CN117095792A - Healthy diet recipe recommendation method and system - Google Patents
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- CN117095792A CN117095792A CN202310975201.2A CN202310975201A CN117095792A CN 117095792 A CN117095792 A CN 117095792A CN 202310975201 A CN202310975201 A CN 202310975201A CN 117095792 A CN117095792 A CN 117095792A
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Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention provides a healthy diet recipe recommendation method and a healthy diet recipe recommendation system, wherein a dividing module is used for acquiring foods edible by eaters and dividing the foods into a plurality of food databases according to different food types; the calculation module is used for calculating the necessary nutrient content of each food in the food database; the initial recipe acquisition module is used for acquiring physical state data of an eater, selecting corresponding food from the food database according to the physical state data and the necessary nutrient content, and acquiring an initial recipe; the recommended recipe acquisition module is used for acquiring the taste change requirement of the eater, selecting food from the initial recipe according to the taste change requirement, and acquiring a recommended recipe, wherein the acquired recommended recipe contains necessary nutrient content calculated according to the physical state data of the eater, so that necessary nutrition can be provided for the eater, and meanwhile, the food in the recommended recipe is also obtained according to the taste change requirement of the eater, so that the actual diet requirement of the eater in a near period of time can be met.
Description
Technical Field
The invention relates to the technical field of diet recommendation, in particular to a healthy diet recipe recommendation method and system.
Background
The food is a main way for people to acquire daily nutrition and required energy, along with the continuous improvement of living standard, the demands of people on the food are not limited to the problem of temperature saturation, but more consideration is given to the health of the food and the reasonable collocation of various elements, however, non-professional staff does not know how to mix trace elements, dietary fibers, proteins, fat, carbon water and the like and how to mix the ingredients contained in each food to eat, so that a healthy diet recipe cannot be acquired, so that the industry of a dietician is born, the dietician can recommend a set of healthy diet recipe which is most suitable for the eater after the comprehensive understanding of the dietician on the basis of the body quality and the eating requirement of the eater, the eater only needs to select the corresponding food to eat according to the healthy diet recipe, however, the dietician can still have certain errors only according to own experience, the body condition and the diet requirement of the eater can possibly change in a short time, and the dietician improper eater can not adapt to the dietician on the basis of the dietician when the change of the dietician is not found in time.
Disclosure of Invention
In view of the above, the invention provides a healthy diet recipe recommendation method and system, which can recommend recipes according to the physical state data and taste change requirements of the eaters, and ensure that the recommended recipes meet the requirements of the eaters.
The technical scheme of the invention is realized as follows:
a healthy diet recipe recommendation method comprising the steps of:
step S1, obtaining edible food of a user, and dividing the edible food into a plurality of food databases according to different food types;
s2, calculating the necessary nutrient content of each item of food in the food database;
step S3, acquiring physical state data of an eater, and selecting corresponding food from a food database according to the physical state data and the necessary nutrient content to acquire an initial recipe;
and S4, obtaining taste change requirements of the eaters, screening food from the initial recipes according to the taste change requirements, and obtaining recommended recipes.
Preferably, the specific steps of the step S1 are as follows:
step S11, obtaining all edible food overall, and removing food which is contained in the food overall and cannot be eaten by eaters;
step S12, dividing the whole food removed into a plurality of food databases according to different food types, wherein the food types comprise grains, potatoes, animal foods, vegetables and fruits, bean products and pure energy foods.
Preferably, in the step S11, the specific steps of removing the food which is included in the food and cannot be eaten by the user are as follows:
step S111, inquiring direct allergic foods input by the eater and direct contradicting foods;
step S112, acquiring hospital medical records and take-out detailed sheets of eaters, and extracting potential allergic foods and potential conflict foods of the eaters from the hospital medical records and the take-out detailed sheets;
step S113, the direct allergic food, the direct contradicting food, the potential allergic food and the potential contradicting food are removed from the food population.
Preferably, the essential nutrients in step S2 include proteins, carbohydrates, fibers and fats.
