CN111564199B - Intelligent nutrition intervention method and terminal - Google Patents
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Abstract
The invention discloses an intelligent nutrition intervention method, which relates to the technical field of health management, and specifically comprises the steps of collecting basic information and diet information of a user; calculating each food dimension score of each food dimension of the user and the overall meal present condition score of the user; analyzing and processing to generate a user personalized intervention scheme presented in a daily task form; and receiving feedback of the user on the daily task completion condition, and gradually evolving and adjusting the user personalized intervention scheme. The invention also provides a terminal for realizing the method. The method provided by the invention is based on the acquisition and result feedback of basic information and diet information of a user by the existing mobile intelligent terminal, the efficiency of a nutrition intervention process is effectively improved, and a feasible gradual evolution nutrition intervention scheme is provided based on the acquired specific data information, instead of the traditional method which only provides a wide nutrition suggestion and does not relate to specific and effective landing actions, so that the specific requirements of users with different nutrition demands are fully met.
Description
Technical Field
The invention relates to the technical field of health management, in particular to an intelligent nutrition intervention method and a terminal.
Background
With the development of social economy, the living standard of people is obviously improved, the life style is also obviously changed, and various chronic non-infectious diseases closely related to the social environment and the life style of people are increased day by day and gradually become main diseases affecting the health of people.
Chronic diseases have become a major cause of global lethal disability. The chronic diseases not only seriously harm the health of people and reduce the life quality of people, but also bring huge economic loss to the whole society. By 2030, the world economy forum predicts that five chronic diseases worldwide will cause us $ 47 trillion losses to the world economy, corresponding to 4% of the total value of domestic production worldwide.
Chronic diseases have the characteristics of long course of disease, high morbidity, high disability rate and high mortality rate. The management of chronic diseases is well done, which is not only beneficial to the health of residents, but also beneficial to reducing medical expenses and reasonably utilizing sanitary resources. In practice, in order to improve the quality of life of the chronically ill population and reduce the attendant medical costs associated with the disease, it is necessary to perform the necessary nutritional interventions. However, the individual nutritional intervention requirements for patients with chronic diseases are difficult to realize at present, and on one hand, the individual requirements of wide patients are difficult to meet due to the extreme shortage of professional dieticians; on the other hand, the existing nutrition intervention modes are mostly evaluated face to face and intervention suggestions are provided, so that the efficiency is low, and the nutrition intervention effect of patients is difficult to be monitored and monitored continuously.
In the existing nutritional intervention process, a dietician obtains physical and morphological information, physiological index information, disease condition information and the like of a patient through observation and inquiry, and then analyzes according to professional knowledge and experience to provide an intervention scheme of the type and quantity information of ingestible food for the patient. With the development of electronic communication technology and network technology, the communication between people is more convenient, and the appearance of mobile intelligent terminals such as mobile phones, tablets, PADs and the like provides possibility for an efficient nutritional intervention process.
Disclosure of Invention
Aiming at the technical problems, the invention provides an intelligent nutrition intervention method, which aims to solve the problems that the prior art cannot comprehensively and autonomously evaluate the food quality of a user, cannot provide a personalized nutrition intervention scheme, lacks an evolutionarily adjustable intervention feedback mechanism and the like.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method of intelligent nutritional intervention, comprising:
acquiring basic information of a user including sex, age, height, weight, disease type and nutritional risk level, and acquiring meal information of the user including food intake type, frequency, total food intake amount and outside meal behavior in a questionnaire form;
calculating user nutrition and diet indexes according to the collected user basic information, the user diet information and a preset nutrition and diet grading rule, wherein the user nutrition and diet indexes comprise food dimension grades and overall diet current condition grades of the user;
extracting key fields from the acquired user basic information, the user meal information and the user nutrition and meal indexes obtained through calculation, and bringing the key fields into a preset data processing program for analysis and processing to generate a user personalized intervention scheme presented in a daily task form, wherein the key fields comprise disease types, nutrition risk levels and user overall meal present condition scores;
and receiving feedback of the user on the daily task completion condition, and gradually evolving and adjusting the user personalized intervention scheme.
