Diet intervention method and system for crowd with cardiopulmonary health risk

Abstract

The invention discloses a diet intervention method and system for crowds with cardiopulmonary health risks. The system comprises a database, an information acquisition module, a risk assessment module, a calculation module, a diet recommendation module, an auditing module and an information pushing module. The database comprises a personal information database, a suggestion database, a recipe database and a different-energy-level food intake database; the information acquisition module is used for acquiring intelligent bed sleep health data and personal information data in a database, and generating a personal user data set for the risk assessment module and the calculation module; wherein the sleep health data collected by the intelligent bed comprises but is not limited to the whole late heart rate, the respiration rate, the heart rate variability and the like; according to the invention, through combination of static and dynamic health data, through risk assessment and early warning, diet health management is carried out on crowds with cardiopulmonary health risks, and the problem that the public cannot effectively utilize knowledge to carry out self-health management actually is accurately solved.

Description

Technical Field

[0001] The present invention relates to the technical field of diet intervention methods and systems for people at risk of cardiopulmonary health, and specifically to a diet intervention method and system for people at risk of cardiopulmonary health. Background Art

[0002] More and more studies have proved that health management can be intervened through diet, and diet intervention plays an effective role in disease prevention, during illness, and in the rehabilitation stage. The irrationality of the dietary structure has caused obesity and various sub-healthy problems among residents. Many studies have shown that diet is closely related to chronic diseases related to cardiopulmonary such as hypertension and heart disease. However, firstly, the sources of online diet advice knowledge are numerous, the quantity is huge, the accuracy is uneven, and the expression methods are also different, making it difficult for people to find a truly high-quality diet plan suitable for individual needs. Secondly, most of this knowledge is general and static, and it is not recommended by combining the individual's physical condition and dynamic changes in health data, lacking individual pertinence. And most people do not have a relatively accurate perception of the portion size of food and how much food to eat. All these lead to the fact that the public actually cannot effectively use knowledge for self-health management.

[0003] In view of this, the present invention combines static and dynamic health data, and through risk assessment and early warning, conducts diet health management for people at risk of cardiopulmonary health to solve the above problems. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, but such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above and / or problems existing in the prior art of a diet intervention method and system for people at risk of cardiopulmonary health, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a diet intervention method and system for people at risk of cardiopulmonary health. The present invention combines static and dynamic health data, and through risk assessment and early warning, conducts diet health management for people at risk of cardiopulmonary health, accurately solving the problem that the public actually cannot effectively use knowledge for self-health management.

[0007] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided:

[0008] A diet intervention method and system for people at risk of cardiopulmonary health, which includes:

[0009] A database, an information collection module, a risk assessment module, a calculation module, a diet recommendation module, a review module, and an information push module;

[0010] The database includes: a personal information database, a suggestion database, a recipe database, and a food intake database for different energy levels;

[0011] The information collection module includes: the collection of sleep health data of the intelligent bed and the collection of personal information data in the database, generating a personal user dataset for use by the risk assessment module and the calculation module; among them, the sleep health data collected by the intelligent bed includes, but is not limited to: overnight heart rate, respiratory rate, heart rate variability, etc.; the data in the personal information database is initially collected in the form of an online questionnaire, stored in the personal information database after collection, and updated regularly;

[0012] Among them, the intelligent bed includes an embedded sensor network composed of a heart rate monitor, a respiratory monitor, and a sleep monitor, a low-power processor for processing data and storage, a wireless communication module for sending data to a smartphone application or server, an Android or iOS application for displaying sleep data and providing a user interface to manage sleep parameters, and a backend service for data storage analysis and communication with the application;

[0013] The risk assessment module is mainly used to evaluate the user's health status, mainly for cardiovascular and cerebrovascular health assessment. Long-term (10-year and lifetime) risk assessment uses the China-PAR model, and short-term risk assessment is comprehensively evaluated according to heart rate variability (HRV);

[0014] The calculation module mainly calculates the energy level required by the user's body according to the user's gender, height, weight, and physical activity level, and matches the corresponding food intake according to this energy level;

