KR102200391B1 - Food recommendation service system based on analysis of user eating habits and method thereof - Google Patents

Abstract

The present invention relates to a food recommendation service system based on analysis of eating habits of a user and a method thereof. According to the present invention, a food recommendation service method using a food recommendation service system based on analysis of eating habits of a user comprises the steps of: collecting food consumption data used by the user for a set period; learning the nutrient intake state of the user from the food consumption data for the set period based on registered national standard food ingredient data; comparing the nutrient intake state of the user with nutrient intake reference data matching personal information of the user to extract insufficient nutrients of the user; and selecting at least one food menu containing the insufficient nutrients from a previously stored recommended candidate food menus and providing the selected menus to the user. According to the present invention, the method analyzes the recent nutrient intake conditions of the user based on the food consumption data of the user collected for the set period and provides recommended food menus capable of compensating for the lack of nutrients, thereby solving a nutritional imbalance of the user, inducing even nutrient intake, and contributing to improved eating habits.

Description

A food recommendation service system based on analysis of user eating habits and method thereof

The present invention relates to a food recommendation service system and method thereof based on user eating habit analysis, and in more detail, by analyzing the nutritional intake status based on the user's food consumption data, by selecting a healthy menu that can supplement insufficient nutritional components. It relates to a food recommendation service system and method based on user eating habit analysis that provides recommendations to users.

Due to the recent increase in single-person households, the rate of eating home meals has been decreasing year by year. According to the '2017 National Health and Nutrition Survey' surveyed by the Korea Centers for Disease Control and Prevention, the rate of energy intake through home meals was 38.3%, which was 20.3% lower than 58.6% in 2005.

In addition, as the number of single-person households increases, it is not easy to cook rice alone, and due to difficulties in handling food materials, more people are trying to solve meals through food establishments and convenience foods rather than home meals. And in recent years, due to the development of technology, delivery orders using a delivery application of a smartphone are also being actively made.

In addition, due to the development of food processing technology, various Home Meal Replacement (HMR) products in the form of room temperature storage that are easy to store are on sale, and convenience store lunchboxes that can be easily solved for one meal at an affordable price are also continuously configured with various menus. Sales are increasing.

Delivery foods and convenience foods have the advantage of being able to consume a variety of foods at any time, but have a chronic problem of causing nutritional imbalance. Most convenience food packaging has a nutritional information label attached to the food, but for consumers who want to eat easily, it will be difficult to think of nutritional ingredients as a major consideration when choosing a menu.

Therefore, there is a need for a service that can recommend a healthier menu in consideration of the nutritional intake of consumers in accordance with the changing dietary trend.

The technology behind the present invention is disclosed in Korean Patent Publication No. 2012-0109264 (published on Oct. 8, 2012).

The present invention provides a food recommendation service system and method based on user eating habits that can provide recommendations by analyzing a nutritional intake state based on a user's food consumption data There is a purpose.

In the present invention, in a food recommendation service method using a food recommendation service system based on user eating habit analysis, the step of collecting food consumption data used by a user for a set period, and the set period based on pre-registered national standard food ingredient data Learning a nutrient intake state of the user from the food consumption data during the period, comparing the nutrient intake state of the user with nutrient intake criterion data matching the user's personal information, and extracting the insufficient nutrient component of the user, And selecting at least one food menu containing the insufficient nutritional component from among pre-stored recommended candidate food menus and providing recommendations to the user.

In addition, in the providing of the recommendation, a food menu containing the insufficient nutritional component and the seasonal food material of the month may be recommended from among the recommended candidate food menus in consideration of the previously registered monthly seasonal food material data.

In addition, the personal information of the user is pre-registered by the user and includes at least one item of gender, age, weight, and height, and the step of extracting the insufficient nutrient component includes pre-established Korean nutrient intake according to the item. By extracting the nutrient intake criterion data matching the personal information of the user from the reference data DB, the extracted nutrient intake criterion data may be compared with the nutrient intake state of the user.

