CN115470701A - Recipe recommendation method and system based on machine learning - Google Patents
Recipe recommendation method and system based on machine learning Download PDFInfo
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- CN115470701A CN115470701A CN202211073147.4A CN202211073147A CN115470701A CN 115470701 A CN115470701 A CN 115470701A CN 202211073147 A CN202211073147 A CN 202211073147A CN 115470701 A CN115470701 A CN 115470701A
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Abstract
The invention discloses a recipe recommendation method and system based on machine learning, relates to the technical field of recipe recommendation, and solves the technical problem that the eating behavior of a user directly influences the acquisition of nutrient substances and the health condition; the method comprises the following steps: collecting long-term diet data and behavior data of patients with 18-70 years old hypertension, hyperglycemia, hyperlipidemia, cardiovascular diseases, diabetes, obesity, gastrointestinal diseases and cancers to form a set of typical chronic disease and diet behavior relation database, analyzing the relation between the diseases and diet behaviors by adopting a deep neural network model, predicting possible pathogenic directions of the user according to the diet and behavior data of the user, and recommending a proper diet and behavior optimization scheme; the method can analyze the relationship between adult diet behavior and disease onset, and provide basis for preventing and treating chronic diseases.
Description
Technical Field
The invention belongs to the field of artificial intelligence, relates to a recipe recommendation technology, and particularly relates to a recipe recommendation method and system based on machine learning.
Background
The death rate of chronic non-infectious diseases (chronic diseases) of Chinese residents accounts for 86.6 percent of the total death cases, about 350 ten thousand people die of cardiovascular and cerebrovascular diseases every year, the prevalence rate of hypertension of people of 18 years old and above is 25.2 percent, and the prevalence rate of diabetes is 9.7 percent. Chronic diseases are closely related to smoking, lack of physical activity, unhealthy diet and harmful alcohol consumption, wherein dietary factors account for the first of all risk factors of chronic diseases. The dietary behavior directly affects nutrient acquisition and health condition, and is an important influence factor in the occurrence and development process of chronic diseases. By applying an artificial intelligence technology, the eating behavior habits of the user are analyzed, the pathogenic direction of the user is predicted, and correct improvement suggestions are given to the user, so that the user can obtain healthy eating and living habits.
Therefore, a recipe recommendation method and system based on machine learning are provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a diet recommendation method and system based on machine learning, which solve the problem that the dietary behavior of a user directly influences the acquisition of nutrient substances and the health condition.
In order to achieve the above object, embodiments according to a first aspect of the present invention provide a recipe recommendation method and system based on machine learning, including a data acquisition module, a data processing module, and an intelligent recommendation module; information interaction is carried out among all modules based on a digital signal mode;
the data acquisition module is used for acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse food grain proportion, diet taste and the usage amount of dairy products of the food; the carbohydrate content, fat content, protein content, water content, mineral content, vitamin content and dietary fiber content of the food; freshness data of the food;
the personal attributes include gender, age, region;
and sending the behavior data, the food data and the personal attributes to the data processing module;
the data processing module is used for receiving the behavior data, the food data and the personal attributes and obtaining the behavior data, the food data, the personal attributes and a prediction model of the pathogenic direction of the eating behavior; wherein the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
the intelligent recommendation module is used for recommending recipes and healthy life styles to the user by the expert system according to the predicted chronic disease direction possibly caused by the dietary life habits of the user.
Preferably, the data acquisition module comprises an intelligent wearable device, and the intelligent wearable device comprises a camera device, a voice recognition device and an odor detection device.
Preferably, the data acquisition module acquires behavior data, food data and personal attributes of the user, and the specific process includes:
a user fills in sex, age and area through an intelligent terminal;
the user wears the intelligent wearable device;
the intelligent wearable device acquires the tobacco intake, eating speed, drinking capacity, exercise time, sleeping time and personal emotion of a user;
the intelligent wearable device acquires a food image of a user;
the data acquisition module sends the behavior data, the food data and the personal attributes to the data processing module.
Preferably, the intelligent terminal comprises an intelligent mobile phone and a computer.
Preferably, the data processing module receives the behavior data, the food data and the personal attribute, and obtains the behavior data, the food data, the personal attribute and the eating behavior pathogenic direction prediction model according to a specific process including:
the data processing module receiving the personal attributes, the behavioral data, and the food analysis data;
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic direction prediction model;
selecting food analysis data, behavior data and personal attribute data of a user for a period of time to carry out pretreatment to form a test sample;
and matching the test sample with the typical chronic disease and the eating behavior relation database data to predict the direction of the chronic disease possibly caused by the eating habits of the user.
