A community smart elderly care service system

Abstract

The present invention relates to the field of smart elderly care technology, and discloses a community smart elderly care service system, including a user information management module: managing the basic information, physical examination records and sensor data of the elderly; a data acquisition module: collecting the user's physical examination information, vital sign sensor data, and regularly updating the data; a data analysis and processing module: analyzing the user's health data, generating a health report, and performing trend analysis; an intelligent recommendation module: providing personalized lifestyle suggestions based on the user's health status; and a user interaction module: providing a user interaction interface. The community smart elderly care service system proposed in the present invention solves the shortcomings of existing systems in terms of personalized services. Through precise nutritional demand calculation, comprehensive data collection and analysis, social interaction and incentive mechanisms, convenient food delivery and kitchen assistants, etc., the system can provide customized diet plans for the elderly, ensuring that users receive comprehensive and accurate diet management.

Description

Technical Field

[0001] The present invention relates to the field of smart elderly care technology, and in particular to a community smart elderly care service system. Background Art

[0002] The community smart elderly care service system is a comprehensive service platform that combines modern information technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence. It aims to provide more convenient and efficient elderly care services for the elderly living in the community, while reducing the care burden on family members and community workers.

[0003] Although the existing community elderly care service system provides services such as health monitoring, telemedicine consultation, and health management plans, and has made significant contributions to improving the quality of life of the elderly, it still faces many challenges in providing personalized services, especially dietary management. First, these systems often rely on relatively general data collection and analysis methods, which leads to the health management recommendations provided being usually based on averages or widely applicable principles. Although they can be applied to most people, they ignore the differences between individuals, especially in terms of diet. Diet plays a vital role in the physical health of the elderly. Reasonable nutritional intake can not only help maintain vitality in daily life, but also prevent a variety of common diseases in the elderly and improve the quality of life. The general perception is that the elderly should follow the principle of light diet, aiming to reduce the risk of cardiovascular disease and control blood sugar levels. However, such dietary guidance is not necessarily suitable for every elderly person, because everyone's physical condition, health background, and nutritional needs are different;

[0004] For example, some elderly people may need to increase their protein intake to promote muscle health due to specific medical conditions, or need to supplement certain vitamins and minerals to make up for the decreased absorption capacity caused by aging. Others may suffer from chronic kidney disease and need to limit the intake of elements such as potassium and phosphorus. If all elderly people are recommended the same diet plan and the intake of different nutrients is the same, this may not only lead to nutritional imbalance, but also may have adverse effects on the health of some people, such as causing malnutrition or aggravating existing health problems.

[0005] In short, these problems existing in existing technologies not only limit the community elderly care service system from realizing its full potential, but also prevent many elderly people from enjoying the health management and care support that best suits them.

[0006] Therefore, it is necessary to design a community smart elderly care service system to solve the above problems. Summary of the invention

[0007] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a community smart elderly care service system.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A community smart elderly care service system includes a user information management module: managing basic information, physical examination records and sensor data of the elderly;

[0010] Data collection module: collects users’ physical examination information, vital sign sensor data, and updates the data regularly;

[0011] Data analysis and processing module: analyzes the user's health data, generates health reports, conducts trend analysis, and compares and analyzes updated data with old data;

[0012] Intelligent recommendation module: provides personalized lifestyle suggestions based on the user's health status;

[0013] User interaction module: provides a user interaction interface for elderly people to operate and interact.

[0014] As a preferred technical solution of the present invention, the data acquisition module includes:

[0015] Vital sign sensor data acquisition unit: supports smart wearable devices, including but not limited to smart watches and health bracelets, real-time monitoring of heart rate, blood pressure, and step count data, and automatically synchronizes them to the system;

[0016] Physical examination report data collection unit: obtains the physical examination report data of the elderly by taking photos and directly connecting to the information database of the physical examination center.

