CN108877896B - Artificial intelligence generated weight management method - Google Patents
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- CN108877896B CN108877896B CN201810557505.6A CN201810557505A CN108877896B CN 108877896 B CN108877896 B CN 108877896B CN 201810557505 A CN201810557505 A CN 201810557505A CN 108877896 B CN108877896 B CN 108877896B
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Abstract
The invention discloses a method for managing weight generated by artificial intelligence. A new weight management scheme generation method is constructed, and a better calorie and nutrient threshold calculation method and a final individual threshold determination method are realized. Meanwhile, the metabolic characteristics of Chinese are fully considered, the heat is controlled, the energy supply ratios of three nutrients and the GL value of saccharides are subjected to standard control, artificial intelligence and a big data setting scheme. Meanwhile, in the storage of the database, according to the region, GL value, unit heat and nutrient data, the automatic generation of diversified recipes can be quickly completed by utilizing the classification mark, and the food and the movement which are most easily insisted or liked by the individual can be pushed by an artificial intelligence pushing algorithm.
Description
Technical Field
The invention relates to an artificial intelligence technology, in particular to an artificial intelligence generated weight management method.
Background
With the popularization of artificial intelligence and big data, various algorithms are endless, and in the aspect of weight control, plan planning is generally carried out from two aspects, namely, a music score and exercise. The generation method of the scheme also stays in the academic level, generally according to the sex, age, height and weight data of a user, 500-1000 kcal is subtracted on the basis of the recommended calorie of diet of normal people by the Chinese nutritional society, the food supply is reduced by simple calorie control and corresponding three major energy supply nutrients (carbohydrate, fat and protein) for recipe collocation, most calculation work is still mainly manual work at present, the scheme data generation is carried out in a mode of data calculation as assistance, food material combinations and weight planning are formed according to the nutritional ingredients and the meal collocation experience of food and harmful or beneficial food materials under different health conditions, a food nutritional ingredient database is pre-recorded in software, various nutritional ingredient content lists are obtained through systematic statistical analysis, and the preset nutrient reference value of the scheme is compared, through adjusting food weight value, constantly contrast, reasonable later by artifical final confirmation, generate a relatively fixed recipe. On the aspect of exercise, the exercise amount and the mode of the aerobic exercise are recommended according to the recommendation of the Chinese academy of nutrition on the exercise amount and different exercise consumptions.
In the actual operation process, the scheme generation method generally has several problems: first, the poor effectiveness, human body has threshold effect and has some interval of self-adjusting adaptability, if a certain amount of calories is subtracted according to the recommended calories, it is likely that the individual's caloric threshold is not reached, resulting in some inefficiency or inefficiency in the generated protocol. Secondly, compliance is low, since recipes and exercise are professional in calculation, complex to use, time consuming and highly dependent on the personal experience of the catering operator, resulting in poor flexibility and diversity of the regimen and difficulty for the individual to adhere to. Thirdly, the rebound rate is high, and the reaction response of human metabolism is generally not considered from the medical perspective and the change of metabolic conditions at different stages is not considered, so that the appetite cannot be controlled in the control process, and new metabolic balance of the human body cannot be formed, thereby causing the rebound.
Disclosure of Invention
The invention aims to provide an artificial intelligence generated weight management method, which is suitable for genetic and metabolic characteristics of overweight and obese people in China, obtains a specific caloric threshold of the people by analyzing and classifying big data according to specific personal conditions, and quickly obtains a plurality of food nutrient ratios capable of adjusting human metabolism by utilizing artificial intelligence, integrates regional food materials, recipes and exercise habits, and is a method for generating a weight management scheme with higher compliance.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
a method of artificial intelligence generated weight management, the method comprising the steps of:
step 1: acquiring personal basic information
Step 2: establishing database according to the data of age, weight, waist circumference, blood sugar and blood pressure of the individual
And step 3: and intelligently identifying whether the individual belongs to a certain classification or not according to the data of the individual, and if the individual belongs to the certain classification, classifying the individual into the existing classification of the crowd library. If the existing classification is not found, the individual is pushed to a professional dietician for manual classification, a machine learning technology is utilized to train the system, a new scheme is obtained, the effectiveness of the scheme is finally verified, and if the effectiveness of 300 individuals is finally over 95%, a scheme library of the classified population library is formed.