In addition, the recommendation candidate food menu includes a food menu of a Ready-Meal group and a food menu of a Delivery-Meal group, respectively, and the providing of the recommendation includes the ready-meal group and the Each delivery wheat group may individually select food menus that satisfy the insufficient nutritional component and provide recommendations for each group.

In addition, in the providing of the recommendation, N food menus selected from the ready mill group and M food menus selected from the delivery mill group are recommended, and based on the food consumption data collected during the setting period, the group The ratio of N and M, which is the number of menus recommended for each, can be adjusted.

In addition, in the providing of the recommendation, recommendation menus selected for each group are arranged and provided according to a priority, and the priority may be determined according to whether or not seasonal ingredients are included and the number of ingredients for the month.

In addition, in the learning of the nutrient intake state, the food consumption data collected during the set period may be learned by adding a higher weight to the nutrient intake corresponding to the data of a preset recent section.

In addition, the food consumption data may include at least one of food menu and restaurant information used by the user during a set period.

In addition, the step of extracting the insufficient nutritional component extracts the insufficient nutritional component and the required energy amount of the user, and the providing of the recommendation includes providing a food menu that satisfies the insufficient nutritional component and the required energy amount to the user. can do.

In addition, the present invention is a collection unit for collecting food consumption data used by a user for a set period, and learning to learn a user's nutrient intake status from food consumption data for the set period based on pre-registered national standard food ingredient data Buwa, an extraction unit that compares the nutrient intake status of the user with nutrient intake criterion data matched with the user's personal information, and extracts the insufficient nutrient ingredient of the user, and the insufficient nutrient ingredient from the previously stored recommended candidate food menu It provides a food recommendation service system including a recommendation unit that selects at least one contained food menu and provides recommendations to the user.

In addition, the recommendation unit may recommend a food menu containing the insufficient nutritional component and the seasonal food material of the month from among the recommended candidate food menus in consideration of the previously registered seasonal food material data.

In addition, the personal information of the user is previously registered by the user and includes at least one item of gender, age, weight, and height, and the extracting unit includes the user from the Korean nutrient intake standard data DB previously established according to the item. By extracting the nutrient intake criterion data matched with the personal information of the user, the extracted nutrient intake criterion data may be compared with the nutrient intake state of the user.

In addition, the recommendation candidate food menu includes a food menu of a Ready-Meal group and a food menu of a Delivery-Meal group, respectively, and the recommendation unit includes the ready-meal group and the delivery wheat group. Food menus satisfying the insufficient nutritional component may be individually selected from each and recommended for each group.

In addition, the recommendation unit recommends N food menus selected from the ready mill group and M food menus selected from the delivery mill group, and is recommended for each group based on the food consumption data collected during the setting period. The ratio of N and M, which is the number of menus, can be adjusted.

In addition, the recommendation unit arranges and provides recommended menus selected for each group according to a priority, and the priority may be determined according to whether or not seasonal ingredients are included in the month and the number of ingredients.

In addition, the learning unit may learn by adding a higher weight to the nutrient intake corresponding to the data of a preset recent section among the food consumption data collected during the set period.

In addition, the extraction unit may extract the insufficient nutritional component and the required energy amount of the user, and the recommendation unit may recommend and provide a food menu satisfying the insufficient nutritional component and the required energy amount to the user.

According to the present invention, based on the user's food consumption data collected during a set period, the user's recent nutrition intake status is analyzed and a food menu that can supplement insufficient nutrients is recommended to the user, thereby solving the user's nutritional imbalance. It induces even nutrient intake and can contribute to improving eating habits.

In addition, the present invention may induce consumption of seasonal food and promote nutritional balance and health of users by prioritizing or providing a food menu containing seasonal ingredients when providing a recommended menu in consideration of additional seasonality.

1 is a diagram showing the configuration of a food recommendation service system based on user eating habit analysis according to an embodiment of the present invention.
FIG. 2 is a diagram showing the configuration of the service server shown in FIG. 1.
3 is a diagram showing in detail the DB unit shown in FIG. 2.
4 is a diagram illustrating a food recommendation service method based on user eating habit analysis according to an embodiment of the present invention.

Then, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention.

1 is a diagram showing the configuration of a food recommendation service system based on user eating habit analysis according to an embodiment of the present invention.