And further, after the overall meal situation score of the user is calculated, the feedback of the overall meal situation score is presented to the user, and after the user finishes daily tasks, each food dimension score is incorporated into the overall meal situation score of the user.
Specifically, the ingested food category includes vegetables, fruits, roughage, dairy products, eggs, red meat, white meat, processed meat, fish, nuts, bean products, sugar-containing beverages, edible oils, dietary supplements, and wines.
Specifically, the nutrition and diet scoring rules are preset with diet scoring dimensions comprising fruits, vegetables, beans, whole grains, dairy products, fish and seafood, refined grains, added sugars, total protein intake, total fatty acid intake and saturated fat intake, and the food intake condition of the user is scored based on the diet scoring dimensions.
Further, the process of calculating the overall meal present score of the user is as follows:
calculating a scoring coefficient k of daily energy requirement based on the human basal metabolic rate BMR and the physical activity level coefficient PAL;
calculating daily average intake w of each food dimension of the user based on the user meal information, wherein w = Tw/k, and Tw is daily sum intake of each food dimension;
equally dividing the grading interval of the dietary grading dimension based on the nutrition and dietary grading rules, evaluating the interval of daily average intake data, and obtaining the grade s of each food dimension;
and the overall diet present condition score S of the user is the sum of the scores of all food dimensions.
Further, the process of generating the user personalized intervention scheme is as follows:
extracting key fields containing disease types and nutrition risk levels from the acquired user basic information;
calculating and extracting key fields including daily energy demand and user overall meal current condition scores based on the acquired user meal information;
and matching key fields according to the dietary behavior rules on the basis of a nutritional intervention database to generate a user personalized intervention scheme presented in a daily task form, wherein each time of matching, each task of a single food category only appears once, and the number of the tasks obtained by matching is at least 1 and at most 4.
Specifically, the preset data processing program carries out statistical analysis and arrangement on the big data of healthy people through medical and nutriologists to form a nutrition intervention database and a meal action rule,
wherein the nutritional intervention database comprises a disease type-based food intake database, a meal pattern database, a food risk rating database, and an intervention task database.
In particular, the meal action rules include:
firstly, matching a recommended food intake database according to basic information of a user mainly based on disease types to form an ideal diet scheme; secondly, matching a meal mode database according to key fields including daily energy demand and the overall meal present condition score of the user to form a practical meal scheme of the user; then, the ideal diet scheme and the actual diet scheme are subjected to cross matching, a diet task conforming to the diet habit of the user is obtained by combining a food risk level database and an intervention task database, key fields contained in the diet task relate to food types, corresponding meals and single intake, so that a task set for personalized nutrition intervention of the user is formed, and daily tasks are extracted from the task set and pushed to the user;
or, on the basis of a decision tree prediction model, representing the nutritional requirement based on the big data of the healthy population and a preset nutritional intervention suggestion scheme by using non-leaf nodes and leaf nodes respectively, wherein each non-leaf node of the decision tree represents a judgment condition of the nutritional requirement of the user, a branch of the decision tree represents an object meeting the condition of the node, the leaf node of the decision tree represents a preset nutritional intervention suggestion scheme, and an associated intervention task database of the nutritional intervention suggestion scheme is pushed to the user in a daily task form.
Further, the receiving of the user feedback on the daily task completion condition, and the gradually evolving and adjusting the user personalized intervention scheme specifically includes:
receiving photographing identification card punching feedback of a user based on a daily task, and judging whether the daily task is finished;
if the user is judged to finish the task on the current day, continuing to push the task on the second day, and if the unfinished task on the current day exists, continuing to push the task on the second day; if the same task is not completed within a certain continuous time, stopping pushing the task and triggering background reminding, and auditing and adjusting the nutrition intervention scheme by corresponding professionals;
and according to the information fed back by the user card punching, the corresponding food dimension scores are counted into the overall diet present condition score of the user and are deducted after the appointed time.