[0015] The diet recommendation module is used to generate dietary recommendation opinions. First, match the information in the suggestion database according to the risk assessment results to generate a preliminary draft of the dietary suggestion; then, according to the food intake information obtained by the calculation module, combined with the user's dietary taboos and preferences, match in the recipe database to generate a final draft of the dietary suggestion;

[0016] The review module, for high-risk groups, before pushing the dietary suggestion, will push the dietary suggestion online to a physician or a dietitian for manual secondary review and confirmation;

[0017] The information push module is used to display the generated dietary suggestion to the user in the form of pictures and texts.

[0018] As a preferred embodiment of the dietary intervention method and system for cardiopulmonary health risk population of the present invention, the personal information database is used to store the personal information of users, providing the necessary information for calculation in the risk assessment section and calculation module, and realizing recommendation according to personal dietary taboos / preferences in the dietary recommendation module. The content of this database includes but is not limited to gender, age, height, weight, physical activity level, dietary taboos, dietary preferences, disease history, family disease history, and drug use situation.

[0019] As a preferred embodiment of the dietary intervention method and system for cardiopulmonary health risk population of the present invention, the recommendation database is mainly used to store the food and population association information that different risk populations should / should not eat, and is used for the dietary recommendation module to generate the initial draft of dietary recommendations. Foods are divided into two major categories: dietary and nutrients. The major categories of dietary include grains, potatoes, vegetables and fruits, milk and beans, animal foods, etc. The major categories of nutrients include fatty acids, minerals, vitamins, and dietary fiber; the recipe database is used to store common recipes associated with diets and is used for the dietary recommendation module to generate the final draft of dietary recommendations.

[0020] As a preferred embodiment of the dietary intervention method and system for cardiopulmonary health risk population of the present invention, the database of food intake at different energy levels is used to provide information on the daily food intake of the human body, including the recommended intakes of 17 foods (cereals, whole grains, potatoes, vegetables, dark vegetables, fruits, milk, soybeans and their products, nuts, livestock and poultry, aquatic products, eggs, cooking oil, alcoholic beverages, sugar, salt, water) at 11 energy levels.

[0021] 6. As a preferred embodiment of the dietary intervention method and system for cardiopulmonary health risk population of the present invention, the specific usage steps are as follows:

[0022] S1 Information collection: (1) Collect the user's personal information from the user personal information database. This information is initially collected through an online questionnaire, and the content includes but is not limited to: gender, age, height, weight, physical activity level (high, medium, low), dietary taboos, dietary preferences, disease history, medication situation, family disease history, blood pressure, blood sugar and other health information; (2) Dynamically collect the user's sleep health data through a smart bed, including but not limited to: heart rate, respiratory rate, heart rate variability, body movement, snoring times, apnea times, etc. The above information forms a personal information dictionary;

[0023] S2 Risk assessment: (1) Using the China-PAR model with information on user's gender, age, place of residence, region, waist circumference, total cholesterol, high-density lipoprotein cholesterol, blood pressure, whether taking antihypertensive drugs, blood sugar, smoking, and family history of cardiovascular disease to obtain the 10-year cardiovascular disease risk; (2) Evaluating the short-term cardiovascular health risk of the user according to the LF and HF indicators, calculating LF / HF. When the value is lower than 1.2, it indicates a risk. When the value is lower than 1, it indicates a high risk.

[0024] S3 Calculation of required dietary intake:

[0025] (1) First, calculate the user's BMI index using height and weight. The calculation formula is:

[0026] BMI = W / H 2

[0027] W (weight): kg

[0028] H (height): m

[0029] (2) Determine whether the user's BMI is normal (18.5 ≤ BMI < 24). If it is normal, use the original weight for the next calculation; if the user's BMI shows emaciation or overweight / obesity (BMI < 18.5 or BMI ≥ 24), then calculate the normal weight value at this height for the user to be used in the next calculation. The calculation formula for the normal weight value is as follows:

[0030] W 标 = B * H 2

[0031] B (normal BMI value): If the user's actual BMI < 18.5, take the value as 18.5; if the user's actual BMI ≥ 24, take the value as 23.9;

[0032] H (height): the user's height, m.

