CN114582465A - Diet plan recommendation method, device, equipment and readable storage medium - Google Patents

Abstract

The application discloses a diet plan recommendation method, a diet plan recommendation device and a readable storage medium. By adopting the scheme, the electronic equipment combines the historical menu record of the user in the recent period of time and the current state data of the user to recommend the menu for the user, so that the purpose of accurate recommendation is realized.

Description

Diet plan recommendation method, device, equipment and readable storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a diet plan recommendation method, apparatus, device, and readable storage medium.

Background

With the rapid development of artificial intelligence, more and more intelligent machines are applied to the lives of people, such as intelligent cooking equipment and the like. The user utilizes intelligent cooking equipment, can accomplish automatic culinary art process under few participation step, brings very big facility for cooking the food.

In the prior art, some recipes are preset in the intelligent cooking equipment, a user selects or searches the recipes from the recipes, and the intelligent cooking equipment automatically cooks gourmet food according to the recipes selected by the user.

However, the intelligent cooking device recommends the recipe to the user only according to the selection or search of the user, does not consider whether the user is eating reasonably, and cannot achieve accurate recommendation.

Disclosure of Invention

The application provides a diet plan recommending method, a diet plan recommending device and a readable storage medium, which combine historical menu records of a user in a recent period of time and current state data of the user to recommend a menu for the user, and achieve the purpose of accurate recommendation.

In a first aspect, an embodiment of the present application provides a diet plan recommendation method, including:

acquiring a historical menu record of cooking of a user through first Internet of things equipment;

acquiring the current state data of the user;

determining a diet plan according to the historical menu records and the state data;

outputting the diet plan.

In a second aspect, an embodiment of the present application provides a diet plan recommendation device, including:

the first obtaining module is used for obtaining a historical menu record of cooking of a user through the first Internet of things equipment;

the second acquisition module is used for acquiring the current state data of the user;

the processing module is used for determining a diet plan according to the historical menu record and the state data;

an output module for outputting the diet plan.

In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory and a computer program stored on the memory and executable on the processor, the processor when executing the computer program causing the electronic device to carry out the method according to the first aspect or the various possible implementations of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which computer instructions are stored, and when executed by a processor, the computer instructions are configured to implement the method according to the first aspect or various possible implementation manners of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program, which when executed by a processor, implements the method according to the first aspect or the various possible implementations of the first aspect.

According to the diet plan recommendation method, the diet plan recommendation device, the diet plan recommendation equipment and the readable storage medium, the electronic equipment obtains the historical menu record of cooking of the user through the first Internet of things equipment, obtains the current state data of the user, and determines and outputs a diet plan according to the historical menu record and the current state data of the user. By adopting the scheme, the electronic equipment combines the historical menu record of the user in the recent period of time and the current state data of the user to recommend the menu for the user, so that the purpose of accurate recommendation is realized.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of a network architecture to which a diet plan recommendation method provided in an embodiment of the present application is applied;

FIG. 2 is a flow chart of a dietary plan recommendation method provided by an embodiment of the present application;

FIG. 3 is a detailed flowchart of step 203 in FIG. 2;

FIG. 4 is another flow chart of a dietary plan recommendation method provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of a dietary plan recommendation device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

With the development of artificial intelligence technology and the application of falling to the ground, intelligent cooking equipment such as an intelligent cooking machine, an intelligent bread maker and the like appears. Because intelligent cooking equipment can accomplish the culinary art according to the menu is automatic, give people the life and bring very big facility, consequently receive people's favor doubly. When cooking is started, a plurality of menus are displayed on a display screen of the cooking equipment, and after a user selects the menus, the intelligent cooking equipment automatically cooks the gourmet food according to the menus selected by the user. If the menu searched by the user does not exist locally in the intelligent cooking equipment, the menu is downloaded from the cloud end by the intelligent cooking equipment, and after the menu is successfully downloaded, the food is automatically cooked according to the menu.

At present, the social rhythm is very fast, and people do not pay attention to the dietary structure because of reasons such as busy work, so that the body is lack of certain nutrient components or certain nutrient components are excessive. Malnutrition or overnutrition can easily cause deterioration of physical conditions and various diseases. Clearly, the user desires a reasonably healthy diet.

However, the existing intelligent cooking device recommends recipes to the user only according to the selection or search of the user, does not consider whether the user reasonably eats, and cannot realize accurate recommendation.

Based on this, the embodiment of the application provides a diet plan recommendation method, a diet plan recommendation device, diet plan recommendation equipment and a readable storage medium, which combine historical menu records of a user in a recent period of time and current state data of the user to recommend a diet plan for the user, so as to achieve the purpose of accurate recommendation.

Fig. 1 is a schematic diagram of a network architecture to which a diet plan recommendation method provided in an embodiment of the present application is applied. Referring to fig. 1, the network architecture includes: the Internet of Things system comprises an Internet of Things server 11 and at least two Internet of Things devices 12, wherein the Internet of Things server 11 and each Internet of Things device 12 establish network connection to form a Narrow-Band Internet of Things (NB-IoT).

The internet of things server 11 may be hardware or software. When the internet of things server 11 is hardware, the internet of things server 11 is a single server or a distributed server cluster composed of a plurality of servers. When the internet of things server 11 is software, it may be a plurality of software modules or a single software module, and the embodiments of the present application are not limited.

The internet-of-things device 12 includes a first internet-of-things device 121 and a second internet-of-things device 122. The first internet of things equipment comprises but is not limited to a food processor, an oven, an air fryer, a cooking machine, a bread maker, an intelligent electric oven, an intelligent breakfast machine, an intelligent wall breaking machine, a frying pan, an intelligent pressure cooker, a toaster, an electric cooker, an electric pressure cooker and the like. The first internet of things device 121 may be designed integrally, an operating system is built in the first internet of things device 121, and a display screen may be installed on the first internet of things device 121, and the first internet of things device 121 receives various instructions input by a user through a touch control type operating method or a key-press type operating mode, and feeds back and displays various information of a cooking process to the user.

