are presented to the user in the form of voice, text, graphics, etc.
[0088] In summary, according to the health data processing method provided by the present disclosure, compared with the prior art, the present disclosure can first obtain the user's health data in real time, and the health data includes indicator data; input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to the user based on the abnormal indicator data in the health data and / or the monitored indicator data. Through the scheme of the present disclosure, by integrating modern wearable devices, smartphone applications, etc., the present application can obtain the user's health data in real time and uninterruptedly, ensuring the comprehensiveness and timeliness of the data. Using the pre-trained indicator monitoring model, the real-time acquired health data is continuously analyzed and monitored to ensure that any health changes can be captured in time. When abnormal indicator data is monitored, personalized content can be pushed to the user based on the health data and / or the monitored abnormal indicator data, thereby providing personalized health management services.
[0089] Based on the above Figure 1 , Figure 2 The specific implementation of the method shown in this embodiment provides a health data processing device, such as Figure 3 As shown, the device includes: an acquisition module 31 and a monitoring module 32;
[0090] An acquisition module 31 is used to acquire the user's health data in real time, wherein the health data includes indicator data;
[0091] The monitoring module 32 is used to input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to the user based on the health data and / or abnormal indicator data in the monitored indicator data.
[0092] In a specific application scenario, the monitoring module 32 can be used to use the indicator monitoring model to determine whether the indicator data is within the preset normal indicator range; if the indicator data is within the preset normal indicator range, the indicator status of the indicator data is determined to be a normal indicator; if the indicator data is not within the preset normal indicator range, the indicator status of the indicator data is determined to be an abnormal indicator.
[0093] In a specific application scenario, the monitoring module 32 can be used to push a health warning notification to the user based on the abnormal indicator data indicated by the abnormal indicator in the monitored indicator data.
[0094] In a specific application scenario, the health data includes a physical examination report and / or medical records, and the monitoring module 32 can be used to push dietary advice reminders to the user based on the abnormal indicator data indicated by the physical examination report, the medical records and / or the abnormal indicators.
[0095] In a specific application scenario, the monitoring module 32 may be used to push exercise suggestion reminders to the user based on the physical examination report, the medical record and / or the abnormal indicator data indicated by the abnormal indicator.
[0096] In specific application scenarios, such as Figure 3 As shown, the device also includes: a receiving module 33, a generating module 34;
[0097] A receiving module 33, used to receive interactive question information input by a user;
[0098] The generating module 34 is used to generate corresponding answers and / or medical advice according to the interactive question information, and present the answers and / or medical advice to the user in a target form.
[0099] It should be noted that for other corresponding descriptions of the functional units involved in the health data processing device provided in this embodiment, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.
[0100] Based on the above Figure 1 and Figure 2The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The method shown.
[0101] Based on this understanding, the technical solution of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present disclosure.
[0102] Based on the above Figure 1 and Figure 2 The method shown, and Figure 3 In order to achieve the above-mentioned purpose, the embodiment of the present disclosure also provides an electronic device, which includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The method shown.
[0103] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.
[0104] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.
[0105] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.
[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present disclosure can be implemented by means of software plus the necessary general hardware platform, or by hardware. The present disclosure can first obtain the user's health data in real time, and the health data includes indicator data; the indicator data is input into the pre-trained indicator monitoring model for health indicator monitoring processing, and personalized content is pushed to the user based on the abnormal indicator data in the health data and / or the monitored indicator data. Through the scheme of the present disclosure, by integrating modern wearable devices, smart phone applications, etc., the present application can obtain the user's health data in real time and uninterruptedly, ensuring the comprehensiveness and timeliness of the data. Using the pre-trained indicator monitoring model, the real-time acquired health data is continuously analyzed and monitored to ensure that any health changes can be captured in time. When abnormal indicator data is monitored, personalized content can be pushed to the user based on the health data and / or the monitored abnormal indicator data, thereby providing personalized health management services.
[0107] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0108] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features applied herein.
Claims
1. A health data processing method, It is characterized in that The method comprises: Acquire the user's health data in real time, wherein the health data includes indicator data; The indicator data is input into a pre-trained indicator monitoring model for health indicator monitoring processing, and personalized content is pushed to the user based on the health data and / or abnormal indicator data in the monitored indicator data.
2. The method according to claim 1, It is characterized in that The step of inputting the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing includes: Using the indicator monitoring model to determine whether the indicator data is within a preset normal indicator range; If the indicator data is within the preset normal indicator range, determining that the indicator status of the indicator data is a normal indicator; If the indicator data is not within the preset normal indicator range, the indicator status of the indicator data is determined to be an abnormal indicator.
3. The method according to claim 2, It is characterized in that The pushing of personalized content to the user according to the health data and / or abnormal indicator data in the monitored indicator data includes: Push a health warning notification to the user based on the abnormal indicator data indicated by the abnormal indicator in the monitored indicator data.
4. The method according to claim 2, It is characterized in that The health data includes medical examination reports and / or medical records; The pushing of personalized content to the user according to the health data and / or abnormal indicator data in the monitored indicator data includes: Push dietary advice reminders to the user based on the physical examination report, the medical records and / or the abnormal indicator data indicated by the abnormal indicator.
5. The method according to claim 4, It is characterized in that The pushing of personalized content to the user according to the health data and / or abnormal indicator data in the monitored indicator data includes: Pushing exercise suggestion reminders to the user based on the physical examination report, the medical records and / or the abnormal indicator data indicated by the abnormal indicator.
6. The method according to claim 1, It is characterized in that The health data includes prescription medications; The pushing of personalized content to the user according to the health data and / or abnormal indicator data in the monitored indicator data includes: Push medication reminders to the user based on the prescription medication situation.
7. The method according to claim 1, It is characterized in that The method further comprises: Receive interactive question information input by the user; A corresponding answer and / or medical advice is generated according to the interactive question information, and the answer and / or the medical advice is presented to the user in a target form.
8. A health data processing device, It is characterized in that include: An acquisition module, used to acquire the user's health data in real time, wherein the health data includes indicator data; The monitoring module is used to input the indicator data into a pre-trained indicator monitoring model for health indicator monitoring processing, and push personalized content to users based on the health data and / or abnormal indicator data in the monitored indicator data.
9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.
10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.