[0033] (3) Calculate the user's daily energy requirement (EER). The calculation formula is:

[0034] EER = COE * PAL(14.52W - 155.88S + 565.79)

[0035] COE (age coefficient): 18 - 49 = 1; 50 - 64 = 0.95; 65 - 74 = 0.925; >75 = 0.9;

[0036] PAL (physical activity level): low 1.4 kcal / day; moderate 1.7 kcal / day; high 2.0 kcal / day;

[0037] W (weight): kg

[0038] S(Gender): Male = 0, Female = 1

[0039] (5) Match according to the required energy value in the food intake database at different energy levels, obtain the recommended intakes of 17 foods, and generate a personal food intake dictionary;

[0040] S4 Dietary Recommendation Plan Generation:

[0041] (1) Draft Generation: Match according to the risk assessment results in the recommendation database to obtain a list of food information that the user should and should not eat, and generate a draft of the dietary recommendation;

[0042] (2) Final Draft Generation: Extract the user's dietary taboos and dietary preference information from the collected information, combine the information in the draft of the dietary recommendation, perform food type matching in the recipe database, and obtain relevant food recipes. Combine the personal food intake dictionary to determine the food portion and generate the final draft of the dietary recommendation;

[0043] S5 Expert Review: For the population with a high risk in the short-term risk assessment, the dietary recommendation plan is pushed online to a dietitian for secondary review;

[0044] S6 Report Generation and Push: Push the generated final draft of the dietary recommendation to the user in the form of pictures and texts.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By combining static and dynamic health data, through risk assessment and early warning, the present invention conducts dietary health management for the population at risk of cardiopulmonary health, and accurately solves the problem that the public is actually unable to effectively utilize knowledge for self-health management. Brief Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in conjunction with the drawings and detailed embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0047] Figure 1 It is a system diagram of a dietary intervention method and system for a population at risk of cardiopulmonary health according to the present invention;

[0048] Figure 2 It is a system diagram of calculating BMI of a dietary intervention method and system for a population at risk of cardiopulmonary health according to the present invention;

[0049] Figure 3 It is an information push flow chart based on personal information of a dietary intervention method and system for a population at risk of cardiopulmonary health according to the present invention. Detailed Embodiments

[0050] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.

[0051] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure are locally enlarged out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0052] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will further describe the embodiments of the present invention in detail with reference to the accompanying drawings.

[0053] The present invention provides a dietary intervention method and system for people at risk of cardio-pulmonary health problems. By combining static and dynamic health data, through risk assessment and early warning, the present invention conducts dietary health management for people at risk of cardio-pulmonary health problems, accurately solving the problem that the public is actually unable to effectively utilize knowledge for self-health management.

[0054] Figures 1 - 3 Shown is a schematic diagram of the overall structure of an embodiment of a dietary intervention method and system for people at risk of cardio-pulmonary health problems of the present invention. Please refer to Figures 1 - 3 , an embodiment of a dietary intervention method and system for people at risk of cardio-pulmonary health problems of the present embodiment, its main parts include:

[0055] A database, an information collection module, a risk assessment module, a calculation module, a dietary recommendation module, a review module, and an information push module;

[0056] The database includes: a personal information database, a suggestion database, a recipe database, and a database of food intake at different energy levels;

[0057] The information collection module includes: the collection of sleep health data of the intelligent bed and the collection of personal information data in the database, generating a personal user dataset for use by the risk assessment module and the calculation module; among them, the sleep health data collected through the intelligent bed includes, but is not limited to: all-night heart rate, respiratory rate, heart rate variability, etc.; the data in the personal information database is initially collected in the form of an online questionnaire and stored in the personal information database after collection and updated regularly;