Based on the foregoing process, the present invention further provides a terminal for implementing the above method for intelligent nutritional intervention, including:
the information acquisition module is used for acquiring basic information of the user including sex, age, height, weight, disease type and nutritional risk level, and acquiring meal information of the user including food intake type, intake frequency, total food intake and outdoor dining behaviors in a built-in questionnaire mode;
the diet scoring module is used for calculating the nutrition and diet indexes of the user including the food dimension scores and the overall diet current state scores of the user according to the collected basic information of the user, the diet information of the user and a preset nutrition and diet scoring rule;
the core processing module is used for processing the acquired information, extracting key fields, bringing the key fields into a preset data processing program for analysis and processing, and generating a user personalized intervention scheme containing daily tasks;
the display module is used for presenting the intuitive user overall meal present score and daily tasks based on the user personalized intervention scheme for the user;
the input module is used for carrying out interactive operation by a user;
the storage module is used for storing related data information; and
and the photographing identification card punching module is used for performing photographing identification card punching feedback based on an image identification technology when the user completes a daily task.
Furthermore, the terminal also comprises a health habit module connected with the core processing module and used for evaluating the health habits of the user according to the acquired user basic information and the user meal information.
Compared with the prior art, the invention has the following beneficial effects:
the method provided by the invention is used for acquiring basic information and diet information of a user and feeding back results based on the existing mobile intelligent terminal, so that the efficiency of the nutritional intervention process is effectively improved, and a feasible gradual-evolution nutritional intervention scheme is provided based on the acquired specific data information, instead of the traditional method which only provides a wide nutritional suggestion and does not relate to specific and effective landing actions, so that the specific requirements of users with different nutritional requirements are fully met.
According to the method, the completion condition of the user action task is monitored and followed based on a food image recognition technology, so that the accuracy and convenience of the diet scoring are improved, interactive nutrition management of a user-dietician is realized, the feasibility of continuous adjustment of an intervention plan by the dietician is ensured, and the defect of a rough nutrition management mode is avoided.
Drawings
FIG. 1 is a block flow diagram of an embodiment of the present invention.
Fig. 2 is a block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and examples, which include, but are not limited to, the following examples.
Examples
As shown in fig. 1, the method of intelligent nutritional intervention comprises:
(S1) acquiring basic information of a user, wherein the basic information of the user comprises indexes such as gender, age, height, weight, disease type and nutritional risk level, and the basic information is acquired mainly in a user terminal registration recording mode, and the nutritional risk level is calculated according to the existing rules based on the indexes such as age, weight change condition and appetite change condition; for example, a user autonomously fills in an information form specified in an APP or application software;
acquiring user meal information, wherein the meal information is acquired by filling questionnaires such as meal habit questionnaires and meal frequency questionnaires and the like which are arranged in a user terminal by a user, and the questionnaires arranged in the user terminal are judged and verified by professionals such as doctors, dieticians and nutriologists so as to meet meal evaluation standards and information acquisition requirements; the dietary information relates to indices including food type intake, frequency of intake, total amount consumed, out-eating behavior, etc., wherein the food type includes, but is not limited to, vegetables, fruits, roughage, dairy products, eggs, red meat, white meat, processed meat, fish, nuts, soy products, sugar-containing beverages, cooking oils, dietary supplements, wines, etc.
And (S2) calculating user nutrition and diet indexes according to the collected user basic information, the user diet information and a preset nutrition and diet scoring rule, wherein the user nutrition and diet indexes comprise each food dimension score and the user overall diet present condition score. The nutrition and diet scoring rules are preset with diet scoring dimensions including fruits, vegetables, beans, whole cereals, dairy products, fish and seafood, refined cereals, added sugars, total protein intake, total fatty acid intake and saturated fat intake, and the types of food ingested by the user are scored based on the diet scoring dimensions; the nutrient intake of various food dimensions such as added sugar, total protein intake, total fatty acid intake, saturated fat intake and the like can be obtained by combining the types of ingested food in the acquired dietary information with the existing food-nutrient control data, and the dietary scoring dimension is formed together according to the classification of the food types in the acquisition indexes. The food-nutrient control data may be obtained from a food nutrient database such as the chinese food ingredient table (2018).