And 4, step 4: and synchronizing the individual historical data time stamp and the weight change curve to obtain the stage. Specifically, if the individual data is 0 or 1, it indicates that the first stage is just entered: a weight control period; when the individual data time is multiple and the weight change reaches 50-80% of the control target, entering a second stage: and (4) a transition period. When the individual has reached the 100% weight control target, the third phase, the balance phase, is entered.
And 5: according to the variables: weight, current stage, population classification, health condition, genetic history, and basic data (minimum weight data after 18 years of age, sex, month of birth, etc.), intelligently calculating and specifically setting individual weight control thresholds, carbohydrate control thresholds, and other nutrient allocation values. The specific method comprises the following steps:
and calculating a calorie setting interval according to the weight and the threshold value of the corresponding stage. The three energy supply nutrients are respectively regulated for the second time according to different crowds and health conditions: the metabolic diseases are matched according to the lower value of the interval, wherein the hypoglycemia disease and the liver disease are matched according to the upper value of the interval. The lowest body weight after 18 years of age is compared to the basal metabolic reference table according to the age, sex and weight of the individual to generate a ratio, and the nutrient threshold is set for the third time as the overall calorie to form the final threshold setting.
Step 6: according to the crowd classification and the stage, the threshold value calls a corresponding scheme class library in the scheme library, the scheme class library is numerically associated with the recipe, the exercise and the menu, and finally a diversified weight management scheme is output.
Calculating the calorie, three nutrients and rich nutrients of the recipe in unit amount, and forming a basic recipe library according to the regional and taste preference.
Forming a sports library with units exchanging heat, and classifying according to aerobic sports, anaerobic sports and strength training.
And (3) carrying out artificial intelligence automatic judgment on each value through a threshold system, inputting a menu combination motion combination, outputting a threshold reference contrast value, and judging that the scheme is successful if the error between the threshold reference contrast value and the threshold is not more than 10%.
According to the crowd characteristics, region and taste preference, 1-3 combinations are output in each request, and finally a scheme with high compliance is optimized to serve as a high-weight pushing scheme according to the adoption rate.
Further, the weight control period comprises: generating a control period scheme according to a set threshold, wherein the threshold setting method of the weight control period comprises the following steps:
step 1: adjusting diet calorie, according to the difference of initial body weight and the degree of overweight and obesity, arranging the total calorie of diet per day (not more than 1500 kcal/day at most) at the ratio of 15-20 kcal/kg body weight, and setting the total calorie of diet per day at 3300-6270 kcal (800-.
Step 2: adjusting the dietary structure: the daily diet contains carbohydrate not less than 25% of total calories, protein not less than 25% and fat not less than 25%. The dietary supplement with the added vitamins and minerals meets the requirement of ingesting enough nutrients required by human bodies daily by the Chinese society of nutrition, including vitamins, minerals and dietary fibers, according to the recommended standard of Chinese resident dietary guidelines.
And step 3: regulating and controlling blood sugar load: the total dietary blood sugar load is 50 or less per day, wherein breakfast is 3-15, lunch is 5-30, dinner is 5-15, snack is taken once per day, and food with blood sugar load not higher than 5 is selected before lunch or dinner.
The first stage period is 50% -80% of the total weight loss target from the beginning to the completion, and the time length is 21 days to 84 days.
Further, the transition period includes: generating a transition period scheme according to a set threshold, wherein the method for setting the threshold of the transition period comprises the following steps:
step 1: daily dietary calorie: daily settings were: 4200-6270 kilojoules, and the total calories of the diet per kilogram at a rate of 15-25 kcal/kg, depending on the weight loss and physiological condition of the first stage.
Step 2: diet structure: wherein the carbohydrate content is not less than 30% of the total calories, the protein content is not less than 20%, and the fat content is not less than 25%. The dietary supplement with the added vitamins and minerals meets the requirement of ingesting enough nutrients required by human bodies daily by the Chinese society of nutrition, including vitamins, minerals and dietary fibers, according to the recommended standard of Chinese resident dietary guidelines.
