CN111028918A - Menu recommendation method and device, storage medium and kitchen appliance - Google Patents
Menu recommendation method and device, storage medium and kitchen appliance Download PDFInfo
- Publication number
- CN111028918A CN111028918A CN201911355030.3A CN201911355030A CN111028918A CN 111028918 A CN111028918 A CN 111028918A CN 201911355030 A CN201911355030 A CN 201911355030A CN 111028918 A CN111028918 A CN 111028918A
- Authority
- CN
- China
- Prior art keywords
- user
- food material
- food
- tag
- label
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 235000013305 food Nutrition 0.000 claims abstract description 257
- 239000000463 material Substances 0.000 claims abstract description 236
- 235000015097 nutrients Nutrition 0.000 claims abstract description 83
- 235000016709 nutrition Nutrition 0.000 claims abstract description 33
- 230000035764 nutrition Effects 0.000 claims description 29
- 238000004590 computer program Methods 0.000 claims description 9
- 235000002597 Solanum melongena Nutrition 0.000 description 16
- 244000061458 Solanum melongena Species 0.000 description 16
- 235000013311 vegetables Nutrition 0.000 description 16
- 235000015278 beef Nutrition 0.000 description 13
- 244000061456 Solanum tuberosum Species 0.000 description 9
- 235000002595 Solanum tuberosum Nutrition 0.000 description 9
- 235000013372 meat Nutrition 0.000 description 9
- 235000013330 chicken meat Nutrition 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 241000287828 Gallus gallus Species 0.000 description 6
- 241000220259 Raphanus Species 0.000 description 5
- 235000006140 Raphanus sativus var sativus Nutrition 0.000 description 5
- 235000012631 food intake Nutrition 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 235000011299 Brassica oleracea var botrytis Nutrition 0.000 description 4
- 235000017647 Brassica oleracea var italica Nutrition 0.000 description 4
- 240000003259 Brassica oleracea var. botrytis Species 0.000 description 4
- 240000008436 Ipomoea aquatica Species 0.000 description 4
- 235000019004 Ipomoea aquatica Nutrition 0.000 description 4
- 238000010411 cooking Methods 0.000 description 4
- 230000037406 food intake Effects 0.000 description 4
- 235000019640 taste Nutrition 0.000 description 4
- 235000002566 Capsicum Nutrition 0.000 description 3
- 239000006002 Pepper Substances 0.000 description 3
- 241000722363 Piper Species 0.000 description 3
- 235000016761 Piper aduncum Nutrition 0.000 description 3
- 235000017804 Piper guineense Nutrition 0.000 description 3
- 235000008184 Piper nigrum Nutrition 0.000 description 3
- 235000021186 dishes Nutrition 0.000 description 3
- 235000012015 potatoes Nutrition 0.000 description 3
- 235000019643 salty taste Nutrition 0.000 description 3
- 235000002568 Capsicum frutescens Nutrition 0.000 description 2
- 235000019658 bitter taste Nutrition 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 235000019604 hot taste sensations Nutrition 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 235000015277 pork Nutrition 0.000 description 2
- 235000019614 sour taste Nutrition 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 240000007124 Brassica oleracea Species 0.000 description 1
- 235000003899 Brassica oleracea var acephala Nutrition 0.000 description 1
- 235000011301 Brassica oleracea var capitata Nutrition 0.000 description 1
- 235000001169 Brassica oleracea var oleracea Nutrition 0.000 description 1
- 235000008534 Capsicum annuum var annuum Nutrition 0.000 description 1
- 240000008574 Capsicum frutescens Species 0.000 description 1
- 208000008589 Obesity Diseases 0.000 description 1
- 241001247145 Sebastes goodei Species 0.000 description 1
- 230000037208 balanced nutrition Effects 0.000 description 1
- 235000019046 balanced nutrition Nutrition 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000019680 high-energy food Nutrition 0.000 description 1
- 235000013622 meat product Nutrition 0.000 description 1
- 235000020824 obesity Nutrition 0.000 description 1
- 235000013606 potato chips Nutrition 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 235000013547 stew Nutrition 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Nutrition Science (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of smart home, in particular to a menu recommendation method, a menu recommendation device, a storage medium and a kitchen appliance, wherein the method comprises the following steps: acquiring the total nutrient amount corresponding to the user according to the user label of the user and a first preset relationship, wherein the user label represents the information of the user, and the first preset relationship is the corresponding relationship between the user label and the total nutrient amount; acquiring a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and a second preset relation, wherein the second preset relation is the corresponding relation among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and the food material coefficient, and the food material coefficient represents the using amount of the food material; according to the food material coefficient, obtaining food materials in the food material category corresponding to the food material coefficient; a recommended menu is generated according to food materials, so that the technical problem that the recommended menu does not meet the nutritional requirements of users in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of smart home, in particular to a menu recommendation method and device, a storage medium and a kitchen appliance.