In addition, the first internet of things device 121 may also be designed to be separated, and a wireless or wired communication module may be installed on the first internet of things device 121. The first internet of things device 121 establishes a communication connection with an external smart device through a built-in communication module. For example, the first internet of things device 121 may establish a wireless connection with a smart phone, a smart speaker, a smart television box, and the like through wireless modules such as Wi-Fi, bluetooth, ZigBee, Near Field Communication (NFC), and the like, and the first internet of things device 121 may also establish a wired connection with a smart phone, a smart speaker, a smart television box, and the like through wired modules such as a serial port, a USB interface, and the like. Like this, the user can be through controlling outside smart machine to assign each item instruction to first thing networking device 121, and through each item data of smart machine control first thing networking device 121 culinary art in-process.

The second internet of things device 122 is a nursing device capable of detecting status data of a user, and does not belong to a cooking device, including but not limited to a beauty instrument, a hairdressing comb, a smart watch, a smart bracelet, a smart scale, and the like. Similarly, the second networking device 122 may be an integrated design or a separate design, and the embodiments of the present application are not limited thereto.

In the embodiment of the application, the diet plan comprises target recipes for one day, one or several days or even one month every day in the future, and is a collection of a plurality of target recipes which are arranged according to the sequence of three meals a day and are convenient for a user to view.

The internet of things server 11 acquires the historical menu record from the first internet of things device 121, acquires the current state data of the user from the second internet of things device 122, and determines the diet plan for a period of time in the future according to the historical menu record and the current state data of the user. For example, the internet of things server determines a diet plan most suitable for the user on the same day or the next day according to the historical menu records and the current state data of the user every day and pushes the diet plan to the first internet of things device 121, and the first internet of things device 121 displays the diet plan at the top of the menu page. For another example, the internet of things server determines a menu suitable for the user in the next 7 days according to the historical menu record and the current state data of the user, generates a diet plan according to the menu of the 7 days, and pushes the diet plan to the first internet of things device or the terminal device of the user, and the client APP of the first internet of things device is installed on the terminal device, so that the user can quickly know the diet plan of the future 7 days through the client and prepare food materials. In addition, the internet of things server 11 may also generate a diet plan for 10 days in the future and 1 month in the future, which is not limited in the embodiment of the present application.

It should be noted that, in the above fig. 1, the internet of things server 11 determines a diet plan and pushes the diet plan to the first internet of things device as an example, and the diet plan recommendation method according to the embodiment of the present application is described. However, the embodiment of the present application is not limited, and in other possible implementations, the first internet of things device 121 may obtain the historical menu record locally, obtain the current status data of the user from the second internet of things device 122, and determine the diet plan according to the historical menu record and the status data.

In addition, the user may also input current status data of the user through a touch screen or the like on the first internet of things device 121, and then the first internet of things device 121 determines a diet plan according to the historical recipe record and the status data. Or, the first internet of things device 121 is provided with an image acquisition device such as a camera, and acquires a user image, such as a facial image of the user, through the camera, and the first internet of things device 121 determines the state data according to the user image.

In addition, a first APP of the first internet of things device 121 and a second APP of the second internet of things device 122 are installed on the electronic device of the user, such as a mobile phone of the user, a pad, and the like, and the terminal device acquires the historical menu record through the first APP and acquires the state data through the second APP. The terminal device then determines a diet plan based on the historical recipe records and the status data. Or the terminal equipment acquires the historical menu record through the first APP, acquires the user image by using a camera and the like, and determines the state data according to the user image.

The following describes in detail a diet plan recommendation method provided in the embodiment of the present application based on the network architecture shown in fig. 1. Referring to fig. 2, fig. 2 is a flowchart of a diet plan recommendation method according to an embodiment of the present application. The embodiment is described from the perspective of an electronic device, and the electronic device may be an internet of things server, a first internet of things device, a terminal device of a first APP in which the first internet of things device is installed, and the like. The embodiment comprises the following steps:

201. and acquiring a historical menu record of cooking of the user through the first Internet of things device.

In the embodiment of the application, the first internet of things equipment is cooking equipment, and after a user cooks by using the first internet of things equipment each time, the first internet of things equipment generates a cooking log which is used for indicating the name of a menu, the type of food materials, the weight and the taste of various food materials and the like. When the electronic equipment is the internet of things server, the first internet of things equipment uploads the cooking log to the internet of things server in real time, or the internet of things server acquires the cooking log regularly. The Internet of things server collects cooking logs in a period of time to obtain historical menu records.

When a plurality of cooking devices exist in the internet of things, for example, an intelligent oven, an intelligent cooking machine and an air fryer exist in the house of a user at the same time, the server of the internet of things acquires cooking logs of the cooking devices, and the cooking logs are collected to obtain historical menu records.

In the embodiment of the application, the historical recipe record is used for recording the names of the recipes cooked by the user in a past period of time, the food material types and the food material weights corresponding to each recipe, the used seasonings, the used cooking equipment and the like. For example, the history recipe record represents information about a recipe cooked by the user for the past month, the past week, and the past 10 days.

202. And acquiring the current state data of the user.

For example, the electronic device may obtain the current state data of the user in various ways.

For example, when the electronic device is a first internet of things device, the user may directly input the status data through a touch screen or the like of the first internet of things device. Or the first internet of things equipment is provided with an image acquisition device such as a camera, the user image is acquired through the camera, and the user image is analyzed to obtain user state data.

For another example, the electronic device is a server of the internet of things, a second networking device also exists in the internet of things, the second networking device is a nursing device capable of detecting state data of a user, and the second networking device does not belong to cooking equipment, and includes but is not limited to a beauty instrument, a hairdressing comb, a smart watch, a smart bracelet, a smart weight scale and the like.