Preferably, the establishing of the diet behavior pathogenic direction prediction model based on the artificial intelligence model comprises:
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic prediction model based on a deep neural network;
the method comprises the steps of inputting food analysis data, behavior data and personal attribute data of a user obtained by a wearable data acquisition device for a period of time into a diet behavior pathogenic direction prediction model based on a deep neural network as new samples, namely predicting the direction of a chronic disease possibly caused by the diet living habits of the user.
The artificial intelligence model comprises a deep convolution neural network model or an RBF neural network model and other models with strong nonlinear fitting capability.
Preferably, the intelligent recommendation module recommends the recipe and the healthy lifestyle to the user by the expert system according to the predicted direction of the chronic disease possibly caused by the dietary life habit of the user, and the specific process includes:
the intelligent recommendation module receives the chronic disease directions, and an expert system recommends recipes and healthy life styles to the chronic disease directions.
Preferably, the data acquisition module is in communication and/or electrical connection with the data processing module;
the data processing module is in communication and/or electrical connection with the intelligent recommendation module.
A recipe recommendation method based on machine learning comprises the following steps:
the method comprises the following steps: acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse food grain proportion, diet taste and the usage amount of dairy products of the food; the carbohydrate content, fat content, protein content, water content, mineral content, vitamin content and dietary fiber content of the food; freshness data of the food;
the personal attributes include gender, age, region;
step two: receiving the behavior data, the food data and the personal attribute, and obtaining the behavior data, the food data, the personal attribute and a prediction model of the pathogenic direction of the eating behavior; the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
step three: and recommending the recipes and the healthy life style to the user by the expert system according to the predicted direction of the chronic diseases possibly caused by the dietary life habits of the user.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a recipe recommendation method and system based on machine learning, wherein the method comprises the following steps: collecting long-term diet data and behavior data of patients with 18-70 years old hypertension, hyperglycemia, hyperlipidemia, cardiovascular diseases, diabetes, obesity, gastrointestinal diseases and cancer to form a set of typical chronic disease and diet behavior relation database, analyzing the relation between the diseases and diet behaviors by adopting a deep neural network model, predicting possible pathogenic directions of the user according to the diet and behavior data of the user, and recommending a proper diet and behavior optimization scheme; the method can analyze the relationship between adult diet behavior and disease onset, and provide basis for preventing and treating chronic diseases.
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FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a recipe recommendation system based on machine learning includes a data acquisition module, a data processing module, and an intelligent recommendation module; information interaction is carried out among all modules based on a digital signal mode;
the data acquisition module is used for acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse food grain proportion, diet taste and the usage amount of dairy products of the food; the carbohydrate content, fat content, protein content, water content, mineral content, vitamin content and dietary fiber content of the food; freshness data of food detected by an olfactory AI sensing system;
the personal attributes include gender, age, region; and excluding atypical factors such as family inheritance, accident and the like;
and sending the behavior data, the food data and the personal attributes to the data processing module;
the data processing module is used for receiving the behavior data, the food data and the personal attributes and obtaining the behavior data, the food data, the personal attributes and a prediction model of the pathogenic direction of the eating behavior; wherein the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
and the intelligent recommendation module is used for recommending recipes and healthy life styles to the user by the expert system according to the predicted chronic disease direction possibly caused by the diet life habits of the user.
In this embodiment, the data acquisition module includes intelligent wearing equipment, intelligent wearing equipment includes camera device, speech recognition device and smell detection device.
In this embodiment, the data acquisition module acquires behavior data, food data, and personal attributes of a user, and the specific process includes:
a user fills in personal attributes through an intelligent terminal; wherein the personal attributes include gender, age, and region; it is further explained that atypical factors such as family inheritance, accidents and the like are excluded;
the user wears the intelligent wearable device;
the intelligent wearable equipment acquires behavior data of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood; it is further noted that the personal emotion is used for identifying the emotion of the user through a voice recognition function of the intelligent wearable device by acquiring tones and keywords; the emotion in unit time can be qualitative, sad, stable and happy;
the intelligent wearable device acquires a food image of a user;
the intelligent wearable device acquires behavior data and food images of a user;
acquiring food analysis data according to the food image and an expert system;
it should be further explained that the expert system is the most important and active application field in artificial intelligence, and it realizes the major breakthrough of artificial intelligence from theoretical research to practical application, and from general reasoning strategy discussion to application of special knowledge. The expert system is an important branch of early artificial intelligence, can be regarded as a computer intelligent program system with special knowledge and experience, and generally adopts knowledge representation and knowledge reasoning technology in artificial intelligence to simulate complex problems which can be solved by field experts generally;
according to the image recognition technology and the expert system, different foods correspond to different detection methods: such as vegetables, and detecting whether the color spots and the color hue are abnormal or not by image recognition;
meat, which can detect whether ammonia, nitrogen and sulfur are abnormal or not through gas; and sending the behavior data and the food analysis data to the data processing module.