Background
Along with the development of the intelligent home technology, the intelligent cooking kitchen electricity is more and more widely appeared in the life of people, and the menu recommendation function that the intelligent cooking kitchen electricity possesses provides more choices for the user to cook. In the existing menu recommendation function, a menu is recommended according to food materials input by a user, so that the user is guided to cook.
In the menu recommendation method in the prior art, the menu is recommended only according to the food materials input by the user, and whether the recommended menu meets the nutritional requirements of the user is not considered, so that the menu recommended according to the prior art may affect the physical health of the user.
Disclosure of Invention
Aiming at the problems, the invention provides a menu recommendation method, a menu recommendation device, a storage medium and a kitchen appliance, so as to solve the technical problem that a menu recommended in the prior art does not meet the nutritional requirements of a user.
In a first aspect, the present invention provides a recipe recommendation method, where food material categories are multiple categories, each food material category includes multiple food materials, the method includes:
acquiring the total nutrient amount corresponding to a user according to a user tag of the user and a first preset relationship, wherein the user tag represents the information of the user, and the first preset relationship is the corresponding relationship between the user tag and the total nutrient amount;
acquiring a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and a second preset relation, wherein the second preset relation is the corresponding relation among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and the food material coefficient, and the food material coefficient represents the using amount of the food material;
according to the food material coefficient, obtaining food materials in the food material category corresponding to the food material coefficient;
and generating a recommended menu according to the food materials.
Optionally, the user tags include a gender tag, an age tag, and a weight tag; the step of obtaining the total amount of nutrition corresponding to the user according to the user label of the user and the first preset relationship comprises the following steps:
and acquiring the total nutrient amount corresponding to the user according to the sex label, the age label and the weight label of the user and the first preset relationship, wherein the first preset relationship is the corresponding relationship among the sex label, the age label, the weight label and the total nutrient amount according to the user.
Optionally, the step of obtaining the food materials in the food material categories corresponding to the food material coefficients according to the food material coefficients includes:
determining the food material category corresponding to the food material coefficient according to the food material coefficient;
and determining the food materials corresponding to the food material categories according to the food material labels and the recommendation coefficients corresponding to the food material labels.
Optionally, the food material tags include season tags, favorite value tags, and difficulty tags.
Optionally, the method further comprises: and receiving a dish instruction input by the user through a display interface, generating a menu according to the dish instruction, and sending the menu to the display interface for the user to use.
In a second aspect, the present invention further provides a menu recommending apparatus, where the food material categories are multiple categories, each category of food material includes multiple food materials, the apparatus includes:
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring the total nutrient amount corresponding to a user according to a user tag of the user and a first preset relationship, the user tag represents the information of the user, and the first preset relationship is the corresponding relationship between the user tag and the total nutrient amount;
the obtaining module is further configured to obtain a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and a second preset relationship, where the second preset relationship is a corresponding relationship among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and the food material coefficient represents the consumption of the food material;
the obtaining module is further configured to obtain food materials in the food material categories corresponding to the food material coefficients according to the food material coefficients;
and the generating module is used for generating a recommended menu according to the food materials.
Optionally, the user tags include a gender tag, an age tag, and a weight tag; the obtaining module is used for obtaining the nutrition total amount corresponding to the user according to the user label and the first preset relation of the user, and is specifically used for obtaining the nutrition total amount corresponding to the user according to the sex label, the age label and the weight label of the user and the first preset relation, wherein the first preset relation is the basis of the corresponding relation among the sex label, the age label, the weight label and the nutrition total amount of the user.
Optionally, the obtaining module is further configured to, when obtaining the food material in the food material category corresponding to the food material coefficient according to the food material coefficient, specifically,
determining the food material category corresponding to the food material coefficient according to the food material coefficient;
and determining the food materials corresponding to the food material categories according to the food material labels and the recommendation coefficients corresponding to the food material labels.
In a third aspect, the present invention further provides a storage medium storing a computer program, where the storage medium, when executed by one or more processors, implements the recipe recommendation method according to the first aspect.
In a fourth aspect, the present invention further provides a kitchen appliance, which is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to execute the recipe recommendation method according to the first aspect.
The invention provides a recipe recommendation method, a device, a storage medium and a kitchen appliance, which obtain the total nutrition amount corresponding to a user, namely the total nutrition amount required by the user, through a user tag and a first preset relationship, wherein the user tag represents the information of the user, the first preset relationship is the corresponding relationship between the user tag and the total nutrition amount, food material coefficients corresponding to each type of food materials are obtained according to the total nutrition amount corresponding to the user, the food quantity input by the user in advance and a second preset relationship, the second preset relationship is the corresponding relationship between the total nutrition amount corresponding to the user, the food quantity input by the user in advance and the food material coefficients, the food material coefficients represent the consumption of the food materials, food materials in the category corresponding to the food material coefficients are obtained according to the food material coefficients, the obtained food material nutrition amount is ensured to be equal to the total nutrition amount corresponding to the user, and a recommended recipe is generated according to the food materials, the generated menu is ensured to meet the nutrition requirement of the user, thereby ensuring the body health of the user.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings:
fig. 1 is a schematic flow chart of a menu recommendation method according to an embodiment of the present invention;
fig. 2 is another schematic flow chart of a menu recommendation method according to an embodiment of the present invention;
fig. 3 is another schematic flow chart of a menu recommendation method according to an embodiment of the present invention;
fig. 4 is a connection block diagram of a menu recommendation device according to an embodiment of the present invention.