Preferably, the specific steps of the step S3 are as follows:
step S31, acquiring physical examination data of an eater, wherein the physical examination data comprise height, weight, body fat rate, blood sugar, blood fat and blood pressure;
step S32, inputting physical examination data into a trained neural network, and processing the physical examination data by the neural network to obtain nutrient demand of a user;
step S33, selecting a plurality of food combinations from the food database according to the nutrient demand and the necessary nutrient content of each food, and outputting the food combinations as an initial recipe.
Preferably, the step S31 is to obtain physical examination data of the user for a period of time, and to determine whether the user is in a weight-reducing or body-building state, and when the user is in a weight-reducing or body-building state, adjust the neural network parameters of the step S32, so that the nutrient requirement of the user obtained by the neural network processing is reduced.
Preferably, the specific steps of the step S4 are as follows:
step S41, obtaining takeaway order information of an eater in a period of time recently, and extracting diet preference and food types in the period of time recently according to the takeaway order information;
step S42, searching for food combinations containing the same or similar food types as those of the last time from the initial recipe according to the diet preference;
step S43, outputting the searched food combination as a recommended recipe.
Preferably, step S42 judges whether there is a food in the food combination after the food combination is found, and eliminates the food combination containing the food in the food combination.
A healthy diet recipe recommendation system comprising:
the dividing module is used for acquiring food edible by eaters and dividing the food into a plurality of food databases according to different food types;
a calculation module for calculating the necessary nutrient content of each food in the food database;
the initial recipe acquisition module is used for acquiring physical state data of an eater, selecting corresponding foods from the food database according to the physical state data and the necessary nutrient content, and acquiring an initial recipe;
the recommended recipe acquisition module is used for acquiring the taste change requirement of the eater, screening food from the initial recipes according to the taste change requirement, and acquiring the recommended recipes.
Preferably, the dividing module comprises a rejecting module, and the rejecting module is used for rejecting food which cannot be used by an eater.
Compared with the prior art, the invention has the beneficial effects that:
before recommending the recipes, firstly acquiring edible foods of the eaters, dividing a plurality of databases according to different food types, calculating necessary nutrient content of each food in the databases, then obtaining an initial recipe according to physical state data of the eaters and the necessary nutrient content, providing necessary nutrients for the eaters by combining the food combinations in the initial recipe, and then selecting the food combinations meeting the taste of the eaters from the initial recipe based on the taste change requirements of the eaters, so as to obtain recommended recipes, wherein the finally obtained recommended recipes not only can provide necessary nutrients for the eaters, but also can meet the taste requirements of the eaters themselves, and can be more suitable for the requirements of the eaters.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only preferred embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a healthy diet recipe recommendation method of the present invention;
FIG. 2 is a flowchart of step S1 of a healthy diet recipe recommendation method of the present invention;
FIG. 3 is a flowchart of the healthy diet recipe recommendation method of the present invention, wherein inedible foods are removed in step S11;
FIG. 4 is a flowchart of step S3 of a healthy diet recipe recommendation method of the present invention;
FIG. 5 is a flowchart of step S4 of a healthy diet recipe recommendation method of the present invention;
FIG. 6 is a schematic diagram of a healthy diet recommendation system of the present invention;
in the figure, 1 is a dividing module, 2 is a calculating module, 3 is an initial recipe acquisition module, 4 is a recommended recipe acquisition module, and 5 is a rejecting module.