When the second networked device is a cosmetic device, the status data includes moisture content, oil content, elasticity of the skin of the user's face, an image of the user's face, and the like. According to the state data, the server of the internet of things can determine skin symptoms, including but not limited to: dry skin, oily skin, mixed skin, allergic skin, acne, molting, etc.

When the second networked device is a hair comb, the status data includes moisture content, oil content, tenacity, dandruff level, etc. of the user's hair. According to the state data, the server of the internet of things can determine hair symptoms, including but not limited to: excessive dandruff, dry hair, frizzy hair, oily hair, and acne on scalp.

When the second networked device is a sports bracelet, the state data includes the user's heart rate, temperature, blood pressure, running volume, etc. According to the state data, the Internet of things server can determine whether the heart rate of the user is abnormal or not, whether the blood pressure is abnormal or not, whether the user participates in long-distance running or not and the like.

And when the diet plan is determined each time, the server of the internet of things acquires the current state data of the user through the second networked device.

If the electronic device is a terminal device of the user, a first APP of the first internet of things device and a second APP of the second internet of things device are installed on the terminal device, the terminal device obtains historical menu data through the first APP, and obtains state data through the second APP.

203. And determining a diet plan according to the historical menu records and the state data.

After the historical menu records and the current state data of the user are obtained, the Internet of things server performs comprehensive analysis on the historical menu records and the current state data, and accordingly a diet plan is determined. For example, based on historical recipe records, the internet of things server may determine which nutrients a user has primarily ingested, which nutrients have been ingested in excess, which nutrients have been ingested in deficiency, etc. over a period of time. Meanwhile, determining which nutritional ingredients are required to be supplemented, which nutritional ingredients are required to be excessively ingested, and the like according to the user state data. Combining the historical menu records and the current state data of the user, determining a diet plan which can provide nutrient components which the user needs to supplement but does not contain or contains a small amount of nutrient components which the user takes excessively.

Therefore, after the internet of things server acquires historical menu information through the cooking log of the first internet of things device, the internet of things server performs automatic analysis by combining data acquired from other internet of things devices in the same internet of things, and thus a diet plan is obtained.

204. Outputting the diet plan.

When the electronic equipment is the internet of things server, the internet of things server pushes the diet plan to the first internet of things equipment or the terminal equipment according to the client in real time after determining the diet plan each time. Or when the user requests the diet plan through the client, the server of the internet of things sends the instruction information carrying the diet plan to the client.

Optionally, when the internet of things server outputs the diet plan, in one mode, the diet plan is carried in the indication information and sent to the first internet of things device. When the first internet of things device is provided with the display screen, the diet plan can be preferentially displayed on the display screen, and meanwhile, the benefits of the user for cooking dishes corresponding to the diet plan in the last period of time are displayed, such as improvement of the skin condition of the user, alleviation of hair loss symptoms of the user and the like. In addition, the first internet of things device can prompt the user of the advantages of the diet plan and the cooking diet plan on the physical condition of the user in a voice mode and the like.

In another mode, the internet of things server carries the diet plan in the indication information and sends the diet plan to the terminal equipment of the user, the client of the first internet of things equipment is installed on the terminal equipment, and the terminal equipment preferentially displays the diet plan on the menu page. For example, 100 recipes are preset on the client, 10 recipes are displayed on each page, and after the user installs the client on the terminal device for the first time, 100 recipes are displayed in the preset order on the recipe pages. After receiving the instruction information, the terminal device reorders the 100 recipes and displays the diet plan at the top. Therefore, the user clicks the client to enter the menu page, can quickly know the diet plan in the future for a period of time and prepare food materials without turning pages.

When the electronic device is a terminal device, the terminal device displays the diet plan on a user interface.

When the electronic device is the first internet of things device, the diet plan is displayed on the touch screen after the user is started each time, or the diet plan is prompted to the user through voice and the like.

According to the diet plan recommendation method provided by the embodiment of the application, the electronic equipment obtains the historical menu record of cooking of the user through the first Internet of things equipment, obtains the current state data of the user, and determines and outputs a diet plan according to the historical menu record and the current state data of the user. By adopting the scheme, the electronic equipment combines the historical menu record of the user in the recent period of time and the current state data of the user to recommend the menu for the user, so that the purpose of accurate recommendation is realized.

Fig. 3 is a detailed flowchart of step 203 in fig. 2. Fig. 3 includes:

301. an electronic device determines a first daily intake of various nutritional components by the user from the historical recipe record.

Illustratively, a table is pre-stored on the electronic device that represents the standard intake of each nutritional component, as in table 1.

TABLE 1

In addition, only a part of the nutrients are shown in table 1, and actually, the kinds of the nutrients include, but are not limited to, the nutrients listed in table 1.

When a user uses the first Internet of things device for the first time, family members, age, preference and the like are input, after the electronic device determines a historical menu record according to a cooking log of a past period of time, the average first intake of each user for each nutrient component every day is determined according to the menu recorded in the historical menu record.

For example, the user is a solitary user, and there are a smart oven, a smart food processor, and an air fryer at home. In the past month, the user has had three meals a day at home for 20 days, using the smart oven, the smart food processor and the air fryer. In the process of determining the diet plan, the electronic device obtains cooking logs from the first internet of things devices, and collects the cooking logs to obtain historical menu records. Then, the total amount of each nutrient component input by the user in 20 days is determined according to the content of each nutrient component in each gram of food material, and then the first intake of each nutrient component per day by the user is calculated. For example, the protein intake is: protein ═ meat (unit g) × N% + eggs (unit g) × M% + fish (unit g) × P%, where N% represents protein content per gram of meat, M% represents protein content per gram of eggs, and P% represents protein content per gram of fish.

After the electronic device calculates the average daily intake of each nutrient, if there are other family members, it is necessary to further calculate the average daily first intake of each nutrient for each person.