[0058] Among them, the smart bed includes an embedded sensor network composed of a heart rate monitor, a respiration monitor, and a sleep monitor, a low-power processor for data processing and storage, a wireless communication module for sending data to a smartphone application or a server, an Android or iOS application for displaying sleep data and providing a user interface to manage sleep parameters, and a backend service for data storage analysis and communication with the application;

[0059] The risk assessment module is mainly used to evaluate the user's health condition, mainly for cardiovascular and cerebrovascular health assessment. Long-term (10-year and lifetime) risk assessment uses the China-PAR model, and short-term risk assessment is comprehensively evaluated according to heart rate variability (HRV);

[0060] The calculation module mainly calculates the energy level required by the user's body based on the user's gender, height, weight, and physical activity level, and matches the corresponding intake of various types of food according to this energy level;

[0061] The diet recommendation module is used to generate dietary recommendation opinions. First, match the information in the recommendation database according to the risk assessment results to generate a preliminary draft of the dietary recommendation; then, according to the food intake information obtained by the calculation module, combined with the user's dietary taboos and preferences, match in the recipe database to generate the final draft of the dietary recommendation;

[0062] For high-risk groups, before pushing the dietary recommendation, the review module will push the dietary recommendation online to a physician or a dietitian for manual secondary review and confirmation;

[0063] The information push module is used to display the generated dietary recommendation to the user in the form of pictures and texts;

[0064] Among them, the personal information database is used to store the user's personal information, providing the information necessary for calculations in the risk assessment section and the calculation module, and implementing recommendations according to personal dietary taboos / preferences in the dietary recommendation module. The content of this database includes, but is not limited to, gender, age, height, weight, physical activity level, dietary taboos, dietary preferences, medical history, family medical history, and medication use; the recommendation database is mainly used to store the food and population association information that should / should not be eaten by different risk groups, and is used by the dietary recommendation module to generate the initial draft of dietary recommendations. Foods are divided into two major categories: dietary and nutrients. The major categories of dietary include grains, tubers, vegetables, fruits, milk and beans, and animal foods. The major categories of nutrients include fatty acids, minerals, vitamins, and dietary fiber; the recipe database is used to store common recipes associated with the diet, and is used by the dietary recommendation module to generate the final draft of dietary recommendations; the database of food intake at different energy levels is used to provide information on the daily food intake of the human body, including the recommended intakes of 17 foods (cereals, whole grains, tubers, vegetables, dark vegetables, fruits, milk, soybeans and their products, nuts, livestock and poultry, aquatic products, eggs, cooking oil, alcoholic beverages, sugar, salt, water) at 11 energy levels;

[0065] Further, the specific usage method steps are as follows:

[0066] S1 Information collection: (1) Collect the user's personal information from the user's personal information database. This information is initially collected through an online questionnaire, and the content includes, but is not limited to: gender, age, height, weight, physical activity level (high, medium, low), dietary taboos, dietary preferences, medical history, medication use, family medical history, blood pressure, blood sugar and other health information; (2) Dynamically collect the user's sleep health data through a smart bed, including, but not limited to: heart rate throughout the night, respiratory rate, heart rate variability, body movement, snoring times, apnea times, etc. The above information forms a personal information dictionary;

[0067] S2 Risk assessment: (1) Use the China-PAR model with the user's gender, age, place of residence, region, waist circumference, total cholesterol, high-density lipoprotein cholesterol, blood pressure, whether taking antihypertensive drugs, blood sugar, smoking, and family cardiovascular disease history information to obtain the 10-year cardiovascular disease risk; (2) Evaluate the user's short-term cardiovascular health risk according to the LF and HF indicators, calculate LF / HF, and when the value is lower than 1.2, it indicates a risk, and when the value is lower than 1, it indicates a high risk;

[0068] S3 Calculation of the required dietary amount:

[0069] (1) First, calculate the user's BMI index using height and weight. The calculation formula is:

[0070] BMI = W / H 2

[0071] W (Weight): kg

[0072] H (Height): m

[0073] (2) Determine whether the user's BMI is normal (18.5 ≤ BMI < 24). If it is normal, use the original weight for the next calculation; if the user's BMI shows emaciation or overweight / obesity (BMI < 18.5 or BMI ≥ 24), then calculate the normal weight value for the user's height for use in the next calculation. The formula for the normal weight value is as follows:

[0074] W 标 = B * H 2

[0075] B (Normal BMI value): If the user's actual BMI < 18.5, take the value as 18.5; if the user's actual BMI ≥ 24, take the value as 23.9;

[0076] H (Height): The user's height, m.