Detailed Description
For a better understanding of the technical content of the present invention, a specific example is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1 to 5, the method for recommending healthy diet mainly aims at recommending a set of diet suitable for physical conditions and taste requirements of an eater, and can update the diet in real time according to physical changes and taste changes of the eater in a short period of time, so as to ensure that the recommended diet keeps up with the changes of the eater, and specifically comprises the following steps:
step S1, obtaining edible food of a user, and dividing the edible food into a plurality of food databases according to different food types;
since it is necessary to recommend proper foods for the eaters, before recommending, all foods which can be eaten by the eaters need to be obtained, and then the foods are classified into a plurality of databases according to different types of foods, wherein the types of foods comprise cereals, potatoes, animal foods, vegetables and fruits, bean products and pure energy foods, when the food is recommended in the follow-up recipe, the foods in the plurality of food types can be selected for recommending, but not all foods are edible for the eaters, and due to the differences of individual tastes, favors and genes, some foods cannot be eaten by the eaters, so the foods cannot appear in the recommended recipe, and the specific steps for obtaining the foods which can be eaten by the eaters are as follows:
step S11, obtaining all edible food overall, removing food which is contained in the food overall and cannot be eaten by eaters, wherein the removing comprises the following specific steps of:
in step S111, query the direct allergic foods and direct contradicting foods inputted by the eaters, wherein the direct allergic foods and direct contradicting foods are the names of foods directly inputted by the eaters in the method and system, and can be used for direct rejection, but not all eaters can directly input, so that the allergic foods and contradicting foods of the eaters need to be potentially obtained through other ways, namely:
step S112, acquiring hospital medical records and take-out detailed sheets of eaters, and extracting potential allergic foods and potential conflict foods of the eaters from the hospital medical records and the take-out detailed sheets;
since allergic food can cause allergic symptoms on eaters and seriously endanger lives, allergic food needs to be removed from the food population, for example, part of eaters can be allergic to seafood, mango and the like, the allergic food needs to be removed from the food population in advance, contradictory food such as coriander, cress and the like can be obtained according to the preference of the eaters, and the allergic food and the contradictory food of the eaters can be potentially obtained through medical records of hospitals and takeaway detailed sheets.
Step S113, the obtained direct allergic foods, direct contradicting foods, potential allergic foods and potential contradicting foods are removed from the food population.
Step S12, dividing the removed food into a plurality of food databases according to different types of the food, wherein each food database contains a plurality of foods which can be eaten by eaters.
Step S2, calculating the necessary nutrient content of each food in the food database, wherein the necessary nutrients comprise protein, carbohydrate, fiber and fat;
in the case of a recipe recommendation for a consumer, the recommended foods need to contain the necessary energy required for the life of the human body, so after the food database is obtained, the necessary nutrient content contained in each food needs to be calculated, including protein, carbohydrate, fiber, fat and the like, wherein the protein can help the body to repair itself, the carbohydrate can provide energy for the body and the brain, the fiber can promote the peristalsis of the intestines and stomach, and the fat can increase the feeling of satiety, so that in the case of a subsequent recipe recommendation, the recommended food combination should contain a certain amount of necessary nutrients so as to provide the energy required for the daily activity for the consumer.
Since the physical condition of the user is not constant, the requirements for digestion and necessary nutrient content of the food are different with the change of the physical function and the change of various indexes of the body, and therefore, before the recommendation of the recipe is performed, the physical state data of the user needs to be evaluated, in step S3, the physical state data of the user is obtained, and the corresponding food is selected from the food database according to the physical state data and the necessary nutrient content to obtain the initial recipe, which comprises the following specific steps:
step S31, acquiring physical examination data of the eater, wherein the physical examination data comprise height, weight, body fat rate, blood sugar, blood fat and blood pressure, and the physical condition of the eater can be known according to the physical examination data, so that follow-up corresponding recommended recipes suitable for the physical condition of the eater can be avoided, and the physical function is reduced and the physical health is influenced due to overeating or improper diet of the eater with unhealthy body.
Step S32, inputting physical examination data into a trained neural network, processing the physical examination data by the neural network to obtain nutrient demand of an eater, training the neural network by collecting other human body data, outputting the physical examination data as the nutrient demand, and quickly obtaining the nutrient demand of the eater by adopting a machine learning and history data form, wherein the neural network is suitable for the demands of normal eaters, the neural network is required to be correspondingly regulated for some eaters with special demands, after the physical examination data of the eater are obtained for a period of time, the neural network can be used for judging whether the eater is in a weight-losing or body-building state, when the eater is in the weight-losing or body-building state, the nutrient demand of the eater for each food is reduced, and parameters of the neural network are required to be regulated at the moment, so that the nutrient demand of the eater obtained by the neural network is reduced, and the obtained nutrient demand can be suitable for the eaters in the weight-losing or body-building state.