Reference numerals: 101-an acquisition module; 102-generating module.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments of the present invention and the features of the embodiments can be combined with each other without conflict, and the formed technical solutions are within the scope of the present invention.
Example one
The embodiment provides a menu recommending method for ensuring the health of a user, which can be used on kitchen appliances, such as a range hood, and the user cooks dishes according to a menu displayed on a display interface on the range hood; the method can also be used on equipment such as a mobile terminal and a computer of a user, and the user can cook dishes through a menu displayed by the mobile terminal or the computer; the method can also be used on a takeout platform, and takeout dishes are recommended according to the menu recommended by the method. Specifically, fig. 1 is a schematic flow chart of a menu recommendation method according to an embodiment of the present invention, and referring to fig. 1, the present invention provides a menu recommendation method, where food material categories are multiple categories, each category of food material category includes multiple food materials, and the method includes the following steps:
step S1, obtaining the total nutrient amount corresponding to the user according to the user label of the user and a first preset relationship, where the user label represents information of the user, and the first preset relationship is a corresponding relationship between the user label and the total nutrient amount.
It is understood that the total amount of nutrients corresponding to the obtained user is the total amount of nutrients required by the user.
Step S2, obtaining a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and a second preset relationship, where the second preset relationship is a corresponding relationship among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and the food material coefficient represents the consumption of the food material.
Alternatively, the food material category may be meat, vegetables, etc., and the second preset relationship may be represented by formula (1-1) and formula (1-2).
y＝k1x1+k2x2 (1-1)
k＝k1+k2 (1-2)
Wherein y is the total nutrient amount, k1 is the food material coefficient corresponding to meat, x1 is the nutrient amount corresponding to each meat food material, k2 is the food material coefficient corresponding to vegetables, x2 is the nutrient amount corresponding to each vegetable food material, and k is the food intake of the user.
For example, user a corresponds to a total nutrient amount of 1250 kcal (kcal is a caloric unit) and user a has a food intake of 3 units; the total amount of nutrients for user B is 1250 kcal (kcal is caloric unit) and the food intake for user B is 4 units. When the total amount of nutrients required by the user a and the user B is the same, the amount of food material required by the user B is large when the food consumption of the user B is large relative to the user a, and therefore, the amount of food material allocated to the user B is large for low energy food materials and small for high energy food materials.
If the energy corresponding to a meat food is 500 kcal and the energy corresponding to a vegetable food is 250 kcal, the total amount of nutrients of the user a can be expressed as follows based on the formula (1-1) and the formula (1-2) in combination with the above example: 1250 is 2 is 500 is 250, that is, the food material coefficient corresponding to the user a is k1 is 2, and k2 is 1, it can be understood that for the user a, the recommended meat food material is 2 parts, and the recommended vegetable food material is 1 part. Similarly, the total amount of nutrients for user B may be expressed as: 1250 is 1 is 500 is 250, that is, the food material coefficient corresponding to the user B is k1 is 1, and k2 is 3, it can be understood that for the user B, the recommended meat food material is 1 part, and the recommended vegetable food material is 3 parts.
Step S3, according to the food material coefficients, obtaining the food materials in the food material categories corresponding to the food material coefficients.
Optionally, in order to ensure that the recommended food materials have low repeatability and thus ensure balanced nutrition intake by the user, the food materials recommended for the user may be randomly obtained from corresponding food material categories, for example, meat may include mutton, beef, chicken, pork, and the like, vegetables may include potatoes, hot peppers, eggplants, broccoli, and the like, and a correspondence between the food material categories and the food materials may be established, and optionally, the correspondence may be a mapping table, for example, the mapping table between the food material categories and the food materials shown in table 1:
TABLE 1
| Meat product | Mutton | Beef | Chicken meat | Pork |
| Vegetable products | Potato | Chili pepper | Eggplant | Flower of West blueflower |
In connection with the above example, for user a, for example, 2 parts of meat-based food material may be beef and chicken in table 1, and 1 part of vegetable-based food material may be potato in table 1.
For example, for user B, the recommended meat food material is 1 part, and may be chicken in table 1, and the recommended vegetable food materials are potato, pepper, and broccoli.