And step 3: regulating and controlling blood sugar load: the total daily blood sugar load of the diet is controlled to be within 80, wherein the breakfast is 2-20, the lunch is 10-40, and the dinner is 5-30. The daily snack is arranged before lunch or dinner, and is prepared by selecting food with blood sugar load not higher than 10. In the second stage, along with the reduction of the body weight, the non-fat tissues of the human body are reduced to a certain degree, the resting metabolism is also reduced synchronously, after the body weight is reduced to a certain degree, the fat reduction speed is reduced and even stagnated, the bottleneck problem of weight reduction is solved, in order to avoid or reduce the problem, the second stage is started, the body activity is encouraged to be increased, the exercise such as aerobic exercise or anti-block exercise is increased, the body activity which consumes 90 kcal of calories is replaced by the food variety which increases the dietary calories by 90 kcal, the food variety with the blood sugar load not more than 6 is selected, wherein the daily recommended increased exercise amount is that the calorie consumption of 90-540 kcal of the exercise is increased, and the food variety with the same calories and the blood sugar load not more than 30 is increased, so that the effects of fat reduction efficiency and compliance are maintained.
The second stage duration is 50% -80% of the total weight loss goal from start to finish. The time length is 21-63 days.
Further, the equilibration period comprises: generating a balance period scheme according to a set threshold, wherein the balance period threshold setting method comprises the following steps:
after the goal of reducing fat and losing weight is completed by the lifestyle intervention method, the lifestyle intervention method for preventing weight rebound and establishing a healthy lifestyle comprises the following steps:
step 1: daily dietary calorie: 1500-.
Step 2: diet structure: carbohydrate: protein: the caloric ratio of fat was: 45-60%: 15-25%: 20-30%, and the dietary supplement can be used for supplementing vitamins, minerals and the like according to the recommended standard of Chinese society for nutrition, namely dietary guidelines of Chinese residents, so that the dietary supplement can achieve the purpose of taking sufficient daily nutrients required by human bodies, including vitamins, minerals and dietary fibers.
And step 3: regulating and controlling blood sugar load: the total daily dietary blood glucose load is controlled within 150, wherein breakfast is no more than 40, lunch is no more than 60, and dinner is no more than 40. The daily snack is arranged before lunch or dinner, and is prepared by selecting food with blood sugar load not higher than 10. The third phase encourages increased physical activity, increased exercise such as aerobic exercise or resistance exercise, to replace the food items that increase dietary calories by 90 kcal and have a glycemic load of no more than 10 for every 90 kcal of physical activity consumed. Wherein the increased exercise amount is recommended to be calorie consumption of 90-540 kcal every day, and food varieties with the same calorie and the same blood sugar load not more than 50 are added at the same time, so as to achieve the purposes of keeping weight, balancing diet and forming a healthy life style.
The period of the third stage is 21 days to 84 days.
The artificial intelligence generated weight management method provided by the embodiment of the invention has the following beneficial effects:
firstly, the method comprises the following steps: the weight reduction efficiency is remarkably improved. It regulates the glycolipid metabolism, biochemical metabolism and neuro-hormonal changes of human body, maintains or improves resting metabolic rate, protects lean tissues and improves the efficiency of losing weight and reducing fat by 1.5-2 times under the condition of the same calorie with red characters by intervening and planning GL (blood glucose load) and calorie of diet every day and fully utilizing the influence of insulin on energy and appetite.
Secondly, the method comprises the following steps: the compliance is better, the low GL is utilized to regulate the blood sugar fluctuation range between two meals, regulate the appetite and help the secretion of satiety hormone such as GLP-1, the dietary structure of the low GL enables the satiety to be enhanced by about 80%, the digestion and absorption are slow, the gastric emptying time is increased by about 30%, the hunger sensation and the deprivation sensation are greatly reduced, and meanwhile, the low GL dietary structure is designed according to the diet and living habits in various regions in China, the palatability is strong, Chinese people are easy to insist, and the implementation is simple and convenient.