For example, the rules for nutrition and diet scoring for overweight and obese users may be based on the HEI-2015 (Health Eating Index-2015) diet scoring criteria as shown in Table 1 below.
TABLE 1 fat user Nutrition and diet Scoring rules (based on HEI-2015 Scoring criteria)
The overall meal present score of the user is calculated by the following process:
calculating a scoring coefficient k of the daily energy requirement EN based on the human basal metabolic rate BMR and the physical activity level coefficient PAL:
k＝EN/1000kcal，
EN＝BMR×PAL，
wherein, the BMR (Harris nodal Equation) is as follows:
female: BMR =655+ (9.6X weight in kg) + (1.8X height in cm) - (4.7X age in years)
Male: BMR =66+ (13.7 x weight in kg) + (5 x height in cm) - (6.8 x age in years);
physical activity level coefficient PAL was taken from the PAL control table of the chinese society for nutrition as follows table 2:
TABLE 2PAL comparison Table
Calculating daily average intake w of each food dimension of the user based on the user meal information, wherein Tw is daily sum intake of each food dimension, and is equal to the product of single intake of the user in the food dimension and intake frequency or the value of total intake of the user in a meal counting period divided by the meal counting period in days according to a general calculation mode;
equally dividing the scoring interval (from 0 to full-scale interval) of the dietary scoring dimensionality of each food type based on a nutrition and dietary scoring rule, evaluating the interval of daily average intake data, acquiring the score s of each food dimensionality, and generally taking 2 decimal places;
the user overall meal present condition score S is the sum of all food dimension scores, the maximum score is 100 points, the minimum score is 0 point, 2 decimal points are reserved in background calculation logic, and the score is presented to the user in an integer form through front end feedback.
(S3) extracting key fields from the acquired user basic information, the user diet information and the user nutrition and diet indexes obtained through calculation, and introducing the key fields into a preset data processing program for analysis and processing to generate a user personalized intervention scheme presented in a daily task form, wherein the key fields comprise daily energy requirement, disease type, nutrition risk level and user overall diet present condition score;
specifically, extracting key fields from the user basic information includes: the type of disease, the nutritional risk level,
calculating and extracting key fields including daily energy demand and user overall meal current condition scores based on the acquired user meal information;
and on the basis of a nutritional intervention database, matching key fields according to the dietary behavior rules to generate a user personalized intervention scheme presented in a daily task mode, wherein each task of a single food category only appears once in each matching, and the number of the tasks obtained by matching is at least 1 and at most 4.
The preset data processing program carries out statistical analysis and sorting on the big data of healthy people through medical and nutriologists to form a nutrition intervention database and a diet action rule, wherein the nutrition intervention database comprises a food intake database, a diet mode database, a food risk grade database and an intervention task database based on disease types.
The food intake database based on the disease types can be based on the grading rules of HEI-2015 grading rules, chinese dietary guidelines and the like, and the recommended intake is used as the daily recommended intake standard of various foods of the user.
The meal mode database is formed by induction and sorting according to big data of the occurrence probability of each meal based on the design of nutriologists and the food types and is used as the occurrence meal standard of the food specifically recommended by the user; for example, the aforementioned obese patient has a criterion for recommended intake of the daily diet based on the HEI-2015 scoring criteria, with the full score criterion;
taking the marine fish food shown in table 3 below as an example, by performing expert evaluation setting on the number of meals of various foods, the number of meals of recommended food can be set from high to low according to the number of meals of food in the table when the user is matched. Meanwhile, the database can train data based on an artificial intelligence technology, a supervised machine learning algorithm, a boosting algorithm and the like, and a recommended meal probability weight relation model corresponding to the state indexes such as the occupation type and the disease type of the user is constructed so as to continuously optimize the recommended meal probability weight value corresponding to each state index.