As shown in FIG. 1, a food recommendation service system according to an embodiment of the present invention includes a plurality of user terminals 200 and a food recommendation service server 100.

The food recommendation service server 100 (hereinafter, referred to as the server) may be connected to a network with a plurality of user terminals 200, and may provide a food recommendation service based on eating habit analysis to each user terminal 200. The following embodiments of the present invention mainly exemplify a wireless network (eg, Wi-Fi, Bluetooth, RF, WLAN), but a wireless, wired or wired/wireless combined network may be used.

In an embodiment of the present invention, the server 100 may be a server providing a food recommendation service, or may be in the form of an application executable on a predetermined device (user terminal). The server 100 may provide a food recommendation service to authenticated users through membership registration or personal information input.

The application may be installed and executed on the user terminal 200. The user terminal 200 may communicate with the server 100 while the application is running to provide a food recommendation service based on eating habit analysis to the user. In this way, the server 100 may be a server for a food recommendation service itself, or may be an application implemented in software on a device such as the user terminal 200.

The user terminal 200 may include a device such as a smart phone, a tablet PC, a notebook, a smart pad, a PC, etc. that can connect to a network to exchange information. Here, in the case of a device with a built-in wireless function (smartphone, notebook, pad, etc.), the function of the system may be provided on the device in the form of a mobile application.

FIG. 2 is a diagram showing the configuration of the service server shown in FIG. 1, and FIG. 3 is a diagram showing the DB unit shown in FIG. 2 in detail.

As shown in Figures 2 and 3, the food recommendation service server 100 according to an embodiment of the present invention includes a collection unit 110, a learning unit 120, an extraction unit 130, a recommendation unit 140, It includes a DB unit 150 and a storage unit 160.

The collection unit 110 collects personal information of the user, collects food consumption data used by the user for a set period, and stores the collected information in the user collection DB 151.

The collection unit 110 may register personal information through an application executed on the user terminal 200 and may receive food consumption data by hour, daily, and the like.

Here, the user's personal information may include at least one item of name, ID, sex, age, weight, and height. In addition, the food consumption data used by the user may include at least one of food menu information purchased or consumed by the user and restaurant information used during a set period.

The collection unit 110 may collect data in a way that directly receives food consumption data from the user, but it is linked to a text message containing card usage history or product purchase history, shopping/mart apps, external servers, etc. Food consumption data can also be automatically collected in real time.

For example, information may be collected through a receipt for purchase of food (including restaurants) that the user has agreed to provide through a web or an app, a delivery purchase history through a web or an app, and consumption history through a household account book app. In the case of automatically collected information, it may be possible to check and edit (edit, delete, etc.) details from the user for the reliability of the system.

An embodiment of the present invention provides a recommendation for a healthy food menu that can supplement a user's nutritional intake status from food consumption data for a set period (eg, a day, a week, or a month), and identify insufficient nutritional components.

In addition, in the process of analysis and food recommendation, analysis performance and reliability are improved by utilizing various standard data registered in the analysis standard DB 152 (national standard food ingredient data, Korean nutrient intake standard data, and seasonal agricultural product ingredients per month). Of course, it can provide recommendations for customized food menus.

The learning unit 120 learns the user's nutrient intake status from food consumption data for a set period based on the registered national standard food ingredient data (national standard food ingredient table).

The national standard food ingredient data is recorded by the Rural Development Administration in cooperation with the Ministry of Food and Drug Safety and analyzing the nutrient content contained in each food for thousands of foods.

The learning unit 120 maps (substitutes) the food history actually consumed by the user to the national standard food ingredient data stored in the DB unit 150 to check the nutrient ingredients matched to each food consumed by the user within the set period, and By using it, the user's nutrient intake state during a set period can be learned by a method such as machine learning.

In this way, the learning unit 120 may learn and analyze the user's nutrient intake status from the food details included in the user's food consumption data.

In this case, the learning unit 120 may learn by adding a higher weight to nutrients intake corresponding to the data of a preset recent period (eg, one week) among food consumption data collected during a set period (eg, one month). . Through this, it is possible to recommend and provide food according to the nutritional status or recent eating habits of the user as much as possible.