Thirdly, the method comprises the following steps: increasing daily average consumption of carbohydrate, preventing ketogenesis reaction, inapplicability, high intake of saturated fat and the like which are possibly caused by low-carbon diet, and preventing side effects caused by the ketogenesis reaction.
Fourthly: the low GL dietary structure has more phytochemicals, high-quality protein, dietary fiber, a more reasonable fatty acid structure and the like, improves the dietary structure of overweight and obese people, and is based on regulating and controlling insulin and blood sugar, reducing C peptide secretion, improving insulin sensitivity, reducing fasting insulin secretion level, effectively improving insulin resistance which is a core factor and basic disorder of metabolic diseases and simultaneously improving fatty acid metabolism, so that the low GL dietary structure has obvious improvement effect on chronic metabolic diseases such as hyperinsulinemia, CVD, hypertension, dyslipidemia, hyperglycemia, diabetes, fatty liver, oxidative stress, chronic inflammation, PCOS and the like.
Fifth, the method comprises the following steps: reduce the weight set point and reduce the weight rebound from two aspects of human physiology and behavior. GL regulates and controls the sensitive degree and secretion state of the key hormone insulin that improves the metabolism of three major substances of human body effectively, keeps the basic stability of blood sugar and insulin, combines the diet caloric gibbosity effect and the exercise caloric consumption effect, can start the self-burning consumption of redundant fat in vivo, especially abdominal fat (the oxidation speed is more than 2 times faster than subcutaneous fat) from the physiological and behavioral aspects simultaneously and efficiently, improves the problems of chronic inflammation, insulin resistance and leptin resistance rapidly, and enables the oxidation of the fat of human body and neuroendocrine and metabolic reaction to enter a virtuous circle. Generally, the energy metabolism, glycolipid metabolism, nerve-endocrine regulation and the like of a human body tend to be stable or normal by using the body weight management method for 84 days, and meanwhile, the living modes of the human body such as appetite fluctuation, dietary structure, movement, cognition and the like return to a healthy state and a state beneficial to weight maintenance, so that a new weight regulation point is formed, the weight and the body shape can be easily maintained after the weight loss succeeds, and the weight rebound is effectively controlled. The body weight management method has higher efficiency and better compliance, and belongs to the leading level in China and abroad.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart illustrating a method for generating weight management by artificial intelligence according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Detailed description of the preferred embodiment
A method of artificial intelligence generated weight management, the method comprising the steps of:
step 1: acquiring personal basic information
Step 2: establishing a database according to the data of the age, the weight, the waist circumference, the blood sugar and the blood pressure of the individual;
and step 3: and intelligently identifying whether the individual belongs to a certain classification or not according to the data of the individual, and if the individual belongs to the certain classification, classifying the individual into the existing classification of the crowd library. If the existing classification is not found, the individual is pushed to a professional dietician for manual classification, a machine learning technology is utilized to train the system, a new scheme is obtained, the effectiveness of the scheme is finally verified, and if the effectiveness of 300 individuals is finally over 95%, a scheme library of the classified population library is formed.
And 4, step 4: and synchronizing the individual historical data time stamp and the weight change curve to obtain the stage. Specifically, if the individual data is 0 or 1, it indicates that the first stage is just entered: a weight control period; when the individual data time is multiple and the weight change reaches 50-80% of the control target, entering a second stage: and (4) a transition period. When the individual has reached the 100% weight control target, the third phase, the balance phase, is entered.
And 5: according to the variables: weight, current stage, population classification, health condition, genetic history, and basic data (minimum weight data after 18 years of age, sex, month of birth, etc.), intelligently calculating and specifically setting individual weight control thresholds, carbohydrate control thresholds, and other nutrient allocation values. The specific method comprises the following steps:
and calculating a calorie setting interval according to the weight and the threshold value of the corresponding stage. The three energy supply nutrients are respectively regulated for the second time according to different crowds and health conditions: the metabolic diseases are matched according to the lower value of the interval, wherein the hypoglycemia disease and the liver disease are matched according to the upper value of the interval. The lowest body weight after 18 years of age is compared to the basal metabolic reference table according to the age, sex and weight of the individual to generate a ratio, and the nutrient threshold is set for the third time as the overall calorie to form the final threshold setting.
