CN111125516A - Intelligent menu recommendation method and system based on image recognition - Google Patents
Intelligent menu recommendation method and system based on image recognition Download PDFInfo
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Abstract
The invention discloses an intelligent menu recommendation method based on image recognition, which comprises the following steps of: step 1: obtaining food material images or pictures; step 2: identifying attributes of all food materials; and step 3: judging whether the virtual food material library has the existing food materials or not, and arranging and combining the food materials; and 4, step 4: detecting a menu; and 5: contraindication detection is carried out, and permutation combinations violating contraindications are abandoned; step 6: matching cooking recipes; and 7: displaying a menu; and 8: completing individual preference screening and maximum matching screening, obtaining food materials lacking in the menu, and displaying the result of secondary screening; and step 9: and determining a menu and starting cooking. The invention further provides an intelligent menu recommendation system based on image recognition, which comprises a mobile device with a camera module, a processing module and a communication module, and a cloud data storage module. According to the method, the food materials are identified through an image identification algorithm, customized inspection and screening are carried out aiming at specific users, and the most appropriate menu is recommended.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of catering, in particular to the technical field of an intelligent menu recommendation method and system based on image recognition.
[ background of the invention ]
The Chinese catering is a sunrise industry, is in the developing transformation period, transforms from experience management to fine management, and transforms from loose management to large-scale operation, and in the process, a large number of third-party service organizations are needed. The electronic menu is integrated into a catering management information system (intelligent menu) and is an indispensable data platform in the transformation of the catering industry. Not only so, with the concern of people on the healthy diet, more and more families learn to cook through the cell-phone.
In the prior art, some intelligent recipes exist, but a plurality of defects exist. In the prior art, only the food materials before the identification are limited, the recipe recommendation cannot be carried out according to the existing food materials, and the matching contraindication detection cannot be carried out on the identified food material matching. The prior art in the field can not screen the associated menu based on the identified food materials according to the personal favorite practice, taste, matched food material favorite and the like of the user to make customized recommendation. The art cannot filter recipes containing user dislikes or allergies, or violating collocation contraindications from the identified food material matching recipes. Therefore, a new intelligent recipe recommendation method and a system applying the method are needed to solve the above problems.
[ summary of the invention ]
The invention aims to solve the problems in the prior art, and provides an intelligent menu recommendation method and system based on image recognition, which can recognize food materials through an image recognition algorithm, check and screen customization of specific users and recommend the most appropriate menu.
In order to achieve the purpose, the invention provides an intelligent menu recommendation method based on image recognition, which comprises the following steps of:
step 1: obtaining food material images or pictures through mobile equipment;
step 2: according to the food material images or pictures, identifying the attributes of all food materials: identifying the Food material by image identification technology of object detection, wherein the identified Food material is Food ═ (F)AFBFC…) in which FAIs food material A;
and step 3: judging whether the virtual food material library has existing food materials or not, and if the existing unexpired food materials exist, arranging and combining the identified food materials and the food materials in the food material library by the system; if no ready-made food materials exist, the system carries out permutation and combination on the identified food materials;
and 4, step 4: detecting the menu of the permutation and combination of the food materials, and reserving the permutation and combination which can be used as the menu;
and 5: carrying out contraindication detection on the permutation and combination collocation by combining with user data, and giving up the permutation and combination violating the contraindication;
step 6: calculating the permutation and combination which meets the requirement, and matching the cooking menu for each combination;
and 7: displaying the matched menu on the mobile equipment from top to bottom according to the matching degree;
and 8: and finishing individual preference screening and maximum matching screening, obtaining the food materials lacking in the menu, and displaying the result of secondary screening: optimizing the dish allocation and combination based on taste preference according to the secondary screening requirement of the user; screening by combining a plurality of screening conditions, displaying each menu of the screening result, and simultaneously displaying the lacking food materials;
and step 9: and determining a menu and starting cooking.
Preferably, the image recognition technology in step 2 is a neural network algorithm.