The extraction unit 130 compares the nutrient intake status of the user derived from the learning unit 120 with the nutrient intake standard data matching the user's personal information (eg, gender, age, weight, height, etc.), and Extract nutrients.

The extraction unit 130 first extracts the nutrient intake criterion data matching the current user's personal information (eg, sex, age, weight, height, etc.) from the Korean nutrient intake criterion data stored in the DB unit 150, and then extracts the nutrient. By comparing the intake criteria data with the user's nutrient intake status, insufficient nutrient content can be analyzed.

The standard data on nutrient intake for Koreans was published by the Ministry of Health and Welfare, and data on the standard for nutrient intake according to items (conditions) such as sex, age, weight, and height were prepared. The nutrient intake criteria include the amount of energy required corresponding to the condition and the appropriate ratio of energy for each nutrient.

The extraction unit 130 not only checks the user's insufficient nutrient component, but also analyzes the user's required energy amount and an appropriate ratio of energy for each nutrient by using the nutrient intake status of the user and the nutrient intake standard data suitable for the user.

In this way, the extraction unit 130 first searches for nutrient intake criterion data suitable for the current user's personal information from the DB unit 150, and extracts the lack of nutrient components of the current user by referring to the retrieved nutrient intake criterion data. have.

Next, the recommendation unit 140 selects at least one food menu containing insufficient nutritional ingredients from among the recommended candidate food menus previously stored in the DB unit 150 and provides recommendations to the user. Here, of course, the recommendation unit 140 may select a menu by additionally considering the amount of energy required by the user.

This recommendation unit 140 recommends a food menu containing insufficient nutritional ingredients and seasonal ingredients of the month from among the recommended candidate food menus in consideration of monthly seasonal ingredients (seasonal agricultural products) data previously registered in the DB unit 150 can do.

Here, the monthly seasonal food material (seasonal agricultural product) data is data provided by the Ministry of Agriculture, Food and Rural Affairs, and is stored in the analysis standard DB 152. In general, food ingredients are rich in nutrients and are inexpensive in price in seasons with a large amount of production, but there is a side that is burdensome for a single household to purchase because of the large quantity sold. As in this example, products including seasonal agricultural products of the month are used. If recommended, it can promote consumption of seasonal agricultural products and help improve health.

In an embodiment of the present invention, the recommended candidate food menu includes a food menu of a Ready-Meal group and a food menu of a Delivery-Meal group, respectively. This recommendation candidate food menu is stored in the recommendation menu DB 153.

Here, ready-meal is a quick home meal (HMR) that can be consumed immediately after purchase or after a simple cooking process, and instant intake foods (e.g., convenience store lunch boxes, sandwiches), fresh convenience foods (e.g., salads, simple fruits) and instant cooked foods (Example: instant rice, soup, stew), etc. can be included. Delivery wheat is a food that can be consumed after a certain waiting time after purchase, and may include food delivered, food sold in restaurants, and take-out food.

When recommending food, the recommendation unit 140 may divide and recommend a ready mill and a delivery mill. That is, food menus satisfying insufficient nutrients may be individually selected from the ready wheat group and the delivery wheat group, and recommended for each group may be provided.

In addition, the recommendation unit 140 may provide a list of each recommendation menu selected for each group by individually sorting according to priority. For example, recommended menus selected by Ready Mill are arranged according to priority, and recommended menus selected by Delivery Mill are arranged according to priority.

At this time, the priority may be determined according to whether or not seasonal ingredients (seasonal agricultural products) are included in the month and the number of them. For example, as seasonal ingredients are included rather than not included, and the number (type) included is greater, a higher priority is given, and may be exposed to the top of the list.

In the case of such an embodiment of the present invention, a recommendation menu capable of supplementing insufficient nutrition for each intake point in accordance with the food purchase history of users is selected and recommended, as well as giving high priority to food menus including seasonal ingredients in consideration of seasonality. Therefore, by recommending it first or exposing it to the top, users can promote even nutrient intake and health improvement.