[0017] As a preferred technical solution of the present invention, the data analysis and processing module includes:

[0018] Deep learning model: Use deep learning algorithms to analyze health data and predict potential health risks;

[0019] Trend analysis and early warning: Through time series analysis, the changing trends of health indicators are identified and an early warning mechanism is set up, which is connected to the equipment of the elderly’s family members;

[0020] Personalized health assessment: Generates a personalized health assessment report based on the user's age, gender, and medical history information, providing health status analysis.

[0021] As a preferred technical solution of the present invention, the intelligent recommendation module includes:

[0022] Personalized diet recommendation unit: recommends diet plans and recipes based on the physical condition and nutritional needs of the elderly;

[0023] Personalized exercise recommendation unit: recommends exercise type, intensity and frequency based on the physical condition and health data of the elderly;

[0024] Physical examination cycle recommendation unit: recommends physical examination cycles and items based on the elderly’s physical condition and historical physical examination data.

[0025] As a preferred technical solution of the present invention, the personalized diet recommendation unit includes a food recommendation block, and the food recommendation block includes:

[0026] Food Database: Collects nutritional content data of foods and breaks down each food into its main nutrients;

[0027] Nutritional requirement model: The nutritional requirement model is used to match different nutritional intakes for each elderly person;

[0028] Data analysis library: Use linear programming to optimize food combinations and recommend corresponding recipes.

[0029] As a preferred technical solution of the present invention, the nutritional requirement model is:

[0030]

[0031] In the formula, i represents different nutrients, A i represents the requirement of the ith nutrient in grams per day, w i represents the energy weight of the i-th nutrient, E i is the energy provided by each gram of the i-th nutrient, TDEE is the total energy expenditure, j represents different diseases, a ij represents the adjustment factor of the jth disease on the ith nutrient, f(a ij ) represents the mapping function of the adjustment factor.

[0032] As a preferred technical solution of the present invention, the mapping function f(a ij ) is expressed as:

[0033]

[0034] Where m represents the number of all diseases.

[0035] As a preferred technical solution of the present invention, the total energy consumption TDEE is expressed as:

[0036] TEDD = BMR × L;

[0037] Where BMR stands for basal metabolic rate and L stands for activity level coefficient.

[0038] As a preferred technical solution of the present invention, the personalized diet recommendation unit further includes:

[0039] Food delivery service block: cooperate with food suppliers to provide one-click ordering service to facilitate the elderly to purchase the food they need;

[0040] Smart kitchen assistant block: Develop a smart kitchen assistant application to provide voice guidance and step-by-step prompts to assist the elderly in cooking.

[0041] As a preferred technical solution of the present invention, the user interaction module includes:

[0042] Voice assistant unit: provides voice assistant function and dialect user interface options;

[0043] AR and VR interactive unit: Introducing AR and VR technologies to provide immersive entertainment and exercise experiences for the elderly;

[0044] Community interaction platform: Establish a community interaction platform so that the elderly can share life experiences, participate in online activities, make new friends, increase social interaction, introduce a reward mechanism, and provide prizes to the elderly whose health has improved;

[0045] Food photo recognition unit: provides a photo interface to guide the elderly to take photos of food, and displays recognition results and health recommendations.

[0046] The present invention has the following beneficial effects:

[0047] 1. Improve user information security: Through multimodal authentication and the creation of personalized profiles, the system can ensure the security of the elderly’s personal information. This multi-level authentication mechanism not only prevents unauthorized access, but also provides each user with a detailed health profile, including living habits, hobbies, social relationships, etc., thereby providing more comprehensive and personalized services;

[0048] 2. Enhanced family support: The system allows family members of the elderly to access some key information, such as emergency contact information and health report summaries, which enhances family support and care for the elderly. When abnormal data is detected, the system automatically sends alerts to family members and community medical personnel to ensure that necessary medical measures are taken in a timely manner, thereby increasing the participation of family members in the health management of the elderly.