In addition, it is highly likely that the user will eat breakfast or dinner only at home, or dinner only at home. At this time, the electronic device calculates the average intake of various nutrient components in each person's dinner every day, and then estimates the average intake of various nutrient components in each person every day. Alternatively, the above table 1 is the standard amount of each nutrient component at dinner, and the electronic device can determine the excessive nutrient component and the deficient nutrient component which are taken by the user at dinner by comparing the average intake of each nutrient component in each person with the table 1.

The electronic equipment can determine the first intake of various nutritional ingredients according to the history menu record, and further can determine which nutritional ingredients are too much, which nutritional ingredients are not enough and which nutritional ingredients are not taken by the user in the past period of time by combining the table 1.

302. The electronic device determines a second daily intake of the various nutritional components by the user based on the status data.

The electronic device determines a second daily intake of the various nutritional components by the user based on the status data. For example, the status data indicates moisture content, oil content, elasticity, etc. of the facial skin of the user. The electronic equipment determines that the user is oily skin, dry skin, allergic skin and the like according to the facial features. Each skin type corresponds to a different second intake. Taking fat as an example, the second intake amount for dry skin is 70g, and the second intake amount for oily skin is 100 g. The electronic equipment can determine which nutrient components are over-intake, which nutrient components are under-input and which nutrient components are not intake of the user according to the current state data of the user. By adopting the scheme, the purpose that the electronic equipment accurately determines the second intake amount is achieved.

303. The electronic equipment determines target intake amounts of various nutritional ingredients according to the first intake amount, the second intake amount and a preset standard intake amount of the various nutritional ingredients.

The electronic equipment comprehensively analyzes the first intake amount, the second intake amount and the standard intake amount of each nutrient component, and determines the target intake amount of the nutrient components.

For example, the electronic device determines the nutrient composition with excessive intake according to the first intake and the standard intake, determines the nutrient composition with excessive intake according to the second intake and the standard intake, and then determines the intersection of the two, and takes the nutrient composition in the intersection as the nutrient composition with excessive intake. This is because: taking the nutrient component x as an example, when the first intake is larger than the standard intake, the user is indicated to take excessive amount of the nutrient component x. However, since the user may not eat at home every ton, the reason that the first intake is larger than the standard intake is that the dish cooked at home by the user contains a large amount of the nutritional ingredient x.

If the second intake is also greater than the standard intake, the electronic device may determine: the user does ingest an excess of nutrient x. Therefore, the diet plan must contain no or small amounts of the nutritional ingredient x.

Similarly, the electronic device may determine in the same manner that the user has ingested too little of the nutritional components, collectively referred to as user-deficient nutritional components, which the dietary plan must contain in large amounts.

For another example, for each nutrient, the electronic device determines a first difference based on the first intake and a standard intake, and determines a second difference based on the second intake and the standard intake. Thereafter, a target intake of the nutritional component is determined based on the standard intake, the first difference, and the second difference.

Illustratively, for each nutrient, assuming a first difference is a and a second difference is B, the target intake is (standard intake + a + B) × Y%, and Y is | a/B |. Wherein, a is the standard intake-the first intake, and B is the standard intake-the second intake. When A > 0, it means that the nutrient is not ingested sufficiently, and when A < 0, it means that the nutrient is ingested excessively. Similarly, when B > 0, it means that the nutrient is not ingested sufficiently, and when B < 0, it means that the nutrient is ingested excessively. | A/B | represents the absolute value of the ratio of A to B.

Taking the nutritional ingredient protein as an example, assuming that the standard intake of protein is 60g, the first intake is 70g, and the second intake is 68g, the first difference a is-10 g, the second difference B is-8 g, and the target intake is (60-10-8) × 1.25 is 52.5 g.

By adopting the scheme, the electronic equipment determines the target intake of the nutrient components according to the standard intake, the first difference value between the first intake and the standard intake, and the second difference value between the second intake and the standard intake, thereby achieving the purpose of accurately determining the target intake of various nutrient components.

304. The electronic device determines the dietary plan based on the target intake of various nutritional components.

The electronic equipment determines the nutrient components which are lack by the user and the nutrient components which are excessively ingested by the user according to the target intake of each nutrient component, selects a menu which contains a large amount of nutrient components which are lack by the user and does not contain or slightly contains the nutrient components which are excessively ingested by the user from the candidate menu, and generates a diet plan according to the menus.

For example, the electronic device traverses each candidate recipe, selecting a recipe from which the diet plan includes.

For another example, the electronic device classifies each menu in the menu collection in advance according to the main nutrient component to obtain a plurality of menu categories. For example, see table 2.

TABLE 2

Vitamin menu	Fat menu	Dietary fiber menu	……
				Bean curd with thousand leaves	Braised pork with brown sauce	Celery shredded pork	……
Bamboo shoot cooked with soybean milk	Braised beef in soy sauce	Steamed sweet potato	……
				……	……	……	……

When the diet plan is determined, the electronic equipment determines the nutritional ingredients which are lacked by the user according to the target intake of the various nutritional ingredients, then determines the menu category corresponding to the nutritional ingredients which are lacked by the user from the plurality of menu categories, and finally determines the diet plan according to the menu category. For example, the user is lack of vitamins, and a menu is selected from a vitamin menu to generate a diet plan and recommended to the user. The basis of selection includes but is not limited to: season vegetables, user preferences, and the like.

Or the electronic equipment determines the nutrient components which are excessively taken by the user according to the target intake of various nutrient components, then determines the menu category corresponding to the nutrient components which are excessively taken by the user from a plurality of menu categories, and generates the diet plan according to the menu categories except the menu category in the plurality of menu categories. Referring to table 2 again, assuming that the user has excessive fat intake, the electronic device generates a diet plan from a menu category such as a vitamin menu and a dietary fiber menu and recommends the diet plan to the user.

By adopting the scheme, the purpose of accurately determining the diet plan is realized.