In addition, the recommendation unit 140 may recommend N food menus selected from the ready mill group and M food menus selected from the delivery mill group. The ratio of the number of recommended menus M and M for each group is 5: It may be the same as 5, or may be different, such as 3:7, 6:4, 8:2, etc.

The recommendation unit 140 may adjust the ratio of N and M, which is the number of menus recommended for each group, based on the food consumption data collected for a set period. For example, if the recent share of ready wheat in the user's food consumption data is higher than that of delivery wheat, M may be set to be larger than N so that the recommended share of delivery wheat is higher than that of ready wheat.

The DB unit 150 includes a user collection DB 151, an analysis criterion DB 152, and a recommendation menu DB 153. The user collection DB 151 stores user personal information and food consumption (purchase, consumption) data collected through the collection unit 110, respectively.

Analysis Criteria DB 152 stores the national standard food ingredient table, Korean nutrient intake standard data, and monthly seasonal ingredients. In addition, the recommended menu DB 153 stores various recommended candidate food menus including ready wheat and delivery wheat. Here, the analysis criterion DB 152 may be included in the server 100, but may be connected to the server 100 via a network outside of the server 100 to communicate or be included in an external server.

The storage unit 160 stores the user's nutrient intake status, insufficient nutritional content, and recommended food menu in association and stores, and may provide a history or statistics based on information periodically stored for each set period.

4 is a diagram illustrating a food recommendation service method based on user eating habit analysis according to an embodiment of the present invention.

First, the collection unit 110 collects food consumption data used by the user for a set period (S410).

Then, the learning unit 120 learns the state of intake of nutrients of the user from the food consumption data collected during the set period based on the registered national standard food ingredient data (S420).

Thereafter, the extraction unit 130 compares the nutrient intake state of the user with the nutrient intake criterion data matched with the user's personal information, and extracts the lack of nutrient component of the user (S430). In this case, in consideration of the user's personal information and the corresponding nutrient intake standard data, the required energy amount and the appropriate energy ratio for each nutrient may be calculated and provided along with the information on the nutrient insufficiency of the user.

Then, the recommendation unit 140 selects at least one food menu containing insufficient nutritional ingredients from among the previously stored recommendation candidate food menus and provides recommendations to the user (S440). At this time, it is possible to provide recommendations by dividing the categories into ready mills and delivery mills, and provide recommendations by giving priority to menus containing seasonal agricultural products through inference work by fusion with monthly seasonal agricultural product data.

According to the present invention as described above, based on the user's food consumption data collected during the set period, the user's recent nutrition intake status is analyzed and a food menu capable of supplementing insufficient nutrients is provided to the user, thereby recommending the user's nutritional imbalance. It can contribute to improving dietary habits, inducing even nutrient intake, and relieving food.

In addition, the present invention may induce consumption of seasonal food and promote nutritional balance and health of users by prioritizing or providing a food menu containing seasonal ingredients when providing a recommended menu in consideration of additional seasonality.

The present invention has been described with reference to the embodiments shown in the drawings, but these are only exemplary, and those of ordinary skill in the art will appreciate that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical scope of the present invention should be determined by the technical spirit of the appended claims.

100: food recommendation service server 110: collection unit
120: learning unit 130: extraction unit
140: recommendation unit 150: DB unit
151: user collection DB 152: analysis criteria DB
153: recommended menu DB 160: storage
200: user terminal

Claims (18)