In step S33, a plurality of food combinations are selected from the food database according to the nutrient requirements and the necessary nutrient content of each food, and output as an initial recipe, after the nutrient requirements of the eater are obtained, the corresponding food needs to be selected from the food database, and the sum of the necessary nutrient contents of the selected food needs to be equal to or slightly greater than the nutrient requirements of the eater, and due to the variety of the number of the foods, a plurality of food combinations also exist in the obtained initial recipe, and all the food combinations are output as the initial recipe.
The initial recipe contains a large number of food combinations which, although meeting the energy requirements of the consumer, do not necessarily meet the taste requirements of the consumer and the taste of the consumer may change over a period of time, and for this purpose, the taste change requirements of the consumer are obtained in step S4, the food is selected from the initial recipe according to the taste change requirements, and the recommended recipe is obtained by the steps of:
step S41, obtaining takeaway order information of an eater in a period of time recently, and extracting diet preference and food types in the period of time recently according to the takeaway order information;
the takeout order information includes the type of food purchased by the consumer, such as beef, pork, fish, etc., and in addition, the takeout order information may also be used to obtain the eating preference of the consumer, such as meat/eating preference/fruit preference, etc.
The final food combination is affected by different food preferences and food types in the near-term, so that step S42 searches for a food combination containing the same or similar food types as the food types in the near-term from the initial recipe according to the food preferences, and selects a corresponding food combination according to the food preferences of the eaters, and the selected food combination contains the food types eaten by the eaters in the near-term, and if the food combination does not contain the food types eaten by the eaters in the near-term, the similar food can be selected for replacement, for example, when the food eaten by the eaters in the near-term is beef, but the food combination does not contain beef, pork can be selected for replacement.
Because the food types of the food favorites and the short time are added as the restrictions, the food combinations which do not meet the requirements in the initial recipe can be screened out, and then the searched food combinations are output as the recommended recipe in step S43, so that the food combinations in the recommended recipe can meet the energy requirements of the eaters and the taste requirements of the eaters.
In addition, after the food combination is found in step S42, it is determined whether there is a combination of food with a gram, for example, a combination of bean products and milk, which may cause diarrhea, or a combination of eggs and sugar cane, which may cause stomach discomfort, in the food combination, and the combination containing the gram is removed, and the finally obtained food combination is the recommended recipe.
Referring to fig. 6, a healthy diet recipe recommendation system includes:
the dividing module 1 is used for obtaining edible foods of eaters and dividing the edible foods into a plurality of food databases according to different food types, and comprises a rejecting module which is used for rejecting foods which cannot be used by the eaters;
a calculation module 2 for calculating the necessary nutrient content of each food in the food database;
the initial recipe acquisition module 3 is used for acquiring physical state data of an eater, selecting corresponding foods from the food database according to the physical state data and the necessary nutrient content, and acquiring an initial recipe;
the recommended recipe obtaining module 4 is configured to obtain a taste change requirement of an eater, select food from the initial recipes according to the taste change requirement, and obtain a recommended recipe.