TABLE 3 evaluation table of diet pattern of fish and marine products
A food risk grade database, which is designed by nutriologists and medical experts, and divides a large number of food categories into a recommendation category (G), a general category (Y) and a contraindication category (R) according to nutritional requirements and contraindications of different disease types, so as to serve as sources of food names specifically recommended by users;
the following table 4 shows examples of grades of marine fish food for obesity, hypertension and gout, and the food types of some disease type patients are graded by expert judgment, that is, the food names are matched and recommended to the user, and then the food names are sorted according to the order of food G grade preference, food Y grade suboptimum selection and food R grade forbidden selection in the table.
TABLE 4 Risk rating assessment table for fish and marine products
An intervention task database, which integrates knowledge and experience of experts in the field of nutrition to form a large number of nutrition requirement suggestions of users according with different disease states and is embodied as daily nutrition intervention tasks;
the user nutrition intervention task can be set based on the recommended intake of various foods of the user, and meanwhile, a restriction food intake task and a special food intake task are considered to be added. Specific task configurations may include meal intake, priority food names, alternative food names, task difficulty, intake, and other indicators.
Taking the fruit food tasks shown in the following table 5 as an example, when a user performs a fruit food pushing task, the recommended task number and priority can be judged and ranked according to the daily recommended intake of the user and the single task intake and the task difficulty set manually in advance in the following table. Meanwhile, the database can train data based on an artificial intelligence technology, a supervised machine learning algorithm and the like, and a task difficulty weight relation model corresponding to the state indexes such as the occupation type, the disease type and the like of the user is constructed so as to continuously optimize the task difficulty weight value corresponding to each state index.
| Number of meals | Preferentially recommending food | Alternative recommended food | Difficulty of making | Intake amount (g) |
| Breakfast | Eat one { front } | Or { other _ fruit } and other fruits (1 apple portion) | 3 | 200 |
| Lunch | Eat one { fruit } | Or { other _ fruit } and other fruits (1 apple portion) | 3 | 200 |
| Dinner | Eat one { front } | Or { other _ fruit } and other fruits (1 apple portion) | 3 | 200 |
| Food with meal | Eat one { front } | Or { other _ fruit } and other fruits (1 apple portion) | 1 | 200 |
Table 5 fruit food pushing task evaluation table
Where { fruit } represents one fruit name and { other _ fruit } represents another fruit name.
The meal action rules may be embodied in two forms:
firstly, matching a recommended food intake database according to user basic information mainly comprising disease types to form an ideal diet scheme; secondly, matching a meal mode database according to key fields including daily energy demand and the overall meal present condition score of the user to form a practical meal scheme of the user; and finally, performing cross matching on the ideal diet scheme and the actual diet scheme, combining a food risk level database and an intervention task database, obtaining the diet tasks which accord with the diet habits of the users and contain key fields related to food types, corresponding meals, single intake and the like, forming a task set for the personalized nutritional intervention of the users, and extracting daily nutritional intervention tasks from the task set to push and present the daily nutritional intervention tasks to the users.
Secondly, on the basis of a decision tree prediction model, nutrition requirements and nutrition intervention suggestion schemes based on the big data of healthy people are represented by non-leaf nodes and leaf nodes respectively, wherein each non-leaf node of the decision tree represents a judgment condition of the nutrition requirements of a user, branches of the decision tree represent objects meeting the conditions of the nodes, the leaf nodes of the decision tree represent nutrition intervention suggestion schemes, the nutrition intervention suggestion schemes can be pre-configured on the basis of nutrition intervention rules, and an associated intervention task database of the nutrition intervention suggestion schemes is pushed and presented to the user in a daily nutrition intervention task form. For example, whether a user suffers from a disease is judged based on disease type data, if not, a first ideal diet scheme is adopted and a corresponding daily nutritional intervention task is pushed, if yes, whether complications occur is judged continuously, if not, a second ideal diet scheme is adopted and a corresponding daily nutritional intervention task is pushed, if yes, whether the user is in an acute attack stage is judged continuously, if not, a third ideal diet scheme is adopted and a corresponding daily nutritional intervention task is pushed, and if yes, a preset gradual-evolution diet scheme is adopted and a corresponding daily nutritional intervention task is pushed.