In this embodiment, the intelligent terminal includes intelligent devices such as a smart phone and a computer.
The data processing module receives the behavior data and the food data, and obtains the behavior data, the food data and a prediction model of the pathogenic direction of the eating behavior according to the behavior data, the food data and the prediction model of the pathogenic direction of the eating behavior, and the specific process comprises the following steps:
the data processing module receiving the personal attributes, the behavioral data, and the food analysis data;
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic direction prediction model;
selecting food analysis data, behavior data and personal attribute data of a user for a period of time to carry out pretreatment to form a test sample;
and matching the test sample with the typical chronic diseases and the eating behavior relation database data, and predicting the direction of the chronic diseases possibly caused by the eating habits of the user.
In this embodiment, the data processing module performs normalization, which is expressed as:
wherein x' is a normalized value, x is an original value, and x mean Is the average value of the variable, x max Is the maximum value of the variable.
In this embodiment, the processing module uses a sigmoid function from the input layer to the hidden layer.
In an optional embodiment, the establishing of the eating behavior pathogenic direction prediction model based on the artificial intelligence model comprises:
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic prediction model based on a deep neural network;
the method comprises the steps of inputting food analysis data, behavior data and personal attribute data of a user obtained by a wearable data acquisition device for a period of time into a diet behavior pathogenic direction prediction model based on a deep neural network as new samples, namely predicting the direction of a chronic disease possibly caused by the diet living habits of the user.
In this embodiment, the artificial intelligence model includes a model with strong nonlinear fitting capability, such as a deep convolutional neural network model or an RBF neural network model.
The intelligent recommendation module recommends recipes and healthy life style to the user by an expert system according to the predicted chronic disease direction possibly caused by the dietary life habit of the user, and the specific process comprises the following steps:
the intelligent recommendation module receives the chronic disease directions, and an expert system recommends recipes and healthy life styles to the chronic disease directions.
In this embodiment, the data acquisition module is in communication and/or electrical connection with the data processing module;
the data processing module is in communication and/or electrical connection with the intelligent recommendation module.
As shown in fig. 2, a recipe recommendation method based on machine learning specifically includes the following steps:
the method comprises the following steps: acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse food grain proportion, diet taste and the usage amount of dairy products of the food; secondly, the carbohydrate content, the fat content, the protein content, the water content, the mineral content, the vitamin content and the dietary fiber content of the food; thirdly, freshness data of the food detected by the olfactory AI sensing system;
the personal attributes include gender, age, region; and excluding atypical factors such as family inheritance, accident and the like;
step two: receiving the behavior data and the food data, and obtaining the behavior data and the food data according to a prediction model of a pathogenic direction of the eating behavior; wherein the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
step three: and recommending the recipes and the healthy life style to the user by the expert system according to the predicted direction of the chronic diseases possibly caused by the dietary life habits of the user.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (9)
1. A recipe recommendation system based on machine learning is characterized by comprising a data acquisition module, a data processing module and an intelligent recommendation module; information interaction is carried out among all modules based on a digital signal mode;
the data acquisition module is used for acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse grain proportion, diet taste and the usage amount of dairy products of the food; the carbohydrate content, fat content, protein content, water content, mineral content, vitamin content and dietary fiber content of the food; freshness data of the food;
the personal attributes include gender, age, region;
and sending the behavior data, the food data and the personal attributes to the data processing module;
the data processing module is used for receiving the behavior data, the food data and the personal attributes and obtaining the behavior data, the food data, the personal attributes and a prediction model of the pathogenic direction of the eating behavior; the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
the intelligent recommendation module is used for recommending recipes and healthy life styles to the user by the expert system according to the predicted chronic disease direction possibly caused by the dietary life habits of the user.
2. The machine learning-based recipe recommendation system according to claim 1, wherein the data acquisition module comprises an intelligent wearable device, the intelligent wearable device comprising a camera device, a voice recognition device and an odor detection device.