Preferably, the method for establishing the virtual food material library in step 3 includes the following steps:
step 31: obtaining a food material image through a mobile device;
step 32: identifying the attribute of the food material according to the food material image;
step 33: the shelf life of the food materials is manually added.
Preferably, the user data in step 5 is data previously entered into the mobile device, and the user data includes a user disease history.
Preferably, the missing food material in the step 8 is the desired food material minus the existing food material.
The invention further provides an intelligent recipe recommendation system based on image recognition, which comprises a mobile device with a camera module, a processing module and a communication module, and a cloud data storage module, wherein the camera module is used for acquiring food material images, the processing module is used for processing the food material images, recipe combination, recipe detection, taboo detection and recipe screening, the communication module is used for data communication between the mobile device and the cloud data storage module, and the cloud data storage module is used for storing a virtual food material library, recipes and taboo rules.
The invention has the beneficial effects that:
1. according to the method, the images or pictures of the food materials are obtained, and the food materials are identified according to the characteristics of the food materials; aiming at the packaged food materials, recognizing characters packaged by different food materials by training a neural network model;
2. according to the method, the functions of the virtual food material library are added, the existing food materials can be firstly scanned and added into the virtual food material library, the quality guarantee period is added to the food materials in the food material library, and the associated recommendation can be made according to the unexpired food materials in the food material library;
3. the invention breaks through that the menu can only be matched according to the food materials in front, and the menu can also be recommended by combining the unexpired food materials stored in the virtual food material library;
4. according to the invention, matching screening can be carried out according to personal preference in the menu pre-matched according to the existing food materials before the menu is matched, and the missing food materials of the menu to be cooked are obtained (the food materials in the virtual food material library are subtracted from the needed food materials).
The features and advantages of the present invention will be described in detail by embodiments in conjunction with the accompanying drawings.
[ description of the drawings ]
FIG. 1 is a method flowchart of an intelligent recipe recommendation method and system based on image recognition according to the present invention.
[ detailed description ] embodiments
Referring to fig. 1, the present invention includes the following steps:
step 1: obtaining food material images or pictures through mobile equipment;
step 2: according to the food material images or pictures, identifying the attributes of all food materials: identifying the Food material by image identification technology of object detection, wherein the identified Food material is Food ═ (F)AFBFC…) in which FAIs food material A;
and step 3: judging whether the virtual food material library has existing food materials or not, and if the existing unexpired food materials exist, arranging and combining the identified food materials and the food materials in the food material library by the system; if no ready-made food materials exist, the system carries out permutation and combination on the identified food materials;
and 4, step 4: detecting the menu of the permutation and combination of the food materials, and reserving the permutation and combination which can be used as the menu;
and 5: carrying out contraindication detection on the permutation and combination collocation by combining with user data, and giving up the permutation and combination violating the contraindication;
step 6: calculating the permutation and combination which meets the requirement, and matching the cooking menu for each combination;
and 7: displaying the matched menu on the mobile equipment from top to bottom according to the matching degree;
and 8: and finishing individual preference screening and maximum matching screening, obtaining the food materials lacking in the menu, and displaying the result of secondary screening: optimizing the dish allocation and combination based on taste preference according to the secondary screening requirement of the user; screening by combining a plurality of screening conditions, displaying each menu of the screening result, and simultaneously displaying the lacking food materials;
and step 9: and determining a menu and starting cooking.
Preferably, the image recognition technology in step 2 is a neural network algorithm.
Preferably, the method for establishing the virtual food material library in step 3 includes the following steps:
step 31: obtaining a food material image through a mobile device;
step 32: identifying the attribute of the food material according to the food material image;
step 33: the shelf life of the food materials is manually added.
Preferably, the user data in step 5 is data previously entered into the mobile device, and the user data includes a user disease history.
Preferably, the missing food material in the step 8 is the desired food material minus the existing food material.
The food product management system further comprises a mobile device with a camera module, a processing module and a communication module, the camera module is used for acquiring food material images, the processing module is used for processing the food material images, recipe combination, recipe detection, taboo detection and recipe screening, the communication module is used for data communication between the mobile device and the cloud data storage module, and the cloud data storage module is used for storing a virtual food material library, recipes and taboo rules.