Optionally, the recipe recommendation method provided in this embodiment may be used in combination with a fresh electric commerce platform, for example, in this step, the recommended food materials for the user a are one beef, one chicken and one potato, and the recommended food materials may be purchased by placing an order in the fresh electric commerce platform of the user.
And step S4, generating a recommended menu according to the food materials.
For example, for user a, the determined food materials are beef, chicken, and potatoes, and the recommended recipe may be chicken fried potato chips, beef stew potatoes.
In the recipe recommendation method provided by this embodiment, the total nutrient amount corresponding to the user, that is, the total nutrient amount required by the user, is obtained through the user tag and a first preset relationship, where the user tag represents information of the user, the first preset relationship is a corresponding relationship between the user tag and the total nutrient amount, a food material coefficient corresponding to each type of food material is obtained according to the total nutrient amount corresponding to the user, a food amount pre-input by the user, and a second preset relationship, where the second preset relationship is a corresponding relationship between the total nutrient amount corresponding to the user, a food amount pre-input by the user, and a food material coefficient represents a consumption of the food material, a food material in a food material category corresponding to the food material coefficient is obtained according to the food material coefficient, it is ensured that the obtained total nutrient amount of the food material is equivalent to the total nutrient amount corresponding to the user, a recommended recipe is generated according to generate the recommended recipe, on one hand, the generated recipe meets the requirement of nutrition, on the other hand, obesity caused by too much food intake and weight increase of the user is avoided, so that the nutrition and intake of the user are both guaranteed, and the physical health of the user is guaranteed.
Example two
Optionally, on the basis of the first embodiment, in this embodiment, the user tag includes a gender tag, an age tag, and a weight tag, and this embodiment provides a menu recommendation method for calculating a total amount of nutrition corresponding to the user, specifically, fig. 2 is another flow diagram of the menu recommendation method provided in the embodiment of the present invention, please refer to fig. 2, and step S1 includes:
and a substep S11 of obtaining the total amount of nutrition corresponding to the user according to the gender label, the age label, the weight label and the first preset relationship of the user.
The first preset relationship is a corresponding relationship among a gender label, an age label, a weight label and a total nutrient amount according to a user.
Alternatively, the total nutrient amount may be obtained by the formula (1-3):
y＝k3x3+z (1-3)
wherein y is total nutrient, k3 is nutrient coefficient, x3 is weight label (unit is kilogram), and z is nutrient constant.
When the user is female, the correspondence between the age label, the nutrition coefficient k3, and the nutrition constant z may be as shown in table 2:
TABLE 2
| Age label | K3 | z |
| 18 to 30 years old | 14.6 | 450 |
| Age 31-60 years old | 8.6 | 830 |
| Over 60 years old | 10.4 | 600 |
When the user is male, the correspondence between the age label, the nutrition coefficient k3, and the nutrition constant z may be as shown in table 3:
TABLE 3
| Age label | K3 | z |
| 18 to 30 years old | 15.2 | 680 |
| Age 31-60 years old | 11.5 | 830 |
| Over 60 years old | 13.4 | 490 |
For example, in the case of a female with a user of 47 kg and an age of 25, as shown in table 2, the nutrition coefficient k3 for the user is 14.6, the nutrition constant z is 450, and the total nutrition amount y ═ 14.6 ×, 47+450 ═ 1136.2 kcal for the user can be obtained through the formula (1-3).
Further, taking a male with a user of 65 kg and an age of 25 as an example, it can be seen from table 3 that the nutrition factor k3 for the user is 15.2, the nutrition constant z is 680, and the total amount of nutrition y of the user is 15.2 x 65+680 1668 kcal according to the formula (1-3).
It should be noted that the method can be used for individual users, and the total amount of nutrition of the users can be obtained through the formula (1-3); the method can also be used for family users, and the total nutrient amount required in the family can be calculated by calculating the total nutrient amount of each family member through the formula (1-3) and then summing the total nutrient amount.
EXAMPLE III
Fig. 3 is another flowchart of a menu recommendation method according to an embodiment of the present invention, please refer to fig. 3, and step S3 includes:
and the substep S31, determining the food material category corresponding to the food material coefficient according to the food material coefficient.
Continuing with the example of table 1 in the first embodiment, for user a, where k2 is 1, 1 part of vegetable food material is determined, i.e., 1 part of food material is selected from the vegetables in table 1.
As can be seen from table 1, the vegetables include a plurality of food materials, and in the sub-step S32, the specific steps of determining the food material from one food material category are specifically shown as follows:
and a substep S32 of determining the food material corresponding to the food material category according to the food material labels and the recommendation coefficients corresponding to the food material labels.
Optionally, the food material tags include season tags, favorite value tags, and difficulty tags.