[0049] 3. Efficient data collection and update: The vital sign sensor data collection unit supports smart wearable devices to monitor vital sign data such as heart rate, blood pressure, number of steps, and blood oxygen saturation in real time. The physical examination report data collection unit obtains the latest physical examination report data by taking photos and directly connecting to the physical examination center information database, reducing the tediousness of manual input and ensuring the real-time and accuracy of data, which facilitates the system to collect data;

[0050] 4. Personalized lifestyle suggestions: The intelligent recommendation module provides personalized lifestyle suggestions based on the user's health status, including diet plan, exercise type and frequency, physical examination cycle, etc. Compared with the generally applicable diet guidance, the system can accurately calculate the required amount of each nutrient based on personal physical signs through the nutritional requirement formula, and then recommend a customized diet plan to ensure that users receive comprehensive health management;

[0051] 5. Improve user experience: The voice assistant unit provides multiple dialect options to make the application more inclusive and convenient for the elderly to use. The AR and VR interactive unit introduces immersive technology to provide entertainment and exercise experience for the elderly, which can effectively improve mental health and quality of life. The community interactive platform allows the elderly to share life experiences, participate in online activities, and make new friends, which improves community interactivity, enhances the enthusiasm of the elderly to participate, and contributes to the physical and mental health of the elderly;

[0052] 6. Food photo recognition and health advice: The food photo interface provides a simple and easy-to-use photo interface to guide the elderly to take food photos, and displays recognition results and health advice, which can help users better manage their diet and health;

[0053] 7. Convenient food delivery service: The food delivery service block cooperates with food suppliers or community supermarkets to provide a one-click ordering service, making it convenient for the elderly to purchase the ingredients they need, and provides door-to-door delivery service. The smart kitchen assistant application provides voice guidance and step-by-step prompts to assist the elderly in completing cooking, simplifying the process of shopping and cooking and improving the convenience of life for the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a block diagram of a community smart elderly care service system proposed by the present invention; DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0056] Reference Figure 1, a community smart elderly care service system, includes a user information management module, a data collection module, a data analysis and processing module, an intelligent recommendation module and a user interaction module.

[0057] Among them, the user information management module manages the basic information, physical examination records and sensor data of the elderly. The module is equipped with multimodal authentication and personalized archives, combining biometric technologies such as fingerprints, facial recognition and traditional passwords to improve the security of the system and create personalized health archives for each user, including living habits, hobbies, social relationships, etc., in order to provide more comprehensive services. Secondly, it allows family members of the elderly to access some information, such as emergency contact information and health report summaries, to enhance family support;

[0058] The data collection module collects the user's physical examination information and vital sign sensor data and updates the data regularly. The data collection module includes:

[0059] Vital sign sensor data collection unit: The vital sign sensor data collection unit supports smart wearable devices, such as smart watches, health bracelets, blood glucose meters, and blood pressure monitors, etc., to monitor heart rate, blood pressure, number of steps, blood oxygen saturation and other vital sign data in real time, and transmits data from the device to the system via Bluetooth or Wi-Fi. Thresholds are set in the system. When abnormal data is detected, the system automatically sends an alarm to the user, his family members, and community medical staff;

[0060] Physical examination report data collection unit: The physical examination report data collection unit obtains the physical examination report data of the elderly by taking photos and directly connecting to the information database of the physical examination center. Specifically, the user uploads the photo of the physical examination report through the mobile application or web page. The system combines optical character recognition and natural language processing technology to automatically identify and extract key information. Secondly, the system is connected to the information database of the community physical examination center through the API interface. After the user authorizes, the system can directly obtain the latest physical examination report data.

[0061] Among them, the data analysis and processing module is used to analyze the user's health data, generate health reports, perform trend analysis, and compare and analyze the updated data with the old data. Specifically, the data analysis and processing module includes a deep learning model, which uses a deep learning algorithm to analyze health data and predict potential health risks, such as heart attacks, strokes, etc. It also includes trend analysis and early warning functions. Through time series analysis, it identifies the changing trends of health indicators and sets up an early warning mechanism. The early warning mechanism is connected to the elderly's family devices. Secondly, it also includes personalized health assessments. According to the user's age, gender, medical history and other information, a personalized health assessment report is generated to provide a detailed health status analysis. Finally, the system is equipped with an intelligent recommendation module, which provides personalized lifestyle recommendations based on data analysis and the user's health status.