It should be noted that, although the foregoing embodiments all determine the diet plan by the electronic device according to the historical menu records and the current state data of the user, the diet plan recommendation method according to the embodiments of the present application will be described in detail. However, the embodiment of the present application is not limited thereto, and in other possible implementations, the electronic device may determine the diet plan only according to the historical menu records or the current state data of the user.

Optionally, in the above embodiment, the electronic device determines a plurality of candidate symptoms in advance according to the second networked device, and the candidate symptoms include skin symptoms and/or hair symptoms. For each candidate symptom, the electronic device determines a corresponding data structure to generate a mapping table. The mapping table records the correspondence between symptoms and data structures. The data structure contains at least one of the following information: substances to be supplemented, suitable food materials, suitable seasonings, contraindicated food materials, and contraindicated seasonings.

Illustratively, the electronic device crawls a plurality of candidate symptoms in advance through a web crawler, and for each symptom, determines a cause causing the user to have the symptom, such as which nutritional components are lacking, and the nutritional components may be proteins, minerals and the like. Then, determining which food materials or seasonings contain the nutritional ingredients again through a web crawler technology, and finally constructing a data structure of symptom-nutritional ingredients-food materials/seasonings. Table 3 illustrates the mapping of symptoms to data structures.

TABLE 3

By adopting the scheme, the electronic equipment respectively crawls the nutrient components corresponding to the symptoms, and the suitable food materials, the taboo food materials, the suitable seasonings, the taboo seasonings and the like corresponding to the nutrient components through a crawler technology twice, so that a data structure is constructed, and the purpose of accurately establishing the mapping table containing the corresponding relation between the symptoms and the data structure is realized.

Optionally, in the above embodiment, after the electronic device determines the state data, the current symptom of the user is determined according to the state data, and the current symptom includes a skin symptom and/or a hair symptom. And then, the electronic equipment queries a mapping table according to the current symptom to determine a data structure corresponding to the current symptom. Finally, the electronic device determines a diet plan based on the data structure.

Illustratively, after acquiring the current state data of the user, the electronic device compares the state data with a locally pre-stored symptom table to determine the current symptoms of the user, such as skin acne, skin desquamation, dandruff, dry hair, frizzy hair, head oil, and the like. Then, the electronic device obtains a data structure corresponding to the current symptom according to the current symptom lookup table 3, wherein the data structure indicates the nutritional ingredients, suitable food materials, suitable seasonings, contra-food materials, contra-seasonings and the like that the user needs to supplement. Finally, the electronic device determines a diet plan based on the data structure.

Taking the second networked device as a beauty instrument as an example, 4 symptom tables, namely a neutral skin symptom table, a dry skin symptom table, an oily skin symptom table and a mixed skin symptom table, are stored in advance on the electronic device, and are respectively referred to tables 4-7.

TABLE 4

TABLE 5

TABLE 6

TABLE 7

After the electronic equipment detects the current state data, the state data are found to be in accordance with the table 7, and then the skin symptom of the user is determined to be mixed skin. Next, the electronic device queries table 3 to obtain a data structure corresponding to the mixed skin, which indicates that the user needs to supplement minerals, and suitable food materials include pork liver, fish meat, shrimp, and the like. Finally, the electronic device determines that the diet plan includes recipes for pork liver soup, halal bass, boiled shrimp, and the like.

The electronic device may determine the diet plan only according to the data structure corresponding to the current symptom, or determine the diet plan according to the food material preferred by the user in combination with the historical recipe record, which is not limited in the embodiment of the present application.

By adopting the scheme, the electronic equipment determines the nutritional ingredients, the suitable food materials, the suitable seasonings, the taboo food materials, the taboo seasonings and the like which need to be supplemented by the user according to the current symptoms, and then determines the diet plan, so that the aim of accurately determining the diet plan is fulfilled.

In the following, from the perspective of the first internet of things device, a diet plan recommendation method executed by the first internet of things device is described in detail. For example, please refer to fig. 4. Figure 4 is another flow chart of a dietary plan recommendation method provided by an embodiment of the present application,

fig. 4 includes the following steps:

401. the method comprises the steps that a first Internet of things device receives indication information which comes from an Internet of things server and carries a diet plan, the diet plan is determined by the Internet of things server according to historical menu records of cooking of the first Internet of things device and current state data of a user, and the state data is acquired by the Internet of things server from other Internet of things devices except the first Internet of things device.

402. And the first Internet of things equipment adjusts the menu display sequence according to the diet plan.

For example, after the first internet of things device receives the diet plan, the display sequence of the local recipes is readjusted to preferentially display the recipes included in the diet plan. Therefore, the user clicks the client to enter the menu page, can quickly know the diet plan for a future period of time and prepare the food materials without turning the page.

It can be seen from this that: the order of the menu displayed by the first internet of things device is not fixed, but is continuously and dynamically updated.

According to the diet plan recommendation method provided by the embodiment of the application, the first Internet of things equipment adjusts the display sequence of the local menu according to the indication information which comes from the Internet of things server and carries the diet plan, and preferentially displays the diet plan, so that when the user cooks initially or looks up the menu, the first Internet of things equipment preferentially displays the diet plan, the user can conveniently and rapidly know the diet plan in a future period of time, and the purpose of accurately recommending the menu is achieved.

Optionally, in the embodiment, when the user cooks, the user sends the cooking instruction to the first internet of things device through the client on the mobile phone, or the user sends the cooking instruction to the first internet of things device in a manner of clicking or touching a key on the first internet of things device, where the cooking instruction is used to instruct the first internet of things device to cook the food. After the cooking instruction is identified by the first internet of things device, if the cooking instruction indicates that the first internet of things device cooks the gourmet corresponding to the diet plan, the first internet of things device outputs prompt information in an animation or voice mode to prompt a user of advantages of the diet plan, such as alleviation of hair loss, improvement of oily skin and the like.