The season label can be a season corresponding to the food material, for example, the season label corresponding to the eggplant is summer, and the season label corresponding to the cabbage is winter. The recommendation coefficient corresponding to the seasonal label is not fixed, and specifically, when the current season is summer, the recommendation coefficient (for example, 0.5) corresponding to the eggplant is greater than the recommendation coefficient (for example, 0.1) corresponding to the broccoli; on the contrary, when the current season is winter, the recommendation coefficient corresponding to the eggplant is smaller than that corresponding to the broccoli.
The preference value label represents the degree of the recipe accepted by the user, for example, when the user accepts the recommended recipe "beef stewed with eggplant", 1 preference value is added to the eggplant and the beef respectively. The recommendation coefficient corresponding to the food material with higher favorite value is large, and conversely, the recommendation coefficient corresponding to the food material with lower favorite value is small. For example, if the favorite value of an eggplant is 10 and the favorite value of a pepper is 2, the recommendation coefficient corresponding to the eggplant is greater than the recommendation coefficient corresponding to the pepper. Therefore, a corresponding coefficient can be set for the favorite value of the food material by the user, illustratively, the favorite value of the user is between 0 and 5, and the coefficient corresponding to the favorite value is 0.1; the user's favorite value is between 5 and 10, and the coefficient that favorite value corresponds is 0.2. The coefficient corresponding to the favorite value may be set according to the specific use condition of the user, and is not limited to 0.1 or 0.2, and may be other numerical values.
In addition, the favorite value labels may be accumulated in a manner that the user accepts the recommended recipes (for example, if the user accepts the recommended recipes, 1 favorite value corresponding to the recommended recipes is added), or may be accumulated in a manner that the user actively searches recipes (for example, if the user actively searches the recipes once, 1 favorite value corresponding to the recipes is added), specifically in the following manner:
receiving a dish instruction input by a user through the display interface, generating a menu according to the dish instruction, and sending the menu to the display interface for the user to use. For example, if the menu searched by the user through the display interface is "potato and beef", 1 favorite value is added to each of the potato and the beef.
The difficulty label is preset according to factors such as cooking skill, cutter difficulty, cooking time and the like. The difficulty label can be set to 1 star, 5 stars, etc. Optionally, the recommendation coefficient corresponding to the difficulty label being high is small, whereas the recommendation coefficient corresponding to the difficulty label being low is large, for example, the recommendation coefficient corresponding to the difficulty label being 1 star is 0.3, and the recommendation coefficient corresponding to the difficulty label being 5 stars is 0.1. For example, the difficulty label of the recipe "beef stewed with eggplant" is 5 stars, and the corresponding recommendation coefficient is 0.1. It should be noted that the recommendation coefficient corresponding to the difficulty label may also be other values, and is not limited herein.
Alternatively, step S32 may be implemented by equation (1-4):
E1＝wt*rt+wc*rc+wg*rg (1-4)
wherein E1 is the priority corresponding to the food material, wt is the season label, rt is the coefficient corresponding to the season label, wc is the favorite value label, rc is the coefficient corresponding to the favorite value label, wg is the difficulty label, and rg is the coefficient corresponding to the difficulty label.
For example, taking the current season as summer and calculating the priority of the eggplants in the vegetable, the priority of the eggplants is 1 × 0.5+1 × 0.2+5 × 0.1 ═ 1.2 as shown in the above example and formula (1-4), and if the corresponding priorities in the other vegetable food materials are all smaller than the priority of the eggplants of 1.2, the recommended food material in the vegetable food materials is determined to be an eggplant.
Example four
The embodiment also provides a menu recommendation method, which includes:
and acquiring menu information of each menu, and recommending the menus according to the menu information and the corresponding coefficients, wherein the menu information comprises season labels, user favorite values and taste labels. Alternatively, this can be achieved by equations (1-5):
E2＝wsrs+wbrb+∑wiri (1-5)
wherein, E2 is the priority corresponding to the menu, ws is the season label, rs is the coefficient corresponding to the season label, wc is the user's favorite value, rc is the coefficient corresponding to the user's favorite value, wi is the taste label, ri is the coefficient corresponding to the taste label.
Similarly to example three, the season label may be the season corresponding to the recipe, e.g. the recipe "radish stewed chops" corresponds to winter and the recipe "pan-fried water spinach" corresponds to summer. The coefficient corresponding to the season label is not fixed, specifically, when the current season is summer, the coefficient (0.3) corresponding to the 'fried and steamed stuffed bun with radish' is greater than the coefficient (0.1) corresponding to the 'fried and steamed stuffed bun with radish', and conversely, when the current season is winter, the coefficient (0.1) corresponding to the 'fried and steamed stuffed bun with radish' is less than the coefficient (0.3) corresponding to the 'fried and steamed stuffed bun with radish'.