[0062] The intelligent recommendation module provides personalized lifestyle suggestions based on the user's health status. The module includes:

[0063] Personalized diet recommendation unit: recommends reasonable diet plans and recipes based on the physical condition and nutritional needs of the elderly;

[0064] Personalized exercise recommendation unit: recommends appropriate exercise type, intensity and frequency based on the physical condition and health data of the elderly;

[0065] Physical examination cycle recommendation unit: Based on the elderly's physical condition and historical physical examination data, it recommends reasonable physical examination cycles and items, and the data is connected to the database of the community hospital or community physical examination center, so that community medical staff can remind the elderly to have physical examinations in time and can directly obtain the items that the elderly should undergo physical examinations.

[0066] Among them, the user interaction module provides a user interaction interface for the elderly to operate and interact. The user interaction module includes:

[0067] Voice assistant unit: Provides voice assistant function and dialect user interface options, which makes the application more inclusive, respects and meets the language habits of users in different regions, thereby improving the satisfaction and convenience of use for the elderly;

[0068] AR and VR interactive unit: Introducing AR and VR technologies to provide immersive entertainment and exercise experiences for the elderly. Especially for the elderly who are unable to go out, this unit ensures their mental health and the fun of life.

[0069] Community interaction platform: Establish a community interaction platform so that the elderly can share life experiences, participate in online activities, make new friends, and increase social interaction. At the same time, introduce a reward mechanism to compare the updated physical data of the elderly with the old data, and provide prizes to the elderly whose physical condition has improved, such as community supermarket coupons, etc., and set up a ranking list on the community interaction platform to increase the enthusiasm and interactivity of the elderly;

[0070] Food photo recognition unit: provides a simple and easy-to-use photo interface to guide the elderly to take photos of food, and displays recognition results and health advice. Specifically, the unit uses convolutional neural networks (CNN) to identify food, and connects to nutritional databases such as the USDA FoodDataCentral to obtain detailed nutritional information of food, such as calories, protein, fat, carbohydrates, vitamins and minerals, etc. It generates personalized health advice based on the user's health status, age, gender, weight, activity level and other factors. For some elderly people with diseases, the elderly may not be clear about the types of food they can eat. Setting up this unit will help users better manage their diet and health.

[0071] Furthermore, the personalized diet recommendation unit includes:

[0072] Food recommendation block: This block can recommend nutrients and the amount of nutrients that the elderly should consume based on their physical health conditions;

[0073] Food delivery service block: Cooperate with food suppliers or community supermarkets to provide one-click ordering service, making it convenient for the elderly to purchase the food they need, and provide door-to-door delivery service;

[0074] Smart kitchen assistant block: Develop a smart kitchen assistant application to provide voice guidance and step-by-step prompts to assist the elderly in cooking.

[0075] Furthermore, the food recommendation block includes:

[0076] Food database: collects the nutritional data of food and decomposes each food into its main nutrients. You can also directly use the nutritional database in the existing technology;

[0077] Nutritional requirement model: The nutritional requirement model is used to match different nutritional intakes for each elderly person;

[0078] Data analysis library: Use linear programming to optimize food combinations and recommend corresponding recipes.

[0079] Furthermore, the nutritional requirement model is:

[0080]

[0081] In the formula, i represents different nutrients, A i represents the requirement of the ith nutrient in grams per day, w i represents the energy weight of the i-th nutrient, w i It should be based on the recommended nutrient ratios, for example, common ratios are 50% carbohydrates, 20% protein, and 30% fat. These ratios can be adjusted according to specific circumstances. i is the energy provided by each gram of the i-th nutrient, in (kcal / g), TDEE is the total energy expenditure, in (kcal / day), j represents different diseases, a ij represents the adjustment factor of the jth disease on the ith nutrient, a ij The values ​​are determined based on medical research, clinical guidelines and nutritional recommendations. ij ) represents the mapping function of the adjustment factor;

[0082] Furthermore, the mapping function f(a ij ) is expressed as:

[0083]

[0084] In the formula, m represents the number of all diseases, and the formula represents the set of effects of multiple different diseases on the i-th nutrient.