(S4) receiving feedback of a user on the daily task completion condition, gradually evolving and adjusting the user personalized intervention scheme, and specifically comprising the following steps:
receiving photographing identification card punching feedback of a user based on a daily task, and judging whether the daily task is finished;
if the user is judged to finish the task on the current day, continuing to push the task on the second day, and if the unfinished task on the current day exists, continuing to push the task on the second day; if the same task is not completed in 3 consecutive days, stopping pushing the task and triggering background reminding, and auditing and adjusting the nutrition intervention scheme by professionals such as doctors and dieticians at the monitoring terminal;
in addition, professionals such as doctors and dieticians at the monitoring terminal can perform active monitoring through the background, adjust the intervention scheme according to the periodically updated evaluation report of the user, the periodic task completion report and the like, and display the intervention scheme to the user;
and according to the information fed back by the user card punching, the corresponding food dimension scores are counted into the overall diet present condition score of the user and are deducted after 7 days.
Based on the foregoing process, the present invention further provides a terminal for implementing the above method for intelligent nutritional intervention, including:
the information acquisition module is used for acquiring basic information of the user including sex, age, height, weight, disease type and nutritional risk level, and acquiring meal information of the user including food intake type, intake frequency, total food intake and outside dining behaviors in a built-in questionnaire mode;
the meal scoring module is used for calculating user nutrition and meal indexes including food dimension scores and user overall meal current condition scores according to the collected user basic information, the user meal information and a preset nutrition and meal scoring rule;
the core processing module is used for processing the acquired information, extracting key fields, incorporating the key fields into a preset data processing program for analysis and processing, and generating a user personalized intervention scheme containing daily tasks;
the display module is used for presenting the intuitive user overall meal present score and daily tasks based on the user personalized intervention scheme for the user;
the input module is used for carrying out interactive operation by a user;
the storage module is used for storing related data information; and
and the photographing identification card punching module is used for performing photographing identification card punching feedback based on an image identification technology when the user completes a daily task.
Furthermore, the terminal also comprises a health habit module connected with the core processing module and used for evaluating the health habits of the user according to the collected user basic information and the user meal information.
The terminal can be presented in an APP or application software mode based on existing intelligent equipment such as a mobile phone, a tablet, a notebook and the like, so that a user can use the terminal conveniently.
Through the process, the method can analyze and evaluate the diet and health data of the user, calculate and adjust the diet quality current condition score matched with the demand, generate a daily intervention plan and task and food image recognition feedback intervention completion degree according with the diet habits of the user, and realize the function of dynamically adjusting the plan according to the task completion condition. Users of different nutritional needs may therefore obtain a complete and feasible set of evolutionary nutritional intervention programs based on mobile devices, rather than the traditional approach of providing only broad nutritional recommendations, without involving specific and effective touchdown actions.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but all changes that can be made by applying the principles of the present invention and performing non-inventive work on the basis of the principles shall fall within the scope of the present invention.