Step 6: according to the crowd classification and the stage, the threshold value calls a corresponding scheme class library in the scheme library, the scheme class library is numerically associated with the recipe, the exercise and the menu, and finally a diversified weight management scheme is output.
Calculating the calorie, three nutrients and rich nutrients of the recipe in unit amount, and forming a basic recipe library according to the regional and taste preference.
Forming a sports library with units exchanging heat, and classifying according to aerobic sports, anaerobic sports and strength training.
And (3) carrying out artificial intelligence automatic judgment on each value through a threshold system, inputting a menu combination motion combination, outputting a threshold reference contrast value, and judging that the scheme is successful if the error between the threshold reference contrast value and the threshold is not more than 10%.
According to the crowd characteristics, region and taste preference, 1-3 combinations are output in each request, and finally a scheme with high compliance is optimized to serve as a high-weight pushing scheme according to the adoption rate.
Detailed description of the invention
A method of artificial intelligence generated weight management, the method comprising the steps of:
step 1: acquiring personal basic information
Step 2: establishing a database according to the data of the age, the weight, the waist circumference, the blood sugar and the blood pressure of the individual;
and step 3: and intelligently identifying whether the individual belongs to a certain classification or not according to the data of the individual, and if the individual belongs to the certain classification, classifying the individual into the existing classification of the crowd library. If the existing classification is not found, the individual is pushed to a professional dietician for manual classification, a machine learning technology is utilized to train the system, a new scheme is obtained, the effectiveness of the scheme is finally verified, and if the effectiveness of 300 individuals is finally over 95%, a scheme library of the classified population library is formed.
And 4, step 4: and synchronizing the individual historical data time stamp and the weight change curve to obtain the stage. Specifically, if the individual data is 0 or 1, it indicates that the first stage is just entered: a weight control period; when the individual data time is multiple and the weight change reaches 50-80% of the control target, entering a second stage: and (4) a transition period. When the individual has reached the 100% weight control target, the third phase, the balance phase, is entered.
And 5: according to the variables: weight, current stage, population classification, health condition, genetic history, and basic data (minimum weight data after 18 years of age, sex, month of birth, etc.), intelligently calculating and specifically setting individual weight control thresholds, carbohydrate control thresholds, and other nutrient allocation values. The specific method comprises the following steps:
and calculating a calorie setting interval according to the weight and the threshold value of the corresponding stage. The three energy supply nutrients are respectively regulated for the second time according to different crowds and health conditions: the metabolic diseases are matched according to the lower value of the interval, wherein the hypoglycemia disease and the liver disease are matched according to the upper value of the interval. The lowest body weight after 18 years of age is compared to the basal metabolic reference table according to the age, sex and weight of the individual to generate a ratio, and the nutrient threshold is set for the third time as the overall calorie to form the final threshold setting.
Step 6: according to the crowd classification and the stage, the threshold value calls a corresponding scheme class library in the scheme library, the scheme class library is numerically associated with the recipe, the exercise and the menu, and finally a diversified weight management scheme is output.
Calculating the calorie, three nutrients and rich nutrients of the recipe in unit amount, and forming a basic recipe library according to the regional and taste preference.
Forming a sports library with units exchanging heat, and classifying according to aerobic sports, anaerobic sports and strength training.
And (3) carrying out artificial intelligence automatic judgment on each value through a threshold system, inputting a menu combination motion combination, outputting a threshold reference contrast value, and judging that the scheme is successful if the error between the threshold reference contrast value and the threshold is not more than 10%.
According to the crowd characteristics, region and taste preference, 1-3 combinations are output in each request, and finally a scheme with high compliance is optimized to serve as a high-weight pushing scheme according to the adoption rate.
Further, the weight control period comprises: generating a control period scheme according to a set threshold, wherein the threshold setting method of the weight control period comprises the following steps:
step 1: adjusting diet calorie, according to the difference of initial body weight and the degree of overweight and obesity, arranging the total calorie of diet per day (not more than 1500 kcal/day at most) at the ratio of 15-20 kcal/kg body weight, and setting the total calorie of diet per day at 3300-6270 kcal (800-.