[0085] Total energy expenditure TDEE is expressed as:

[0086] TEDD = BMR × L;

[0087] Where BMR stands for basal metabolic rate and L stands for activity level coefficient.

[0088] Embodiment 1

[0089] The Harris-Benedict equation was used to calculate the basal metabolic rate, where the basal metabolic rate for men is:

[0090] BMR male =88.362+(13.397×weight)+(4.799×height)-(5.677×age),

[0091] The basal metabolic rate for women is:

[0092] BMR female =447.593+(9.247×weight)+(3.098×height)-(4.330×age);

[0093] In the formula, weight is in kilograms (kg), height is in centimeters (cm), and age is in years. This formula is simple and easy to use in the application process. It only requires four basic parameters: height, weight, age, and gender. It is very easy to obtain and use, which improves the efficiency of system operation.

[0094] The steps to obtain the activity level coefficient L are as follows:

[0095] Activity level classification: Activity levels are divided into five levels: sedentary, light activity, moderate activity, heavy activity and very heavy activity, such as office work, watching TV, reading, etc. Light activity means light exercise or occasional physical activity, and daily activity is reflected in moderate-intensity exercise 1-3 days a week, such as walking, light housework, etc. Moderate activity means moderate-intensity exercise or sports activities, and daily activity is reflected in moderate-intensity exercise 3-5 days a week, such as brisk walking, cycling, swimming, etc. Heavy activity means high-intensity exercise or sports activities, and daily activity is reflected in high-intensity exercise 6-7 days a week, such as running, fitness training, heavy physical labor, etc. Very heavy activity means a very high level of physical activity, and daily activity is reflected in high-intensity training or heavy physical labor every day, such as professional athletes, construction workers, etc.

[0096] Each activity level is numerically quantified: the total activity amount (MET-minutes / week) is used to distinguish the above activity levels. When the total activity amount is less than 600 MET-minutes / week, the corresponding activity level is sedentary, when the interval is [600, 3000) MET-minutes / week, the corresponding activity level is light activity, when the interval is [3000, 6000) MET-minutes / week, the corresponding activity level is moderate activity, when the interval is [6000, 12000) MET-minutes / week, the corresponding activity level is heavy activity, and when the interval is greater than or equal to 12000 MET-minutes / week, the corresponding activity level is very heavy activity. The activity level coefficients L corresponding to the five levels are 1.2, 1.375, 1.55, 1.725 and 1.9 respectively;

[0097] Data collection: including the time and frequency of walking, moderate-intensity activities, and high-intensity activities. Specifically, the data can be obtained through questionnaire surveys, and activity information of the elderly is collected based on the International Physical Activity Questionnaire (IPAQ) and the Goldman Physical Activity Questionnaire (GSLTPAQ);

[0098] Data processing: Calculate the total activity based on the collected data. The calculation formula is:

[0099] MET-minutes / week = (walking time × 3.3) + (moderate-intensity activity time × 4.0) + (high-intensity activity time × 8.0). The calculated data is matched with the activity level grade to obtain the corresponding activity level coefficient L.

[0100] Based on the above data, the nutrient requirements for an elderly male with diabetes are calculated as follows, assuming that the male is 70 years old, weighs 70 kg, is 170 cm tall, and has a light activity level, that is, the activity level coefficient L = 1.375, and the BMR is male =88.362+(13.397×70)+(4.799×170)-(5.677×70)=1444.592 kcal / day, then TDEE=1444.592×1.375=1986.86 kcal / day. For diabetic patients, the common nutrient recommended ratios and adjustment factors are:

[0101] Carbohydrates: A lower carbohydrate intake is usually recommended to help control blood sugar. Assuming the recommended ratio w1 is 45%, the adjustment factor f(a 11 ) is 0.8, i.e. a 20% reduction, and the energy E1 provided is 4 kcal / g;