The user's preference value indicates the degree of the user's acceptance of the recipe, for example, when the user accepts the recommended recipe "beef stewed with eggplant", 1 preference value is added to each of the eggplant and the beef. Exemplarily, the favorite value of the user is between 0 and 5, and the coefficient corresponding to the favorite value is 0.1; the user's favorite value is between 5 and 10, and the coefficient that favorite value corresponds is 0.2. For example, the user's preference for "pan-fried water spinach" is 9. The coefficient corresponding to the favorite value may be set according to the specific use condition of the user, and is not limited to 0.1 or 0.2, and may be other numerical values.
Taste labels may include sour, spicy, sweet, bitter, salty tastes. For example, in a menu used by the user, the number of sour taste is 1, the hot taste is 3, the bitter taste is 1, and the salty taste is 10, optionally, the coefficient corresponding to the sour taste is 0.1, the coefficient corresponding to the hot taste is 0.3, the coefficient corresponding to the bitter taste is 0.1, and the coefficient corresponding to the salty taste is 0.5.
Based on the formula (1-5) and in combination with the above example, when the current season is summer, the priority corresponding to the menu "pan-fried water spinach" is 1 × 0.3+1 × 0.9+1 × 0.5 ═ 1.7, and if the priorities of the rest of the menus are less than 1.7, the menu is recommended to be "pan-fried water spinach".
EXAMPLE five
Fig. 4 is a connection block diagram of a menu recommendation device according to an embodiment of the present invention, and as shown in fig. 4, the device includes an obtaining module 101 and a generating module 102.
The obtaining module 101 is configured to obtain the total nutrient amount corresponding to the user according to a user tag of the user and a first preset relationship, where the food material categories are multiple categories, each food material category includes multiple food materials, the user tag represents information of the user, and the first preset relationship is a corresponding relationship between the user tag and the total nutrient amount.
It is understood that the obtaining module 101 may be configured to execute step S1.
The obtaining module 101 is further configured to obtain a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and a second preset relationship, where the second preset relationship is a corresponding relationship among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and the food material coefficient represents the consumption of the food material.
It is understood that the obtaining module 101 may also be used for executing the step S2.
The obtaining module 101 is further configured to obtain food materials in the food material categories corresponding to the food material coefficients according to the food material coefficients.
It is understood that the obtaining module 101 may also be used for executing the step S3.
The generating module 102 is configured to generate a recommended recipe according to the food material.
It is understood that the generating module 102 may be used to execute step S4.
Optionally, the user tags include a gender tag, an age tag, and a weight tag; the obtaining module 101 is configured to, when obtaining the total nutrient amount corresponding to the user according to the user tag of the user and a first preset relationship, specifically, obtain the total nutrient amount corresponding to the user according to the sex tag, the age tag, the weight tag of the user and the first preset relationship, where the first preset relationship is a corresponding relationship between the sex tag, the age tag, the weight tag of the user and the total nutrient amount.
Optionally, the obtaining module 101 may be further configured to execute step S11.
Optionally, the obtaining module 101 is further configured to, when obtaining the food material in the food material category corresponding to the food material coefficient according to the food material coefficient, specifically, determine the food material category corresponding to the food material coefficient according to the food material coefficient; and determining the food materials corresponding to the food material categories according to the food material labels and the recommendation coefficients corresponding to the food material labels.
Optionally, the obtaining module 101 may be further configured to perform step S31 and step S32.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the obtaining module 101 and the generating module 102 may refer to corresponding processes in the foregoing method embodiments, and are not described herein again.
EXAMPLE six
The present embodiment provides a storage medium, where a computer program is stored, and when the storage medium is executed by one or more processors, the recipe recommendation method in any one of the first to fourth embodiments is implemented.
The storage medium may be a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc.
EXAMPLE seven
The present embodiment provides a kitchen appliance, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for recommending recipes in any one of the first to fourth embodiments is executed.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the recipe recommendation method in the first to fourth embodiments.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
To sum up, according to the recipe recommendation method, apparatus, storage medium and kitchen appliance provided by the present invention, the total nutrient amount corresponding to the user, that is, the total nutrient amount required by the user, is obtained through the user tag and the first preset relationship, where the user tag represents the information of the user, the first preset relationship is the corresponding relationship between the user tag and the total nutrient amount, and the food material coefficient corresponding to each type of food material is obtained according to the total nutrient amount corresponding to the user, the food amount pre-input by the user, and the second preset relationship is the corresponding relationship between the total nutrient amount corresponding to the user, the food amount pre-input by the user, and the food material coefficient, which represents the consumption of the food material, and the food material in the food material category corresponding to the food material coefficient is obtained according to the food material coefficient, so as to ensure that the obtained total nutrient amount of the food material is equivalent to the total nutrient amount corresponding to the user, and a recommended menu is generated according to the food materials, so that the generated menu is ensured to meet the nutritional requirements of the user, and the physical health of the user is ensured.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should be noted that, in the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A menu recommendation method is characterized in that food material categories are multiple categories, each food material category comprises multiple food materials, and the method comprises the following steps:
acquiring the total nutrient amount corresponding to a user according to a user tag of the user and a first preset relationship, wherein the user tag represents the information of the user, and the first preset relationship is the corresponding relationship between the user tag and the total nutrient amount;
acquiring a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and a second preset relation, wherein the second preset relation is the corresponding relation among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material and the food material coefficient, and the food material coefficient represents the using amount of the food material;
according to the food material coefficient, obtaining food materials in the food material category corresponding to the food material coefficient;
and generating a recommended menu according to the food materials.