Step 2: adjusting the dietary structure: the daily diet contains carbohydrate not less than 25% of total calories, protein not less than 25% and fat not less than 25%. The dietary supplement with the added vitamins and minerals meets the requirement of ingesting enough nutrients required by human bodies daily by the Chinese society of nutrition, including vitamins, minerals and dietary fibers, according to the recommended standard of Chinese resident dietary guidelines.
And step 3: regulating and controlling blood sugar load: the total dietary blood sugar load is 50 or less per day, wherein breakfast is 3-15, lunch is 5-30, dinner is 5-15, snack is taken once per day, and food with blood sugar load not higher than 5 is selected before lunch or dinner.
The first stage period is 50% -80% of the total weight loss target from the beginning to the completion, and the time length is 21 days to 84 days.
Further, the transition period includes: generating a transition period scheme according to a set threshold, wherein the method for setting the threshold of the transition period comprises the following steps:
step 1: daily dietary calorie: daily settings were: 4200-6270 kilojoules, and the total calories of the diet per kilogram at a rate of 15-25 kcal/kg, depending on the weight loss and physiological condition of the first stage.
Step 2: diet structure: wherein the carbohydrate content is not less than 30% of the total calories, the protein content is not less than 20%, and the fat content is not less than 25%. The dietary supplement with the added vitamins and minerals meets the requirement of ingesting enough nutrients required by human bodies daily by the Chinese society of nutrition, including vitamins, minerals and dietary fibers, according to the recommended standard of Chinese resident dietary guidelines.
And step 3: regulating and controlling blood sugar load: the total daily blood sugar load of the diet is controlled within 80, wherein breakfast is 2-20, lunch is 10-40, and dinner is 5-30. The daily snack is arranged before lunch or dinner, and is prepared by selecting food with blood sugar load not higher than 10. In the second stage, along with the reduction of the body weight, the non-fat tissues of the human body are reduced to a certain degree, the resting metabolism is also reduced synchronously, after the body weight is reduced to a certain degree, the fat reduction speed is reduced and even stagnated, the bottleneck problem of weight reduction is solved, in order to avoid or reduce the problem, the second stage is started, the body activity is encouraged to be increased, the exercise such as aerobic exercise or anti-block exercise is increased, the body activity which consumes 90 kcal of calories is replaced by the food variety which increases the dietary calories by 90 kcal, the food variety with the blood sugar load not more than 6 is selected, wherein the daily recommended increased exercise amount is that the calorie consumption of 90-540 kcal of the exercise is increased, and the food variety with the same calories and the blood sugar load not more than 30 is increased, so that the effects of fat reduction efficiency and compliance are maintained.
The second stage duration is 50% -80% of the total weight loss goal from start to finish. The time length is 21-63 days.
Further, the equilibration period comprises: generating a balance period scheme according to a set threshold, wherein the balance period threshold setting method comprises the following steps:
after the goal of reducing fat and losing weight is completed by the lifestyle intervention method, the lifestyle intervention method for preventing weight rebound and establishing a healthy lifestyle comprises the following steps:
step 1: daily dietary calorie: 1500-.
Step 2: diet structure: carbohydrate: protein: the caloric ratio of fat was: 45-60%: 15-25%: 20-30%, and the dietary supplement can be used for supplementing vitamins, minerals and the like according to the recommended standard of Chinese society for nutrition, namely dietary guidelines of Chinese residents, so that the dietary supplement can achieve the purpose of taking sufficient daily nutrients required by human bodies, including vitamins, minerals and dietary fibers.