[0102] Protein: Protein requirements may be slightly increased to help maintain muscle mass, assuming the recommended ratio w2 is 20%, with an adjustment factor f(a 21 ) is 1.1, i.e., an increase of 10%, providing energy E2 of 4 kcal / g;

[0103] Fat: Fat requirements may need to be increased appropriately to supplement the reduced carbohydrates. Assuming the recommended ratio w3 is 35%, the adjustment factor f(a 31 ) is 1.0, and the energy E3 provided is 9 kcal / g;

[0104] The carbohydrate requirement of the elderly is:

[0105]

[0106] Protein requirements are:

[0107]

[0108] The fat requirement is:

[0109]

[0110] Embodiment 2

[0111] Different from the first embodiment, based on the Harris-Benedict equation, the body fat percentage adjustment factor F, the muscle mass adjustment factor M and the ambient temperature adjustment factor T are introduced into the calculation process of the basal metabolic rate BMR. Since the higher the body fat, the lower the metabolic rate, F is set to 1-0.01×body fat percentage; since the higher the muscle mass, the higher the metabolic rate, M is set to 1+0.01×muscle mass percentage; in a cold environment, the metabolic rate will increase to maintain body temperature, while in a warm environment, the metabolic rate will decrease, and T is set to 1+0.01×(ambient temperature-20). Therefore, the basal metabolic rate of men is:

[0112] BMR male =[88.362+(13.397×weight)+(4.799×height)-(5.677×age)]×F×M×T;

[0113] The basal metabolic rate for women is:

[0114] BMR female =[447.593+(9.247×weight)+(3.098×height)-(4.330×age)]×F×M×T;

[0115] This calculation formula can more accurately reflect an individual's body composition by introducing body fat percentage and muscle mass. For example, for two elderly people of the same weight, if one has higher muscle mass, then his BMR will also be higher, and the amount of nutrients required will also be different. By introducing adjustment factors for body fat percentage and muscle mass, it can better reflect the metabolic differences in different body compositions. The metabolic rate of adipose tissue is lower, while the metabolic rate of muscle tissue is higher. Therefore, this adjustment can improve the accuracy of BMR calculation. By introducing the adjustment factor of ambient temperature, it can more truly reflect the energy needs of the human body under different ambient temperatures. This adjustment makes the BMR calculation closer to actual living conditions. Through more accurate BMR calculations, it can help individuals better understand their energy needs and thus develop more scientific diet plans and exercise plans.

[0116] Based on this embodiment, the nutrient requirement for an elderly male with diabetes is calculated as follows, assuming that the elderly male is 70 years old, weighs 70 kg, is 170 cm tall, has a body fat percentage of 20%, a muscle mass percentage of 30%, an ambient temperature of 22°C, and an activity level of light activity, that is, the activity level coefficient L = 1.375, BMR male =[88.362+(13.397×70)+(4.799×170)-(5.677×70)]×(1-0.01×20)×(1+0.01×30)×(1+0.01×(22-20))≈1499.58 kcal / day, then TDEE=1499.58×1.375≈2061.96 kcal / day, the recommended carbohydrate ratio w1 is 45%, and the adjustment factor f(a 11 ) is 0.8, the energy provided E1 is 4 kcal / g, the recommended protein ratio w2 is 20%, and the adjustment factor f(a 21 ) is 1.1, the energy provided E2 is 4 kcal / g, the recommended fat ratio w3 is 35%, and the adjustment factor f(a 31 ) is 1.0, and the energy E3 provided is 9 kcal / g;

[0117] The carbohydrate requirement of the elderly is:

[0118]

[0119] Protein requirements are:

[0120]

[0121] The fat requirement is:

[0122]

[0123] In practical applications, this nutritional requirement model can provide accurate nutrient content recommendations based on the individual physical condition, activity level and specific health conditions of the elderly, generate personalized nutritional advice, and ensure that each elderly person receives a diet plan that best suits their health needs. This personalized service not only helps optimize nutritional intake, prevent malnutrition and related diseases, but also improves the overall physical health and quality of life of the elderly.