Claims (6)
1. A method of intelligent nutritional intervention, comprising:
collecting basic information of a user including gender, age, height, weight, disease type and nutritional risk level, and collecting diet information of the user including food intake type, intake frequency, total food intake amount and outside dining behavior in a questionnaire form;
calculating user nutrition and diet indexes according to the collected user basic information, the user diet information and a preset nutrition and diet grading rule, wherein the user nutrition and diet indexes comprise food dimension grades and overall diet current condition grades of the user; the process of calculating the overall diet present score of the user is as follows:
calculating a scoring coefficient k of daily energy requirement based on the human basal metabolic rate BMR and the physical activity level coefficient PAL;
calculating daily average intake w of each food dimension of the user based on the user meal information, wherein w = Tw/k, and Tw is the daily sum intake of each food dimension;
equally dividing the grading interval of the dietary grading dimensionality based on a nutrition and dietary grading rule, evaluating the interval of daily average intake data, and acquiring the grading s of each food dimensionality;
the user overall meal present condition score S is the sum of all food dimension scores;
extracting key fields from the acquired user basic information, the user meal information and the user nutrition and meal indexes obtained through calculation, and bringing the key fields into a preset data processing program for analysis and processing to generate a user personalized intervention scheme presented in a daily task form, wherein the key fields comprise disease types, nutrition risk levels and user overall meal present condition scores;
receiving feedback of a user on the completion condition of each day of tasks, and gradually evolving and adjusting the personalized intervention scheme of the user;
the process of generating the user personalized intervention scheme comprises the following steps:
extracting key fields containing disease types and nutrition risk levels from the acquired user basic information;
calculating and extracting key fields including daily energy demand and user overall meal current condition scores based on the acquired user meal information;
matching key fields according to the dietary behavior rules on the basis of a nutritional intervention database to generate a user personalized intervention scheme presented in a daily task form, wherein each time of matching, each task of a single food category only appears once, and the number of the tasks obtained by matching is at least 1 and at most 4;
the preset data processing program carries out statistical analysis and arrangement on the big data of healthy people through medical and nutriologists to form a nutrition intervention database and a diet action rule,
wherein the nutritional intervention database comprises a disease type-based food intake database, a meal pattern database, a food risk rating database, and an intervention task database;
the meal action rules include:
firstly, matching a recommended food intake database according to basic information of a user mainly based on disease types to form an ideal diet scheme; secondly, matching a diet mode database according to key fields including daily energy demand and the overall diet present score of the user to form a practical diet scheme of the user; then, the ideal diet scheme and the actual diet scheme are subjected to cross matching, a diet task conforming to the diet habit of the user is obtained by combining a food risk level database and an intervention task database, key fields contained in the diet task relate to food types, corresponding meals and single intake, so that a task set for personalized nutrition intervention of the user is formed, and daily tasks are extracted from the task set and pushed to the user;
or, on the basis of a decision tree prediction model, representing the nutritional requirement based on the big data of the healthy population and a preset nutritional intervention suggestion scheme by using non-leaf nodes and leaf nodes respectively, wherein each non-leaf node of the decision tree represents a judgment condition of the nutritional requirement of the user, a branch of the decision tree represents an object meeting the condition of the node, the leaf node of the decision tree represents a preset nutritional intervention suggestion scheme, and an associated intervention task database of the nutritional intervention suggestion scheme is pushed to the user in a daily task form.
2. The method of intelligent nutritional intervention of claim 1, wherein feedback is presented to the user after calculating the user's overall meal presence score and each food dimension score is incorporated into the user's overall meal presence score after the user completes a daily task.
3. The method of intelligent nutritional intervention of claim 2, wherein a meal scoring dimension comprising fruits, vegetables, beans, whole grains, dairy, seafood, refined grains, added sugars, total protein intake, total fatty acid intake, and saturated fat intake is pre-set in the nutrition and meal scoring rules, and the user's food intake status is scored based on the meal scoring dimension.
4. The method of intelligent nutritional intervention of claim 3, wherein receiving user feedback on daily task completion, wherein evolutionarily adjusting the user-customized intervention program specifically comprises:
receiving photographing identification card punching feedback of a user based on a daily task, and judging whether the daily task is finished;
if the user is judged to finish the task on the current day, continuing to push the task on the second day, and if the unfinished task on the current day exists, continuing to push the task on the second day; if the same task is not completed within a certain continuous time, stopping pushing the task and triggering background reminding, and auditing and adjusting the nutrition intervention scheme by corresponding professionals;
and according to the information fed back by the user card punching, the corresponding food dimension scores are counted into the overall diet present condition score of the user and are deducted after the appointed time.
5. A terminal for implementing the method of intelligent nutritional intervention according to any one of claims 1 to 4, comprising:
the information acquisition module is used for acquiring basic information of the user including sex, age, height, weight, disease type and nutritional risk level, and acquiring meal information of the user including food intake type, intake frequency, total food intake and outdoor dining behaviors in a built-in questionnaire mode;
the diet scoring module is used for calculating the nutrition and diet indexes of the user including the food dimension scores and the overall diet current state scores of the user according to the collected basic information of the user, the diet information of the user and a preset nutrition and diet scoring rule;
the core processing module is used for processing the acquired information, extracting key fields, incorporating the key fields into a preset data processing program for analysis and processing, and generating a user personalized intervention scheme containing daily tasks;
the display module is used for presenting intuitive user overall meal present score and daily tasks based on a user personalized intervention scheme for the user;
the input module is used for carrying out interactive operation by a user;
the storage module is used for storing related data information; and
and the photographing identification card punching module is used for performing photographing identification card punching feedback based on an image identification technology when the user completes a daily task.