If the cooking instruction indicates that the first Internet of things device cooks gourmets out of the diet plan, the first Internet of things device outputs prompt information in an animation or voice mode to prompt a user to cook defects of the menu, such as acceleration of hair loss, greasy skin and the like.

By adopting the scheme, the user experience is improved by prompting the advantages or the disadvantages of the menu currently cooked by the user.

The following describes the recommendation method in the embodiment of the present application in detail with two specific scenarios.

Scene one: first thing networking device is intelligent cooking machine, second thing networking device is the beauty instrument.

The diet plan recommendation process includes the following steps:

firstly, acquiring a historical menu record.

A23-year-old miss has an intelligent food processor and a beauty instrument in three families. And inputting family members, ages, hobbies and the like in the process of logging in the intelligent food processor by Zhang III. Zhang three culinary art back at every turn, intelligent cooking machine uploads the culinary art log to thing networking server. The internet of things server analyzes the cooking logs of the user in the past period of time, such as the past month, one week, 10 days or the past five days, obtains a historical menu record, and stores the historical menu record into the database. And then, after a new cooking log is obtained each time, recording related information of the menu corresponding to the cooking log into a historical menu record, and continuously updating and maintaining the historical menu record.

The server of the Internet of things analyzes the historical menu records regularly and analyzes the intake of various nutrient components. The analytical procedure was as follows:

protein ═ meat (unit g) × N% + egg (unit g) × M% + fish (unit g) × P%;

fat ═ meat x D%;

carbohydrate (staple food (rice/noodle/bread) × E% + beans (bean curd/mung bean) × F% + vegetables (cabbage/green vegetable) × G%;

animal viscera (pig liver/pig heart) H% + egg yolk (salted duck egg/egg yolk) I%;

dietary fiber ═ vegetable (cabbage/green vegetable) × + J% + coarse grain (oatmeal/sweet potato) × K%;

vitamins ═ beans (tofu/soymilk) × + L% + vegetables (carrot/cauliflower) × + Q%;

trace elements ═ bones (spareribs/mutton chops) ×% O + seafood (fish/shrimp) × R%.

After the internet of things server determines the total amount of various nutritional ingredients input by the user in a past period of time, the internet of things server further calculates the average first intake of each user to various nutritional ingredients every day. And then comparing the first intake amount with the standard intake amount to obtain a first difference value corresponding to each nutrient component.

And secondly, acquiring the current state data of Zhang III.

When the diet plan is determined every time, the internet of things server determines a first difference value corresponding to each nutrient component, and meanwhile, the internet of things server acquires the current state data of the user, wherein the state data is acquired by a beauty instrument and uploaded to the internet of things server. And according to the current state data of the user, the Internet of things server determines that Zhangsan belongs to oily skin. There are many causes of oily skin, but food is one of the important factors affecting skin type. The skin condition can be improved by taking more food materials rich in dietary fiber.

It should be noted that, the above-mentioned process of acquiring the historical menu records and the process of acquiring the current status data of the user are not in strict sequence.

And thirdly, comprehensively analyzing the historical menu records and the current state data of the user.

The server of the Internet of things analyzes historical menu records of Zhang III to discover: the three-flavored vegetable recipe for the latest period of time comprises braised spareribs in soy sauce, braised mutton chops in soy sauce, braised meat in soy sauce, braised prawns in oil and the like. And determining first difference values of various nutritional components according to the recipes in the historical recipe record, and further determining that Zhang III is lack of dietary fibers, vitamins and alcohols recently and excessive intake of fat and protein.

The server of the internet of things analyzes the current state data of the user to find: zhang III is recently oily skin and lacks dietary fiber.

Therefore, the internet of things server determines that the nutritional ingredients required to be supplemented by the user Zhang III are dietary fibers, vitamins and alcohols, and excessive nutritional ingredients including fat and protein are ingested. And traversing each menu in the follow-up menus by the server of the Internet of things, screening out the menus with high dietary fiber, low protein and low fat, combining the menus to generate a diet plan for 3 days, 5 days, 10 days or even one month in the future, and pushing the diet plan to the intelligent food processor.

And when the intelligent food processor is started again by Zhang III, the diet plan in the diet plan is preferentially displayed on the display screen of the intelligent food processor. Zhang III can select diet plan, and also can select other recipes. When she selects other recipes, the intelligent processor prompts her to: the nutrient components of the recipe are not good for improving her current skin condition.

In addition, the internet of things server can draw a trend chart of the moisture and the oil content of the skin and send the trend chart to the terminal equipment of the user, so that the user can visually see that: the effect of diet program on the state of skin. For example, the internet of things server determines a historical menu record according to a cooking log of the last 30 days (1 month and 1 day to 1 month and 30 days), determines skin symptoms of the user according to current state data of the 1 month and 30 days, determines a diet plan of the future 30 days (1 month and 31-2 months and 29 days) according to the skin symptoms and the historical menu record, and pushes the diet plan to the intelligent food processor. In the next 30 days, the Internet of things server acquires the state data of the user every day, and draws a trend chart of skin moisture and oil content according to the state data, so that the user can visually know the influence of diet on the skin state.

Scene two: first thing networking device is intelligent cooking machine, second thing networking device is the hairdressing comb.

The diet plan recommendation process includes the following steps:

firstly, acquiring a historical menu record.

The 48-year-old plum family has an intelligent food processor and a hair comb.

The acquisition mode of the historical menu record can be referred to the acquisition mode in the first scenario, and details are not repeated here.

And secondly, acquiring the current state data of Liqu.

The thing networking server can determine with intelligent cooking border in other physical net equipment of same thing networking, like intelligent hairdressing comb. The server of the internet of things detects that the hairdressing comb is on line, and obtains state data of a user, wherein the state data is an image collected by a camera of the hairdressing comb. The internet of things server analyzes the image discovery: li IV is a person with thin hair, and has hair loss frequently. The reason for the thinning of hair is because the body lacks proteins, trace elements and vitamins, and this condition can be improved by dietary habits.