[0077] (3) Calculate the user's daily energy requirement (EER). The formula is:

[0078] EER = COE * PAL (14.52W - 155.88S + 565.79)

[0079] COE (Age coefficient): 18 - 49 = 1; 50 - 64 = 0.95; 65 - 74 = 0.925; >75 = 0.9;

[0080] PAL (Physical activity level): Low 1.4 kcal / day; Moderate 1.7 kcal / day; High 2.0 kcal / day;

[0081] W (Weight): kg

[0082] S (Gender): Male = 0, Female = 1

[0083] (6) Match according to the required energy value in the food intake database at different energy levels to obtain the recommended intakes of 17 foods and generate a personal food intake dictionary;

[0084] S4 Dietary Recommendation Plan Generation:

[0085] (1) Draft generation: Match according to the risk assessment results in the recommendation database to obtain the list of food information that the user should and should not eat, and generate the initial draft of the dietary recommendation;

[0086] (2) Final draft generation: Extract the user's dietary taboos and preferences from the collected information, combine it with the information in the initial draft of the dietary advice, perform food type matching in the recipe database, and obtain relevant food recipes. Combine with the personal food intake dictionary to determine the food portion and generate the final draft of the dietary recommendation;

[0087] S5 Expert review: For those with a high risk in the short-term risk assessment, the dietary recommendation plan is pushed online to a dietitian for secondary review;

[0088] S6 Report generation and push: Push the generated final draft of the dietary recommendation to the user in the form of pictures and texts.

[0089] The specific implementation methods are as follows:

[0090] 1. User information collection: Collect user A's personal information from the personal information database, for example: {'ID': 001, 'gender':'male', 'age': 59, 'height': 173, 'weight': 85, 'physical activity level': 'low', 'place of residence': 'city','region':'south', 'waist circumference': 90, 'total cholesterol': 6.5, 'high-density lipoprotein cholesterol': 2.1,'systolic blood pressure': 155, 'diastolic blood pressure': 90, 'drug': 'antihypertensive drug', 'fasting blood glucose': 5.1,'smoking': 'yes', 'family history of cardiovascular disease': 'yes', 'dietary taboos': 'lactose intolerance, mango allergy', 'dietary preference':'soy products'}. Collect user A's sleep health data {'ID': 001, 'LF': 1.22, 'HF': 1.96} from the smart bed to form user A's personal information dictionary;

[0091] 2. Risk assessment: (1) Extract gender, age, place of residence, region, waist circumference, total cholesterol, high-density lipoprotein cholesterol, blood pressure, whether taking antihypertensive drugs, blood glucose, smoking, and family history of cardiovascular disease information from user A's personal information dictionary, and use the CHINA-PAR model to obtain the user's 10-year cardiovascular risk as medium risk; (2) Use LF and HF to evaluate the short-term cardiovascular risk result, LF / HF = 0.62, and the result is high risk;

[0092] 3. Calculation of required dietary amount:

[0093] (1) Obtain the height and weight from the user information dictionary, substitute them into the BMI formula to get the value BMI = 85 / 1.73 2 = 28.4;

[0094] (2) Since BMI ≥ 24, calculate the standard weight value W 标 = 23.9 * 1.73 2 = 71.53 kg;

[0095] (3) Calculate the EER: EER = 0.95 * 1.4(14.52 * 71.35 - 155.88 * 0 + 565.79) = 2130 kJ

[0096] (4) Obtain the energy level 2200 that is closest to 2130 in the food intake database at different energy levels, and obtain the dietary intake at this energy level to form an intake dictionary;

[0097] 4. Generate a preliminary draft of the dietary plan: According to the risk assessment results, user A belongs to the cardiovascular risk group. Retrieve the information on foods suitable and unsuitable for cardiovascular risk groups from the diet advice library. For example: {'suitable': ['vegetables','spinach', 'fungi', 'fruits', 'yam', 'banana', 'tea'], 'unsuitable': ['salt', 'alcohol', 'cholesterol']}.