2. The recipe recommendation method of claim 1, wherein the user tag comprises a gender tag, an age tag, and a weight tag; the step of obtaining the total amount of nutrition corresponding to the user according to the user label of the user and the first preset relationship comprises the following steps:
and acquiring the total nutrient amount corresponding to the user according to the sex label, the age label and the weight label of the user and the first preset relationship, wherein the first preset relationship is the corresponding relationship among the sex label, the age label, the weight label and the total nutrient amount according to the user.
3. The recipe recommendation method according to claim 1, wherein the step of obtaining the food material in the food material category corresponding to the food material coefficient according to the food material coefficient comprises:
determining the food material category corresponding to the food material coefficient according to the food material coefficient;
and determining the food materials corresponding to the food material categories according to the food material labels and the recommendation coefficients corresponding to the food material labels.
4. The recipe recommendation method according to claim 3, wherein the food material tags include a season tag, a favorite value tag, and a difficulty tag.
5. The recipe recommendation method as claimed in claim 1, further comprising:
and receiving a dish instruction input by the user through a display interface, generating a menu according to the dish instruction, and sending the menu to the display interface for the user to use.
6. A recipe recommending apparatus, characterized in that food material categories are plural categories, each food material category comprising plural food materials, the apparatus comprising:
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring the total nutrient amount corresponding to a user according to a user tag of the user and a first preset relationship, the user tag represents the information of the user, and the first preset relationship is the corresponding relationship between the user tag and the total nutrient amount;
the obtaining module is further configured to obtain a food material coefficient corresponding to each type of food material according to the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and a second preset relationship, where the second preset relationship is a corresponding relationship among the total nutrient amount corresponding to the user, the food quantity input by the user in advance, the nutrient amount of each type of food material, and the food material coefficient represents the consumption of the food material;
the obtaining module is further configured to obtain food materials in the food material categories corresponding to the food material coefficients according to the food material coefficients;
and the generating module is used for generating a recommended menu according to the food materials.
7. The recipe recommendation device according to claim 6, wherein the user tag comprises a gender tag, an age tag, and a weight tag; the obtaining module is used for obtaining the total amount of nutrition corresponding to the user according to the user label of the user and the first preset relation,
and acquiring the total nutrient amount corresponding to the user according to the sex label, the age label and the weight label of the user and the first preset relationship, wherein the first preset relationship is the corresponding relationship among the sex label, the age label, the weight label and the total nutrient amount according to the user.
8. The recipe recommending device according to claim 6, wherein the obtaining module is further configured to, when obtaining the food material in the food material category corresponding to the food material coefficient according to the food material coefficient, specifically,
determining the food material category corresponding to the food material coefficient according to the food material coefficient;
and determining the food materials corresponding to the food material categories according to the food material labels and the recommendation coefficients corresponding to the food material labels.
9. A storage medium storing a computer program, the storage medium implementing the recipe recommendation method according to any one of claims 1-5 when executed by one or more processors.