6. The terminal of claim 5, further comprising a health habit module connected to the core processing module, for evaluating the health habits of the user according to the collected user basic information and the user meal information.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112071400A (en) * | 2020-09-21 | 2020-12-11 | 中山大学 | Food material quantitative selection method based on disease knowledge base |
| CN112489768A (en) * | 2020-12-28 | 2021-03-12 | 重庆市汇人健康管理有限责任公司 | Intelligent healthy diet management system for improving fatty liver disease |
| CN112837783B (en) * | 2021-01-28 | 2024-02-06 | 北京大学第一医院 | Nutritional assessment method and device for patient |
| CN113744840A (en) * | 2021-09-07 | 2021-12-03 | 朱珍妮 | System and method for realizing personalized instant nutrition evaluation and guidance based on centralized meal supply environment |
| CN114496166B (en) * | 2022-02-16 | 2022-12-20 | 上海楚动智能科技有限公司 | Tumor patient nutrition prescription system |
| WO2024060126A1 (en) * | 2022-09-22 | 2024-03-28 | Nutricia Early Life Nutrition (Shanghai) Co., Ltd. | Meal plan generating method, apparatus, and computer implemented algorithm thereof |
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Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6904408B1 (en) * | 2000-10-19 | 2005-06-07 | Mccarthy John | Bionet method, system and personalized web content manager responsive to browser viewers' psychological preferences, behavioral responses and physiological stress indicators |
| PL2327315T3 (en) * | 2009-11-29 | 2014-03-31 | Nestec Sa | Dosing protocols for increasing protein synthesis in an active individual |
| CN106485067A (en) * | 2016-09-29 | 2017-03-08 | 北京理工大学 | Individual dietary energy method computations in conjunction with BMI |
| WO2018081175A1 (en) * | 2016-10-24 | 2018-05-03 | Habit, Llc | System and method for implementing meal selection based on vitals, genotype, and phenotype |
| CN107194175A (en) * | 2017-05-24 | 2017-09-22 | 刘凤江 | One kind set interactive health management service network plateform system |
| CN107658001B (en) * | 2017-10-10 | 2020-10-13 | 一步到味(天津)科技有限公司 | Household oil health management method and system |
| CN110021403A (en) * | 2017-10-30 | 2019-07-16 | 合肥美的智能科技有限公司 | Recommend method and apparatus, household electrical appliance and the machine readable storage medium of food materials |
| CN108461124A (en) * | 2018-03-27 | 2018-08-28 | 周梦杰 | Nutrition Management method based on personalized precision and diet guide system |
| CN109841270A (en) * | 2019-02-02 | 2019-06-04 | 成都尚医信息科技有限公司 | Dietary nutrition health investigation and assessment system and its implementation based on smart machine |
| CN109817307A (en) * | 2019-02-02 | 2019-05-28 | 成都尚医信息科技有限公司 | Nutritious food order system and its implementation based on smart machine |
| CN110097946B (en) * | 2019-03-01 | 2022-06-07 | 西安电子科技大学 | Diet recommendation method based on nutrient analysis |
| CN111061943A (en) * | 2019-10-28 | 2020-04-24 | 安徽四创电子股份有限公司 | Dish recommendation system and dish recommendation method based on data mining and analysis |
| CN110797107A (en) * | 2019-10-30 | 2020-02-14 | 武汉绿安健膳方科技有限公司 | Method for evaluating nutrition of household diet |
| CN110767289A (en) * | 2019-10-30 | 2020-02-07 | 武汉绿安健膳方科技有限公司 | Internet-based household nutrition management method |
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