[0098] 5. Generate the final draft of the dietary plan: Obtain the user's dietary taboos and preferences, remove the taboo foods from the suitable foods, and give priority to recommending the preferred foods. In this case, when matching in the recipe database, avoid matching dairy products and mangoes, and give priority to recommending soy products.

[0099] An example of the generated final draft of the dietary plan is as follows:

[0100] Dear User A, based on your cardiovascular risk assessment results, it is recommended that you increase the intake of fresh vegetables, fruits, beans, nuts, whole grains, and fish in your daily life to reduce the risk of cardiovascular diseases.

[0101] Based on your energy requirements, the recommended daily intake is as follows:

[0102]

[0103]

[0104] The following is an example of a recommended recipe for you:

[0105]

[0106]

[0107] Although the present invention has been described above with reference to the embodiments, various modifications can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in the present invention can be combined with each other in any way, and the reason for not exhaustively describing these combinations in this specification is only to save space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A dietary intervention method and system for people at risk of cardiorespiratory health problems, characterized in that, Including: A database, an information collection module, a risk assessment module, a calculation module, a diet recommendation module, a review module, and an information push module; The database includes: a personal information database, a suggestion database, a recipe database, and a database of food intake at different energy levels; The information collection module includes: the collection of intelligent bed sleep health data and the collection of personal information data in the database, generating a personal user dataset for use by the risk assessment module and the calculation module; among them, the sleep health data collected by the intelligent bed includes, but is not limited to, all-night heart rate, respiratory rate, heart rate variability, etc.; the data in the personal information database is initially collected in the form of an online questionnaire, stored in the personal information database after collection, and updated regularly; Among them, the intelligent bed includes an embedded sensor network composed of a heart rate monitor, a respiratory monitor, and a sleep monitor, a low-power processor for processing data and storage, a wireless communication module for sending data to a smartphone application or server, an Android or iOS application for displaying sleep data and providing a user interface to manage sleep parameters, and a backend service for data storage analysis and communication with the application; The risk assessment module is mainly used to evaluate the user's health status, mainly for cardiovascular and cerebrovascular health assessment. Long-term (10-year and lifetime) risk assessment uses the China-PAR model, and short-term risk assessment is comprehensively evaluated according to heart rate variability (HRV); The calculation module mainly calculates the energy level required by the user's body according to the user's gender, height, weight, and physical activity level, and matches the corresponding food intake of various types according to this energy level; The diet recommendation module is used to generate dietary recommendation opinions. First, match information in the suggestion database according to the risk assessment results to generate a preliminary draft of the dietary suggestion; then, according to the food intake information obtained by the calculation module, combined with the user's dietary taboos and preferences, match in the recipe database to generate a final draft of the dietary suggestion; The review module, for high-risk groups, before pushing the dietary suggestion, will push the dietary suggestion online to a physician or dietitian for manual secondary review and confirmation; The information push module is used to display the generated dietary suggestion to the user in the form of pictures and texts.

2. The dietary intervention method and system for people at risk of cardio-pulmonary health according to claim 1, wherein The personal information database is used to store the user's personal information, provide the necessary information for calculation in the risk assessment section and the calculation module, and implement recommendations according to personal dietary taboos / preferences in the diet recommendation module. The content of this database includes, but is not limited to, gender, age, height, weight, physical activity level, dietary taboos, dietary preferences, disease history, family disease history, and drug use situation.