10. Kitchen appliance, characterized in that it comprises a memory and a processor, said memory having stored thereon a computer program which, when executed by said processor, performs a recipe recommendation method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911355030.3A CN111028918A (en) | 2019-12-25 | 2019-12-25 | Menu recommendation method and device, storage medium and kitchen appliance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911355030.3A CN111028918A (en) | 2019-12-25 | 2019-12-25 | Menu recommendation method and device, storage medium and kitchen appliance |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111028918A true CN111028918A (en) | 2020-04-17 |
Family
ID=70213127
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911355030.3A Pending CN111028918A (en) | 2019-12-25 | 2019-12-25 | Menu recommendation method and device, storage medium and kitchen appliance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111028918A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112102921A (en) * | 2020-09-27 | 2020-12-18 | 中国建设银行股份有限公司 | Method and device for generating nutritional recipes and server |
| CN114036386A (en) * | 2021-11-15 | 2022-02-11 | 珠海格力电器股份有限公司 | Diet recommendation method, electronic device and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060122468A1 (en) * | 2004-11-15 | 2006-06-08 | Mali Tavor Ltd. | Nutritional counseling method and server |
| WO2012047940A1 (en) * | 2010-10-04 | 2012-04-12 | Nabil Abujbara | Personal nutrition and wellness advisor |
| CN105701092A (en) * | 2014-11-24 | 2016-06-22 | ***通信集团公司 | Method and device for automatically recommending menu |
| US20180121631A1 (en) * | 2016-10-27 | 2018-05-03 | True Positive Analytics Pvt. Ltd. | Systems and methods for multi-parameter and personalized dietary recommendations |
| CN108198606A (en) * | 2017-12-29 | 2018-06-22 | 中粮营养健康研究院有限公司 | A kind of nutrient diet quantitative calculation method and a kind of nutrient health service plate |
| CN109243579A (en) * | 2018-10-10 | 2019-01-18 | 无锡市睿健时代贸易有限公司 | Processing method, system, storage medium and the terminal of prepared food nutrition data |
| CN109300524A (en) * | 2018-09-29 | 2019-02-01 | 深圳春沐源控股有限公司 | Information recommendation method and device |
| CN110265114A (en) * | 2019-06-21 | 2019-09-20 | 四川医枢科技股份有限公司 | Nutrition intelligent management system |
-
2019
- 2019-12-25 CN CN201911355030.3A patent/CN111028918A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060122468A1 (en) * | 2004-11-15 | 2006-06-08 | Mali Tavor Ltd. | Nutritional counseling method and server |
| WO2012047940A1 (en) * | 2010-10-04 | 2012-04-12 | Nabil Abujbara | Personal nutrition and wellness advisor |
| CN105701092A (en) * | 2014-11-24 | 2016-06-22 | ***通信集团公司 | Method and device for automatically recommending menu |
| US20180121631A1 (en) * | 2016-10-27 | 2018-05-03 | True Positive Analytics Pvt. Ltd. | Systems and methods for multi-parameter and personalized dietary recommendations |
| CN108198606A (en) * | 2017-12-29 | 2018-06-22 | 中粮营养健康研究院有限公司 | A kind of nutrient diet quantitative calculation method and a kind of nutrient health service plate |
| CN109300524A (en) * | 2018-09-29 | 2019-02-01 | 深圳春沐源控股有限公司 | Information recommendation method and device |
| CN109243579A (en) * | 2018-10-10 | 2019-01-18 | 无锡市睿健时代贸易有限公司 | Processing method, system, storage medium and the terminal of prepared food nutrition data |
| CN110265114A (en) * | 2019-06-21 | 2019-09-20 | 四川医枢科技股份有限公司 | Nutrition intelligent management system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112102921A (en) * | 2020-09-27 | 2020-12-18 | 中国建设银行股份有限公司 | Method and device for generating nutritional recipes and server |
| CN114036386A (en) * | 2021-11-15 | 2022-02-11 | 珠海格力电器股份有限公司 | Diet recommendation method, electronic device and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20140089321A1 (en) | Method and system to recommend recipes | |
| CN109243579B (en) | Cooked food nutrition data processing method, system, storage medium and terminal | |
| CN108255084B (en) | Cooking control method based on central control equipment and kitchen housekeeper robot | |
| CN110287306B (en) | Recipe recommendation method and equipment | |
| CN110706131A (en) | Method and device for creating electronic menu, electronic equipment and storage medium | |
| CN111028918A (en) | Menu recommendation method and device, storage medium and kitchen appliance | |
| JP2012003319A (en) | Menu determination support device, menu determination support program, and memory medium for storing menu determination support program | |
| CN109214955A (en) | The generation method and device of food product set meal | |
| CN109710855A (en) | A kind of method, apparatus, cooking equipment and the storage medium of determining menu | |
| JP6410069B1 (en) | Recipe information providing apparatus, recipe information providing method, and recipe information providing program | |
| CN108492861A (en) | Accurate diet system for prompting and method | |
| KR101692299B1 (en) | Method and Apparatus for providing a recommended dinner menu | |
| CN110716967A (en) | Intelligent menu recommendation method, household appliance and computer readable storage medium | |
| JP2019133624A (en) | Recipe information provision apparatus, recipe information provision method, and recipe information provision program | |
| WO2012116395A1 (en) | A system and a method for generating a meal plan | |
| CN115587245A (en) | Menu list recommendation method and device, storage medium and electronic device | |
| CN112700842B (en) | Household diet scheme recommendation method | |
| CN115062194A (en) | Menu recommendation method and device | |
| CN112232917A (en) | Commodity recommendation method, device and equipment for e-commerce platform | |
| CN111863192A (en) | Method, device and equipment for recommending recipes | |
| WO2011037221A1 (en) | Menu determination support device, menu determination support program, and storage medium in which menu determination support program is stored | |
| CN113127709A (en) | Method and device for menu recommendation and refrigerator | |
| CN113378042B (en) | Ordering method and device based on air conditioner and intelligent air conditioner | |
| CN113888247A (en) | Catering order processing method and system, storage medium and electronic device | |
| CN112493897B (en) | Dosing method, dosing machine and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200417 |
|
| RJ01 | Rejection of invention patent application after publication |