The working process of the invention is as follows:
the invention relates to an intelligent menu recommendation method and system based on image recognition, which are explained in the working process by combining with the attached drawings.
And the recommendation of the intelligent menu can be completed by utilizing the mobile equipment and according to the steps. The related picture and image processing can not only identify the food materials in front of eyes, but also identify the food materials which are packaged and cannot be identified according to appearance shape characteristics, and the method is to train a neural network model to identify characters packaged by different food materials. For example, some liquid food materials such as soy sauce and vinegar cannot be recognized according to shapes, and the outer package of the soy sauce or the vinegar is subjected to character recognition by utilizing a learned algorithm; also orlistat biscuits, which can be identified as orlistat on the basis of their font.
The contraindication detection can be compared by combining a database in a storage module according to the past history. For example, the following contraindications are available:
hypertension: greasy and spicy foods are not recommended; high salt recipes are not recommended: such as pickled foods, cooked foods of soy sauce and pickles; foods high in cholesterol are not recommended; alcohol is not recommended;
hyperlipidemia: food materials with high fat content are not recommended: fat meat (streaky pork, fat beef, fat mutton, spareribs), nut-type food materials, and the like; high cholesterol foods such as fat oils, animal viscera, fried foods, etc. are not recommended; high sugar or sweet recipes are not recommended;
hyperglycemia: high fat recipes are not recommended: recipes involving frying; the recipe of high sugar containing food materials is not recommended: pastry, potato, sweet potato, porridge, sweet rice, sweet food (dried jujube, candied jujube, raisin, dried apricot, longan, honey, ice cream, sweet beverage), persimmon, prepared from refined rice flour; alcohol is not recommended;
gout: the high purine menu is not recommended: beer, seafood, animal viscera, mushroom, nut, oyster sauce, bean food, beef and mutton, broth and spinach;
hyperthyroidism: food materials with high iodine content are not recommended: kelp, laver, hair weeds, jellyfish, sea cucumber, sedge and persimmon; spicy recipe: hot pepper, mustard; nerve stimulation menu: coffee, strong tea;
patients with hemorrhoids: pungent and pungent food material stimulation: pepper, coffee, fried bread stick; greasy food material: fat meat; alcohol: beer, white spirit;
cholecystitis: spicy is not recommended: pepper, capsicum; seafood is not recommended; fried and fried dishes are not recommended; alcohol is not recommended; high fat is not recommended.
When identifying food materials, the food materials are connected together and matched according to the existing food materials in the virtual food material library, matching contraindication detection is carried out on the identified food material matching, and a matching menu is calculated for the food material combinations meeting the conditions; associating a menu according to the returned food material attributes, screening and customizing the matched menu according to the personal practice, taste preference and side dish preference of the user to make personalized recommendation, and reminding the matched menu of the lacking food materials (the food materials in the food material library are subtracted from the required food materials); the matched menu containing user allergy and disliking food materials (side dish) is filtered out from the pre-matched menu according to the returned food materials.
The above embodiments are illustrative of the present invention, and are not intended to limit the present invention, and any simple modifications of the present invention are within the scope of the present invention.
Claims (6)
1. An intelligent menu recommendation method based on image recognition is characterized by comprising the following steps: the method comprises the following steps:
step 1: obtaining food material images or pictures through mobile equipment;
step 2: according to the food material images or pictures, identifying the attributes of all food materials: identifying the Food material by image identification technology of object detection, wherein the identified Food material is Food ═ (F)AFBFC…) in which FAIs food material A；
And step 3: judging whether the virtual food material library has existing food materials or not, and if the existing unexpired food materials exist, arranging and combining the identified food materials and the food materials in the food material library by the system; if no ready-made food materials exist, the system carries out permutation and combination on the identified food materials;
and 4, step 4: detecting the menu of the permutation and combination of the food materials, and reserving the permutation and combination which can be used as the menu;
and 5: carrying out contraindication detection on the permutation and combination collocation by combining with user data, and giving up the permutation and combination violating the contraindication;
step 6: calculating the permutation and combination which meets the requirement, and matching the cooking menu for each combination;
and 7: displaying the matched menu on the mobile equipment from top to bottom according to the matching degree;
and 8: and finishing individual preference screening and maximum matching screening, obtaining the food materials lacking in the menu, and displaying the result of secondary screening: optimizing the dish allocation and combination based on taste preference according to the secondary screening requirement of the user; screening by combining a plurality of screening conditions, displaying each menu of the screening result, and simultaneously displaying the lacking food materials;
and step 9: and determining a menu and starting cooking.