The healthy diet recipe recommendation system can be applied to diet health management of people, an eater can input favors, allergic foods, contradictory foods and the like through the recommendation system, or the dividing module 1 can automatically screen out according to online use marks of the eater, the eater can not eat foods are removed through the removing module 5, a plurality of food databases are obtained through dividing, then the calculating module 2 can calculate the necessary nutrient content of each food in the food databases, wherein the necessary nutrient contains energy necessary for human bodies such as protein, carbohydrate, fiber and fat, then the initial recipe acquisition module 3 can acquire an initial recipe according to the physical condition of the eater, the finally recommended recipe acquisition module 4 can screen the initial recipe, the screened conditions are taste requirement changes of the eater, and the finally acquired recommended recipe can not only meet the energy requirement of the eater, but also meet the taste requirement of the eater, and the recommended recipe is more humanized, thereby realizing special customization of the healthy diet.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method of recommending a healthy diet, comprising the steps of:
step S1, obtaining edible food of a user, and dividing the edible food into a plurality of food databases according to different food types;
s2, calculating the necessary nutrient content of each item of food in the food database;
step S3, acquiring physical state data of an eater, and selecting corresponding food from a food database according to the physical state data and the necessary nutrient content to acquire an initial recipe;
and S4, obtaining taste change requirements of the eaters, screening food from the initial recipes according to the taste change requirements, and obtaining recommended recipes.
2. The healthy diet recommended method according to claim 1, wherein the specific steps of step S1 are:
step S11, obtaining all edible food overall, and removing food which is contained in the food overall and cannot be eaten by eaters;
step S12, dividing the whole food removed into a plurality of food databases according to different food types, wherein the food types comprise grains, potatoes, animal foods, vegetables and fruits, bean products and pure energy foods.
3. The healthy diet recommended method according to claim 2, wherein the specific steps of rejecting the food which is contained in the food population and cannot be eaten by the eater in step S11 are:
step S111, inquiring direct allergic foods input by the eater and direct contradicting foods;
step S112, acquiring hospital medical records and take-out detailed sheets of eaters, and extracting potential allergic foods and potential conflict foods of the eaters from the hospital medical records and the take-out detailed sheets;
step S113, the direct allergic food, the direct contradicting food, the potential allergic food and the potential contradicting food are removed from the food population.
4. A healthy diet recommended method according to claim 1, characterized in that the essential nutrients in step S2 include proteins, carbohydrates, fibers and fats.
5. The healthy diet recommended method according to claim 1, wherein the specific steps of step S3 are:
step S31, acquiring physical examination data of an eater, wherein the physical examination data comprise height, weight, body fat rate, blood sugar, blood fat and blood pressure;
step S32, inputting physical examination data into a trained neural network, and processing the physical examination data by the neural network to obtain nutrient demand of a user;
step S33, selecting a plurality of food combinations from the food database according to the nutrient demand and the necessary nutrient content of each food, and outputting the food combinations as an initial recipe.
6. The method according to claim 5, wherein the step S31 is performed to obtain physical examination data of the user for a period of time to determine whether the user is in a weight-reducing or fitness state, and the neural network parameters of the step S32 are adjusted to reduce the nutrient requirement of the user obtained by the neural network processing when the user is in the weight-reducing or fitness state.
7. The healthy diet recommended method according to claim 1, wherein the specific steps of step S4 are:
step S41, obtaining takeaway order information of an eater in a period of time recently, and extracting diet preference and food types in the period of time recently according to the takeaway order information;
step S42, searching for food combinations containing the same or similar food types as those of the last time from the initial recipe according to the diet preference;
step S43, outputting the searched food combination as a recommended recipe.
8. The method according to claim 7, wherein step S42 is performed after finding the food combination, determining whether there is a food in the food combination, and eliminating the food combination containing the food in the food combination.
9. A system according to any one of claims 1-8, characterized by comprising:
the dividing module is used for obtaining edible food of eaters and dividing the edible food into a plurality of food databases according to different food types
A calculating module for calculating the necessary nutrient content of each food in the food database
The initial recipe acquisition module is used for acquiring physical state data of an eater, selecting corresponding foods from the food database according to the physical state data and the necessary nutrient content, and acquiring an initial recipe;
the recommended recipe acquisition module is used for acquiring the taste change requirement of the eater, screening food from the initial recipes according to the taste change requirement, and acquiring the recommended recipes.
10. The healthy diet recommendation system of claim 9, wherein the partitioning module comprises a culling module for culling food that is not available to the consumer.
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