3. The dietary intervention method and system for people at risk of cardiopulmonary health according to claim 1, characterized in that, The suggestion database is mainly used to store the food and population association information that different risk groups should / should not eat, and is used for the diet recommendation module to generate a preliminary draft of the diet recommendation. Food is divided into two major categories: diet and nutrients. The major categories of diet include cereal and tuber foods, vegetables and fruits, milk and legumes, and animal foods. The major categories of nutrients include fatty acids, minerals, vitamins, and dietary fiber. The recipe database is used to store common recipes associated with the diet and is used by the diet recommendation module to generate the final diet recommendation.

4. A dietary intervention method and system for people at risk of cardio-pulmonary health problems according to claim 1, characterized in that, The food intake database for different energy levels is used to provide information on the daily food intake of the human body, including the recommended intakes of 17 foods (cereals, whole grains, tubers, vegetables, dark vegetables, fruits, milk, soybeans and their products, nuts, livestock and poultry, aquatic products, eggs, cooking oil, alcoholic beverages, sugar, salt, water) at 11 energy levels.

5. The dietary intervention method and system for people at risk of cardiorespiratory health according to claim 1, characterized in that The specific usage method steps are as follows: S1 Information collection: (1) Collect user personal information from the user personal information database. This information was initially collected through an online questionnaire, and the content includes but is not limited to: gender, age, height, weight, physical activity level (high, medium, low), dietary taboos, dietary preferences, medical history, medication use, family medical history, blood pressure, blood sugar and other health information; (2) Dynamically collect user sleep health data through a smart bed, including but not limited to: heart rate throughout the night, respiratory rate, heart rate variability, body movement, snoring times, apnea times, etc. The above information forms a personal information dictionary; S2 Risk assessment: (1) Use the China-PAR model on user information such as gender, age, place of residence, region, waist circumference, total cholesterol, high-density lipoprotein cholesterol, blood pressure, whether taking antihypertensive drugs, blood sugar, smoking, and family history of cardiovascular disease to obtain the 10-year cardiovascular disease risk; (2) Evaluate the short-term cardiovascular health risk of the user according to the LF and HF indicators, calculate LF / HF. When the value is less than 1.2, it indicates a risk, and when the value is less than 1, it indicates a high risk; S3 Calculation of the required diet quantity: (1) First, calculate the user's BMI index using height and weight. The calculation formula is: BMI = W / H 2 W (weight): kg H (height): m (2) Determine whether the user's BMI is normal (18.5 ≤ BMI < 24). If it is normal, use the original weight for the next calculation; if the user's BMI shows emaciation or overweight / obesity (BMI < 18.5 or BMI ≥ 24), then calculate the normal weight value for this height of the user for use in the next calculation. The normal weight value calculation formula is as follows: W 标 = B * H 2 B (normal BMI value): If the user's actual BMI < 18.5, take the value as 18.5; if the user's actual BMI ≥ 24, take the value as 23.9; H (height): user height, m. (3) Calculate the user's daily required energy (EER). The calculation formula is: EER = COE * PAL (14.52W - 155.88S + 565.79) COE (age coefficient): 18 - 49 = 1; 50 - 64 = 0.95; 65 - 74 = 0.925; >75 = 0.9; PAL (physical activity level): low 1.4 kcal / day; medium 1.7 kcal / day; high 2.0 kcal / day; W (weight): kg S(Gender): Male = 0, Female = 1 (4) Match according to the required energy value in the food intake database at different energy levels, obtain the recommended intakes of 17 kinds of foods, and generate a personal food intake dictionary; S4 Dietary recommendation plan generation: (1) Draft generation: Match according to the risk assessment results in the recommendation database to obtain a list of food information that the user should and should not eat, and generate a draft of the dietary recommendation; (2) Final draft generation: Extract the user's dietary taboos and dietary preference information from the collected information, combine the information in the draft of the dietary recommendation, perform food type matching in the recipe database, and obtain relevant food recipes. Combine the personal food intake dictionary, determine the food portion, and generate the final draft of the dietary recommendation; S5 Expert review: For those with a high risk in the short-term risk assessment, the dietary recommendation plan is pushed online to a dietitian for secondary review; S6 Report generation and push: Push the generated final draft of the dietary recommendation to the user in the form of pictures and texts.

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