And step 3: regulating and controlling blood sugar load: the total daily dietary blood glucose load is controlled within 150, wherein breakfast is no more than 40, lunch is no more than 60, and dinner is no more than 40. The daily snack is arranged before lunch or dinner, and is prepared by selecting food with blood sugar load not higher than 10. The third phase encourages increased physical activity, increased exercise such as aerobic exercise or resistance exercise, to replace the food items that increase dietary calories by 90 kcal and have a glycemic load of no more than 10 for every 90 kcal of physical activity consumed. Wherein the increased exercise amount is recommended to be calorie consumption of 90-540 kilocalories every day, and food varieties with the same calorie and the blood sugar load not more than 50 are added at the same time, so as to achieve the purposes of keeping weight, balancing diet and forming a healthy life style;
the period of the third stage is 21 days to 84 days.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a unit, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional unit in the embodiments of the present invention may be integrated together to form an independent part, or each unit may exist separately, or two or more units may be integrated to form an independent part.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-only memory (ROM, Read-Onl8 memory 8), a Random Access memory (RAM, Random Access memory 8), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Claims (4)
1. An artificial intelligence generated weight management method, characterized in that the method comprises the following steps:
step 1: acquiring personal basic information;
step 2: establishing a database according to the data of the age, the weight, the waist circumference, the blood sugar and the blood pressure of the individual;
and step 3: intelligently identifying whether the individual belongs to a certain class or not according to the data of the individual, if the individual belongs to the certain class, classifying the individual into the existing class of a crowd base, if the existing class is not found, pushing the individual to a professional dietician for manual classification, training a system by using a machine learning technology to obtain a new scheme, and finally verifying the validity of the scheme, if the validity of 300 individuals exceeding 95% is finally achieved, forming the scheme base of the classified crowd base;
and 4, step 4: synchronizing individual historical data time stamps and weight change curves to obtain the stage; specifically, if the individual data is 0 or 1, it indicates that the first stage is just entered: a weight control period; when the individual data time is multiple and the weight change reaches 50-80% of the control target, entering a second stage: a transition period; entering a third stage, a balance period, when the individual has reached the weight control target of 100%;
and 5: according to the variables: the weight, the current stage, the crowd classification, the health condition, the genetic medical history and the basic data, wherein the basic data comprises the data of gender, the birth year and the lowest weight after 18 years old, the weight control threshold, the carbohydrate control threshold and the nutrient allocation value of an individual are intelligently calculated and specifically set, and the specific method comprises the following steps:
calculating a calorie setting interval according to the weight and the threshold value of the corresponding stage; the three energy supply nutrients are respectively regulated for the second time according to different crowds and health conditions: the metabolic diseases are matched according to the lower value of the interval, wherein hypoglycemia and liver diseases are matched according to the upper value of the interval; comparing the lowest body weight reference standard body weight table with the basal metabolism reference table according to the age and sex of the individual after 18 years to generate a ratio which is used as the integral calorie and the nutrient threshold value for the third time to form the final threshold value setting;
step 6: according to the crowd classification and the stage, a threshold value is used for calling a corresponding scheme class library in a scheme library, the scheme class library is numerically associated with a recipe, a sport recipe and a menu, and finally a diversified weight management scheme is output;
1. calculating the calorie, three nutrients, rich nutrients and GL value database of the menu in unit amount, and forming a basic menu database according to the region and taste preference,
2. forming a sports library for exchanging heat by units, and classifying according to aerobic sports, anaerobic sports and strength training,
3. through a threshold system, carrying out artificial intelligence to automatically judge each value, inputting a menu combination motion combination, outputting a threshold reference contrast value, judging that the scheme is successful if the error between the threshold reference contrast value and the threshold is not more than 10 percent,
4. according to the crowd characteristics, region and taste preference, 1-3 combinations are output in each request, and finally a scheme with high compliance is optimized to serve as a high-weight pushing scheme according to the adoption rate.
2. The artificial intelligence generated weight management method of claim 1, wherein the weight control period comprises: generating a control period scheme according to a set threshold, wherein the threshold setting method of the weight control period comprises the following steps:
step 1: adjusting diet calorie, wherein the total diet calorie is arranged according to the proportion of 15-20 kcal/kg per kg body weight according to the difference of initial body weight and the degree of overweight and obesity, the maximum total diet calorie is not more than 1500 kcal/day, and the total diet calorie per day is set to be 3300-6270 kJ;
step 2: adjusting the dietary structure: the daily diet contains carbohydrate not less than 25% of total calories, protein not less than 25% and fat not less than 25%, and can be supplemented with vitamins and minerals according to the recommended standard of Chinese society of Nutrition "dietary guide of Chinese residents" to achieve the effect of ingesting sufficient daily nutrients required by human body, including vitamins, minerals and dietary fiber;
and step 3: regulating and controlling blood sugar load: the total dietary blood sugar load is 50 or less per day, wherein breakfast is 3-15, lunch is 5-30, dinner is 5-15, snack is once per day, and food with blood sugar load not higher than 5 is selected before lunch or dinner;
the first stage period is 50% -80% of the total weight loss target from the beginning to the completion, and the time length is 21 days to 84 days.