2. The intelligent menu recommendation method based on image recognition as claimed in claim 1, wherein: the image recognition technology in the step 2 is a neural network algorithm.
3. The intelligent menu recommendation method based on image recognition as claimed in claim 1, wherein:
the method for establishing the virtual food material library in the step 3 comprises the following steps:
step 31: obtaining a food material image through a mobile device;
step 32: identifying the attribute of the food material according to the food material image;
step 33: the shelf life of the food materials is manually added.
4. The intelligent menu recommendation method based on image recognition as claimed in claim 1, wherein: the user data in step 5 is data input into the mobile device in advance, and the user data comprises the disease history of the user.
5. The intelligent menu recommendation method based on image recognition as claimed in claim 1, wherein: the missing food material in the step 8 is the required food material minus the existing food material.
6. The utility model provides an intelligence menu recommendation system based on image identification which characterized in that: including taking camera module, processing module, communication module's mobile device and high in the clouds data storage module, camera module is used for acquireing edible material image, and processing module is used for handling edible material image, menu combination, menu detection, taboo detection, menu screening, and communication module is used for the data communication between mobile device and the high in the clouds data storage module, and high in the clouds data storage module is used for storing virtual edible material storehouse, menu, taboo rule.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111640485A (en) * | 2020-05-26 | 2020-09-08 | 合肥美的电冰箱有限公司 | Diet information recommendation method and device, electronic equipment and medium |
| CN112137415A (en) * | 2020-09-28 | 2020-12-29 | 杭州老板电器股份有限公司 | Cooking appliance control system with camera recognition function, control method and cooking appliance |
| CN112231506A (en) * | 2020-10-28 | 2021-01-15 | 刘娴 | Information recommendation method and device based on food material identification |
| CN112288534A (en) * | 2020-10-30 | 2021-01-29 | 广州富港万嘉智能科技有限公司 | Personalized menu generation method, cooking method, server, intelligent cooking equipment, ordering system and storage medium |
| CN114927164A (en) * | 2022-07-18 | 2022-08-19 | 深圳市爱云信息科技有限公司 | Sample compatibility detection method, device, equipment and storage medium based on AIOT platform |
| CN115104863A (en) * | 2022-08-22 | 2022-09-27 | 广东海新智能厨房股份有限公司 | Intelligent cabinet based on image recognition and intelligent cabinet prompting method |
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2019
- 2019-12-03 CN CN201911216007.6A patent/CN111125516A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111640485A (en) * | 2020-05-26 | 2020-09-08 | 合肥美的电冰箱有限公司 | Diet information recommendation method and device, electronic equipment and medium |
| CN112137415A (en) * | 2020-09-28 | 2020-12-29 | 杭州老板电器股份有限公司 | Cooking appliance control system with camera recognition function, control method and cooking appliance |
| CN112231506A (en) * | 2020-10-28 | 2021-01-15 | 刘娴 | Information recommendation method and device based on food material identification |
| CN112288534A (en) * | 2020-10-30 | 2021-01-29 | 广州富港万嘉智能科技有限公司 | Personalized menu generation method, cooking method, server, intelligent cooking equipment, ordering system and storage medium |
| CN114927164A (en) * | 2022-07-18 | 2022-08-19 | 深圳市爱云信息科技有限公司 | Sample compatibility detection method, device, equipment and storage medium based on AIOT platform |
| CN115104863A (en) * | 2022-08-22 | 2022-09-27 | 广东海新智能厨房股份有限公司 | Intelligent cabinet based on image recognition and intelligent cabinet prompting method |
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