3. The machine learning-based recipe recommendation system according to claim 2, wherein the data acquisition module acquires behavioral data, food data and personal attributes of the user by a specific process comprising:
a user fills in sex, age and area through an intelligent terminal;
the user wears the intelligent wearable device;
the intelligent wearable device acquires the tobacco intake, the eating speed, the drinking amount, the exercise time, the sleep time and the personal emotion of a user;
the intelligent wearable device acquires a food image of a user;
the data acquisition module sends the behavior data, the food data and the personal attributes to the data processing module.
4. The machine learning-based recipe recommendation system according to claim 3, wherein the smart terminal comprises a smart phone and a computer.
5. The machine-learning-based recipe recommendation system according to claim 4, wherein the data processing module receives the behavior data, the food data and the personal attributes, and obtains the behavior data, the food data, the personal attributes and a prediction model of pathogenic directions of eating behaviors according to the following specific procedures:
the data processing module receiving the personal attributes, the behavioral data, and the food analysis data;
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic direction prediction model;
selecting food analysis data, behavior data and personal attribute data of a user for a period of time to carry out pretreatment to form a test sample;
and matching the test sample with the typical chronic disease and the eating behavior relation database data to predict the direction of the chronic disease possibly caused by the eating habits of the user.
6. The machine learning based recipe recommendation system according to claim 5, wherein the establishing of the diet behavior pathogenic direction prediction model based on the artificial intelligence model comprises:
extracting enough training samples according to a typical chronic disease and diet behavior relation database, and establishing a diet behavior pathogenic prediction model based on a deep neural network;
the method comprises the steps that food analysis data, behavior data and personal attribute data of a user obtained by a wearable data acquisition device for a period of time are input into a diet behavior pathogenic direction prediction model based on a deep neural network as new samples, namely, chronic disease directions possibly caused by diet living habits of the user are predicted;
the artificial intelligence model comprises a deep convolution neural network model or an RBF neural network model and other models with strong nonlinear fitting capability.
7. The machine learning-based recipe recommendation system according to claim 6, wherein the intelligent recommendation module recommends a recipe and a healthy lifestyle to the user by an expert system according to the predicted direction of the chronic diseases possibly caused by the dietary life habits of the user, and the specific process comprises:
the intelligent recommendation module receives the chronic disease directions, and an expert system recommends recipes and healthy life styles to the chronic disease directions.
8. The machine learning based recipe recommendation system according to claim 7, wherein the data acquisition module is in communication and/or electrically connected with the data processing module;
the data processing module is in communication and/or electrical connection with the intelligent recommendation module.
9. A recipe recommendation method based on machine learning is characterized by comprising the following steps:
the method comprises the following steps: acquiring behavior data, food data and personal attributes of a user; wherein the behavioral data includes tobacco intake, eating speed, alcohol consumption, exercise time, sleep time, and individual mood;
the food analysis data comprises the type, temperature, meat and vegetable proportion, staple food and coarse food grain proportion, diet taste and the usage amount of dairy products of the food; the carbohydrate content, fat content, protein content, water content, mineral content, vitamin content and dietary fiber content of the food; freshness data of the food;
the personal attributes include gender, age, region;
step two: receiving the behavior data, the food data and the personal attribute, and obtaining the behavior data, the food data, the personal attribute and a prediction model of the pathogenic direction of the eating behavior; wherein the diet behavior pathogenic direction prediction model is obtained based on an artificial intelligence model;
step three: and recommending the recipes and the healthy life style to the user by the expert system according to the predicted direction of the chronic diseases possibly caused by the dietary life habits of the user.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116525067A (en) * | 2023-06-21 | 2023-08-01 | 安徽宏元聚康医疗科技有限公司 | Nutrient recipe recommendation system and method |
| CN116798598A (en) * | 2023-02-24 | 2023-09-22 | 广东康合慢病防治研究中心有限公司 | Method and system for intelligently matching operation paths of chronic disease management standard |
| CN117854676A (en) * | 2024-01-17 | 2024-04-09 | 泰昊乐生物科技有限公司 | Health management plan customization method and system |
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2022
- 2022-09-02 CN CN202211073147.4A patent/CN115470701A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116798598A (en) * | 2023-02-24 | 2023-09-22 | 广东康合慢病防治研究中心有限公司 | Method and system for intelligently matching operation paths of chronic disease management standard |
| CN116525067A (en) * | 2023-06-21 | 2023-08-01 | 安徽宏元聚康医疗科技有限公司 | Nutrient recipe recommendation system and method |
| CN117854676A (en) * | 2024-01-17 | 2024-04-09 | 泰昊乐生物科技有限公司 | Health management plan customization method and system |
| CN117854676B (en) * | 2024-01-17 | 2024-08-27 | 泰昊乐生物科技有限公司 | Health management plan customization method and system |
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