3. The artificial intelligence generated weight management method of claim 2, wherein the transition period comprises: generating a transition period scheme according to a set threshold, wherein the method for setting the threshold of the transition period comprises the following steps:
step 1: daily dietary calorie: daily settings were: 4200-6270 kilojoules, and the total calories of the diet per kilogram at a rate of 15-25 kcal/kg, based on the weight loss and physiological conditions of the first stage;
step 2: diet structure: wherein the carbohydrate is not less than 30 percent of the total calorie, the protein is not less than 20 percent, the fat is not less than 25 percent, and the dietary supplement and the supplement with vitamins and minerals are added to achieve the goal of taking sufficient nutrients required by human bodies daily according to the recommended standard of Chinese society of Nutrition "dietary guidelines of Chinese residents";
and step 3: regulating and controlling blood sugar load: the total daily blood sugar load of the diet is controlled to be within 80, wherein the breakfast is 2-20, the lunch is 10-40, and the dinner is 5-30; the snack is arranged before lunch or dinner once a day, selecting food with blood sugar load not higher than 10, from the second stage, encouraging to increase body activity, increasing exercise such as aerobic exercise or anti-blocking exercise, replacing the body activity which consumes 90 kcal of heat with food with the amount which increases 90 kcal of heat and selecting food with blood sugar load not more than 6, wherein the food with 90-540 kcal of increased exercise is recommended every day, and the same calorie is increased and the food with blood sugar load not more than 30 is recommended; the second stage period is 50% -80% of the total weight loss target from the beginning to the completion, and the time length is 21-63 days.
4. The artificial intelligence generated weight management method of claim 3, wherein the balance period comprises: generating a balance period scheme according to a set threshold, wherein the balance period threshold setting method comprises the following steps:
after the goal of reducing fat and losing weight is completed by the lifestyle intervention method, the lifestyle intervention method for preventing weight rebound and establishing a healthy lifestyle comprises the following steps:
step 1: daily dietary calorie: 1500-;
step 2: diet structure: carbohydrate: protein: the caloric ratio of fat was: 45-60%: 15-25%: 20-30%, and the dietary supplement can be used for supplementing vitamins and minerals according to the recommended standard of Chinese society for nutrition, such that sufficient daily nutrients required by human body include vitamins, minerals and dietary fibers;
and step 3: regulating and controlling blood sugar load: controlling the total daily dietary blood glucose load within 150, wherein breakfast is no more than 40, lunch is no more than 60, dinner is no more than 40, snack once a day is arranged before lunch or dinner, food with blood glucose load no more than 10 is selected, the third stage encourages to increase physical activity, increase exercise such as aerobic exercise or resistance exercise, and replace food varieties with increased dietary calorie of 90 kcal and blood glucose load no more than 10 with physical activity consuming 90 kcal, wherein the recommended daily increased exercise amount is 90-540 kcal of calorie consumption, and food varieties with equal calorie and blood glucose load no more than 50 are added; the period of the third stage is 21 days to 84 days.
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| CN110517753A (en) * | 2019-08-30 | 2019-11-29 | 青岛海尔科技有限公司 | Recipe method of adjustment and device based on wisdom domestic operation system |
| CN110693009A (en) * | 2019-10-28 | 2020-01-17 | 中康道(北京)健康科技有限公司 | Method for intervening metabolic diseases by adopting life style |
| CN111312365A (en) * | 2019-11-15 | 2020-06-19 | 北京酷蟹科技有限公司 | Personalized nutritional diet method and system |
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