In the food recommendation service method using a food recommendation service system based on user eating habit analysis,
Collecting food consumption data used by the user for a set period;
Learning a nutrient intake state of a user from food consumption data for the set period based on pre-registered national standard food ingredient data;
Comparing the nutrient intake state of the user with the nutrient intake criterion data matched with the user's personal information, and extracting the insufficient nutrient component of the user; And
Selecting at least one food menu containing the insufficient nutritional ingredient from among pre-stored recommended candidate food menus and providing recommendations to the user,
The learning step,
A food recommendation service method for learning by adding a higher weight to an intake nutrient corresponding to the data of a preset recent section among food consumption data collected during the set period. The method according to claim 1,
The step of providing the recommendation,
A food recommendation service method for recommending and providing a food menu containing the insufficient nutritional component and seasonal ingredients of the month from among the recommended candidate food menus in consideration of previously registered monthly seasonal food material data. The method according to claim 1,
The personal information of the user,
It is previously registered by the user and includes at least one item of gender, age, weight, and height,
The step of extracting the insufficient nutritional component,
A food recommendation service method for extracting nutrient intake criterion data matching the personal information of the user from the Korean nutrient intake criterion data DB previously established according to the above items, and comparing the extracted nutrient intake criterion data with the nutrient intake status of the user . The method according to claim 1,
The above recommended candidate food menu,
Including the food menu of the Ready-Meal group and the food menu of the Delivery-Meal group, respectively,
The step of providing the recommendation,
A food recommendation service method for individually selecting food menus satisfying the undernutrition in each of the ready mill group and the delivery mill group and providing recommendations for each group. The method of claim 4,
The step of providing the recommendation,
It recommends N food menus selected from the ready mill group and M food menus selected from the delivery mill group,
A food recommendation service method of adjusting the ratio of the N and M, which is the number of menus recommended for each group, based on the food consumption data collected during the set period. The method of claim 4,
The step of providing the recommendation,
The recommended menus selected for each group are arranged and provided according to priority,
The priority above is,
A food recommendation service method that is determined based on the availability and number of seasonal ingredients for the month. delete The method according to claim 1,
The above food consumption data,
Food recommendation service method including at least one of food menu and restaurant information used by the user during a set period. The method according to claim 1,
The step of extracting the insufficient nutritional component extracts the insufficient nutritional component and the required energy amount of the user,
The step of providing the recommendation,
A food recommendation service method for recommending a food menu that satisfies the insufficient nutritional component and required energy amount to a user. A collection unit for collecting food consumption data used by the user for a set period;
A learning unit that learns a user's nutrient intake status from food consumption data for the set period based on pre-registered national standard food ingredient data;
An extraction unit that compares the user's nutrient intake state with nutrient intake standard data matching the user's personal information, and extracts the user's insufficient nutrient component; And
And a recommendation unit for selecting at least one food menu containing the insufficient nutritional ingredient from among pre-stored recommended candidate food menus and providing recommendations to the user,
The learning unit,
A food recommendation service system for learning by adding a higher weight to nutrients intake corresponding to the data of a preset recent section among food consumption data collected during the set period. The method of claim 10,
The recommendation unit,
A food recommendation service system that recommends a food menu containing the insufficient nutritional ingredients and seasonal ingredients of the month from among the recommended candidate food menus in consideration of previously registered monthly seasonal food material data. The method of claim 10,
The personal information of the user,
It is previously registered by the user and includes at least one item of gender, age, weight, and height,
The extraction unit,
A food recommendation service system that extracts nutrient intake criterion data matching the personal information of the user from the Korean nutrient intake criterion data DB previously established according to the above items, and compares the extracted nutrient intake criterion data with the nutrient intake state of the user . The method of claim 10,
The above recommended candidate food menu,
Including the food menu of the Ready-Meal group and the food menu of the Delivery-Meal group, respectively,
The recommendation unit,
A food recommendation service system that individually selects food menus satisfying the undernutrition in each of the ready mill group and the delivery mill group and provides recommendations for each group. The method of claim 13,
The recommendation unit,
It recommends N food menus selected from the ready mill group and M food menus selected from the delivery mill group,
A food recommendation service system for adjusting a ratio of the N and M, which is the number of menus recommended for each group, based on the food consumption data collected during the set period. The method of claim 13,
The recommendation unit,
The recommended menus selected for each group are arranged and provided according to priority,
The priority above is,
A food recommendation service system that is determined based on the availability and number of seasonal ingredients for the month. delete The method of claim 10,
The above food consumption data,
A food recommendation service system including at least one of food menu and restaurant information used by a user during a set period. The method of claim 10,
The extraction unit extracts the insufficient nutritional component and the required energy amount of the user,
The recommendation unit,
A food recommendation service system that recommends a food menu that satisfies the insufficient nutritional component and required energy amount to a user.

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