And thirdly, comprehensively analyzing the historical menu records and the current state data of the user.

The Internet of things server analyzes the historical menu record discovery of Liqu: the menu of the last period of time of lie four includes: dried pan shrimp, fat beef meal, shredded pork with miso, etc. And determining first difference values of various nutritional components according to the menus in the historical menu records, and further determining that the plum four is lack of vitamins and trace elements recently and the intake of fat is excessive.

The server of the internet of things analyzes the current state data of the user to discover: zhang san often fails to hair, lacks vitamins and trace elements, and has excessive intake of fat.

Therefore, the server of the Internet of things determines that the nutritional ingredients needing to be supplemented by the user LiIV are vitamins and trace elements, and excessive nutritional ingredients including fat are taken. And traversing each menu in the follow-up menus by the server of the Internet of things, screening out the menus with high vitamins, high trace elements and less fat, combining the menus to generate a diet plan for 3 days, 5 days, 10 days or even one month in the future, and pushing the diet plan to the intelligent food processor.

When the intelligent food processor is turned on again, the diet plan in the diet plan is preferentially displayed on the display screen of the intelligent food processor. The fourth plum can be used for selecting a diet plan and also can be used for selecting other recipes. When she selects other recipes, the intelligent processor prompts her to: the nutrient components of the recipe are not good for improving her current skin condition.

In the embodiment, the purpose of accurate recommendation is achieved by recommending the personalized menu suitable for the user through the skin symptom and the hair symptom and combining the historical menu record.

In the above embodiment, the electronic device determines and outputs the diet plan according to the historical menu records and the current state data. Wherein the diet plan can be a diet plan for a plurality of days or a month in the future. However, users may prefer to schedule a diet by themselves. Therefore, the embodiment of the application also provides a menu recommendation method, wherein the electronic device acquires a historical menu record of cooking of a user through a first internet of things device, acquires current state data of the user through a second internet of things device, and the first internet of things device and the second internet of things device are in the same internet of things. And then, determining and outputting a target menu according to the historical menu record and the state data. In this way, the user can flexibly schedule a daily or every ton diet plan according to a plurality of target recipes.

In addition, in the above embodiment, since the current state data of the user is not constant, the state data detected each time may be changed due to the user's recent sleep, diet, water intake, whether to work outdoors, and the like. For this reason, after the electronic device acquires new state data each time, a target menu is determined according to the history menu records and the latest state data, the target menu may be a menu for eating one or more times in the future, for example, if the acquisition time point of the latest state data is 9 am, the target menu is a menu for suggesting lunch and/or dinner of the user, and the like.

The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.

Fig. 5 is a schematic diagram of a diet plan recommending apparatus according to an embodiment of the present application. The diet plan recommending apparatus 500 includes: a first acquisition module 51, a second acquisition module 52, a processing module 53 and an output module 54.

The first obtaining module 51 is configured to obtain a historical recipe record of cooking by a user through a first internet of things device;

a second obtaining module 52, configured to obtain current status data of the user;

a processing module 53, configured to determine a diet plan according to the historical recipe record and the status data;

an output module 54 for outputting the diet plan.

In a possible implementation, the processing module 53 is configured to determine a first intake of various nutritional components by the user per day according to the historical recipe record; determining a second intake of the various nutritional components by the user per day based on the status data; determining target intake amounts of various nutritional ingredients according to the first intake amount, the second intake amount and a preset standard intake amount; determining the dietary plan based on the target intakes of the various nutritional components.

In a possible implementation manner, the processing module 53, when determining the target intake of each nutritional component according to the first intake, the second intake and a preset standard intake, is configured to determine, for each of the various nutritional components, a first difference value according to the first intake and the standard intake; determining a second difference based on the second intake and the standard intake; determining a target intake of the nutritional component based on the standard intake, the first difference, and the second difference.

In a possible implementation manner, when determining the diet plan according to the target intake of each nutritional component, the processing module 53 is configured to classify each menu in the set of menus according to the main nutritional component to obtain a plurality of menu categories, determine the nutritional component lacking from the user according to the target intake of each nutritional component, determine a menu category corresponding to the nutritional component lacking from the plurality of menu categories, and determine the diet plan according to the menu category.

In one possible implementation, when the processing module 53 determines the second daily intake of the various nutritional components by the user according to the status data, it is configured to determine the skin type of the user according to the status data, and determine the second daily intake of the various nutritional components by the user according to the skin type.

In a possible implementation, the processing module 53 is configured to determine a current symptom of the user according to the status data, where the current symptom includes a skin symptom and/or a hair symptom; querying a mapping table according to the current symptom to determine a data structure corresponding to the current symptom, where the data structure includes at least one of the following information: nutritional ingredients to be supplemented, suitable food materials, suitable seasonings, contraindicated food materials and contraindicated seasonings; determining the dietary plan based on the data structure and the historical recipe record.

In a possible implementation, before the processing module 53 queries a mapping table according to the current symptom to determine a data structure corresponding to the current symptom, the processing module is further configured to determine a plurality of candidate symptoms, and determine a corresponding data structure for each candidate symptom in the plurality of candidate symptoms to generate the mapping table.

In a possible implementation manner, the output module 54 is configured to send, to the first internet of things device, indication information indicating the diet plan; and/or sending indication information for indicating the diet plan to a terminal device, wherein the terminal device is a terminal device of a client side provided with the first internet of things device.

In a possible implementation manner, the second obtaining module 52 is configured to obtain the current state data of the user through a second internet-of-things device, where the first internet-of-things device and the second internet-of-things device are in the same internet of things; or acquiring a user image of the user, and acquiring the current state data of the user according to the user image; or, determining the current state data of the user according to the information input by the user.