[0124] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A community smart elderly care service system, characterized in that: include: User information management module: manages basic information, physical examination records and sensor data of the elderly; Data collection module: collects users’ physical examination information, vital sign sensor data, and updates the data regularly; Data analysis and processing module: analyzes the user's health data, generates health reports, conducts trend analysis, and compares and analyzes updated data with old data; Intelligent recommendation module: provides personalized lifestyle suggestions based on the user's health status; User interaction module: provides a user interaction interface for elderly people to operate and interact.

2. A community smart elderly care service system according to claim 1, characterized in that: The data acquisition module comprises: Vital sign sensor data acquisition unit: supports smart wearable devices, including but not limited to smart watches and health bracelets, real-time monitoring of heart rate, blood pressure, and step count data, and automatically synchronizes them to the system; Physical examination report data collection unit: obtains the physical examination report data of the elderly by taking photos and directly connecting to the information database of the physical examination center.

3. A community smart elderly care service system according to claim 1, characterized in that: The data analysis and processing module includes: Deep learning model: Use deep learning algorithms to analyze health data and predict potential health risks; Trend analysis and early warning: Through time series analysis, the changing trends of health indicators are identified and an early warning mechanism is set up, which is connected to the equipment of the elderly’s family members; Personalized health assessment: Generates a personalized health assessment report based on the user's age, gender, and medical history information, providing health status analysis.

4. A community smart elderly care service system according to claim 1, characterized in that: The intelligent recommendation module includes: Personalized diet recommendation unit: recommends diet plans and recipes based on the physical condition and nutritional needs of the elderly; Personalized exercise recommendation unit: recommends exercise type, intensity and frequency based on the physical condition and health data of the elderly; Physical examination cycle recommendation unit: recommends physical examination cycles and items based on the elderly’s physical condition and historical physical examination data.

5. A community smart elderly care service system according to claim 4, characterized in that: The personalized diet recommendation unit includes a food recommendation block, and the food recommendation block includes: Food Database: Collects nutritional content data of foods and breaks down each food into its main nutrients; Nutritional requirement model: The nutritional requirement model is used to match different nutritional intakes for each elderly person; Data analysis library: Use linear programming to optimize food combinations and recommend corresponding recipes.

6. A community smart elderly care service system according to claim 5, characterized in that: The nutritional requirement model is: In the formula, i represents different nutrients, A i represents the requirement of the ith nutrient in grams per day, w i represents the energy weight of the i-th nutrient, E i is the energy provided by each gram of the i-th nutrient, TDEE is the total energy expenditure, j represents different diseases, a ij represents the adjustment factor of the jth disease on the ith nutrient, f(a ij ) represents the mapping function of the adjustment factor.

7. A community smart elderly care service system according to claim 6, characterized in that: The mapping function f(a) of the adjustment factor ij ) is expressed as: Where m represents the number of all diseases.

8. A community smart elderly care service system according to claim 6, characterized in that: The total energy expenditure TDEE is expressed as: TEDD = BMR × L; Where BMR stands for basal metabolic rate and L stands for activity level coefficient.

9. A community smart elderly care service system according to claim 4, characterized in that: The personalized diet recommendation unit also includes: Food delivery service block: cooperate with food suppliers to provide one-click ordering service to facilitate the elderly to purchase the food they need; Smart kitchen assistant block: Develop a smart kitchen assistant application to provide voice guidance and step-by-step prompts to assist the elderly in cooking.

10. A community smart elderly care service system according to claim 1, characterized in that: The user interaction module comprises: Voice assistant unit: provides voice assistant function and dialect user interface options; AR and VR interactive unit: Introducing AR and VR technologies to provide immersive entertainment and exercise experiences for the elderly; Community interaction platform: Establish a community interaction platform so that the elderly can share life experiences, participate in online activities, make new friends, increase social interaction, introduce a reward mechanism, and provide prizes to the elderly whose health has improved; Food photo recognition unit: provides a photo interface to guide the elderly to take photos of food, and displays recognition results and health recommendations.

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