In a possible implementation, the processing module 53 is further configured to identify cooking instructions after determining a diet plan according to the historical recipe record and the status data;

the output module is further configured to output prompt information to prompt the user of drawbacks of the dish indicated by the cooking instruction when the dish indicated by the cooking instruction is not the dish in the diet plan.

In a possible implementation manner, the processing module is further configured to adjust a menu display order according to the diet plan to preferentially display the menus included in the diet plan.

The diet plan recommending device provided by the embodiment of the application can execute the actions of the electronic equipment in the embodiment, the implementation principle and the technical effect are similar, and the details are not repeated.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 600 is, for example, the electronic device described above, and the electronic device may be an internet of things server, a first internet of things device, or a terminal device of a client installed with the first internet of things device, where the electronic device 600 includes:

a processor 61 and a memory 62;

the memory 62 stores computer instructions;

the processor 61 executes the computer instructions stored by the memory 62, causing the processor 61 to execute the diet plan recommendation method implemented by the internet of things device as described above; alternatively, the processor 61 is caused to execute the diet plan recommendation method implemented by the first internet of things device as described above.

For a specific implementation process of the processor 61, reference may be made to the above method embodiments, which implement the principle and the technical effect similarly, and this embodiment is not described herein again.

Optionally, the electronic device 600 further comprises a communication component 63. Wherein the processor 61, the memory 62 and the communication means 63 may be connected by a bus 64.

Embodiments of the present application further provide a computer-readable storage medium having stored therein computer instructions, which when executed by a processor, are used to implement a diet plan recommendation method implemented by an electronic device as described above.

Embodiments of the present application also provide a computer program product, which contains a computer program that, when executed by a processor, implements a diet plan recommendation method as implemented by an electronic device.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (13)

1. A method for dietary plan recommendation, comprising:

acquiring a historical menu record of cooking of a user through first Internet of things equipment;

acquiring the current state data of the user;

determining a diet plan according to the historical menu records and the state data;

outputting the diet plan.

2. The method of claim 1, wherein determining a dietary plan based on the historical recipe record and the state data comprises:

determining a first daily intake of various nutritional components by the user according to the historical recipe record;

determining a second intake of the various nutritional components by the user per day based on the status data;

determining target intake amounts of various nutritional ingredients according to the first intake amount, the second intake amount and a preset standard intake amount;

determining the dietary plan based on the target intakes of the various nutritional components.

3. The method of claim 2, wherein determining a target intake of each nutrient based on the first intake, the second intake, and a predetermined standard intake comprises:

determining a first difference for each of the various nutritional components based on the first intake and the standard intake;

determining a second difference based on the second intake and the standard intake;

determining a target intake of the nutritional component based on the standard intake, the first difference, and the second difference.

4. The method of claim 2, wherein determining the dietary plan based on the target intakes of the various nutritional components comprises:

classifying each menu in the menu set according to the main nutrient components to obtain a plurality of menu categories;

determining nutritional components that the user lacks based on the target intake of the various nutritional components;

determining a menu category corresponding to the nutrient components lacking for the user from the plurality of menu categories;

determining the diet plan according to the recipe category.

5. The method of claim 2, wherein said determining a second intake of said various nutritional components by said user per day based on said status data comprises:

determining a skin type of the user from the status data;

determining a second intake of the various nutritional components by the user per day based on the skin type.

6. The method of claim 1, wherein determining a dietary plan based on the historical recipe record and the state data comprises:

determining a current symptom of the user from the status data, the current symptom comprising a skin symptom and/or a hair symptom;

querying a mapping table according to the current symptom to determine a data structure corresponding to the current symptom, where the data structure includes at least one of the following information: nutritional ingredients to be supplemented, suitable food materials, suitable seasonings, contraindicated food materials and contraindicated seasonings;

determining the dietary plan based on the data structure and the historical recipe record.

7. The method of claim 6, wherein prior to querying a mapping table according to the current symptom to determine a data structure corresponding to the current symptom, the method further comprises:

determining a plurality of candidate symptoms;

determining, for each candidate symptom of the plurality of candidate symptoms, a corresponding data structure to generate the mapping table.

8. The method of any one of claims 1-7, wherein the outputting the dietary plan comprises:

sending indication information for indicating the diet plan to the first Internet of things device;

and/or

And sending indication information for indicating the diet plan to a terminal device, wherein the terminal device is a terminal device of a client side provided with the first Internet of things device.

9. The method according to any one of claims 1-7, wherein said obtaining current status data of said user comprises:

acquiring the current state data of the user through a second internet-of-things device, wherein the first internet-of-things device and the second internet-of-things device are in the same internet of things;

or;

acquiring a user image of the user, and acquiring current state data of the user according to the user image;

or,

and determining the current state data of the user according to the information input by the user.

10. The method of any of claims 1-7, wherein after determining a dietary plan based on the historical recipe record and the status data, further comprising:

recognizing a cooking instruction;

when the dish indicated by the cooking instruction is not the dish in the diet plan, outputting prompt information to prompt the user about the defect of cooking the dish indicated by the cooking instruction.

11. The method of any one of claims 1-7, further comprising:

and adjusting the menu display sequence according to the diet plan so as to display the menus contained in the diet plan preferentially.

12. A method for menu recommendation, the method comprising:

acquiring a historical menu record of cooking of a user through first Internet of things equipment;

acquiring the current state data of the user through a second internet-of-things device, wherein the first internet-of-things device and the second internet-of-things device are in the same internet of things;

determining a target menu according to the historical menu records and the state data;

and outputting the target menu.

13. A dietary plan recommendation device, comprising:

the first obtaining module is used for obtaining a historical menu record of cooking of a user through the first Internet of things equipment;

the second acquisition module is used for acquiring the current state data of the user;

the processing module is used for determining a diet plan according to the historical menu records and the state data;

an output module for outputting the diet plan.

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