CN111128342A - Artificial intelligence recommended dish collocation platform and dish collocation customizing method - Google Patents
Artificial intelligence recommended dish collocation platform and dish collocation customizing method Download PDFInfo
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- 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
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Abstract
The invention provides an artificial intelligence recommended dish collocation platform and a dish collocation customizing method, and relates to the field of artificial intelligence. An artificial intelligence recommended dish collocation platform comprises a user acquisition unit, an image generation unit, a dish database and a dish analysis unit, wherein the user acquisition unit is used for a user to log in and input a plurality of user characteristics; the vegetable recommending unit is used for recommending vegetables to the user, and the image analyzing unit is connected with the image generating unit and used for analyzing the virtual portrait to obtain the daily required nutrition of the user; the dish recommending unit is respectively connected with the image analyzing unit and the dish analyzing unit so as to recommend a dish combination containing a plurality of dishes according to daily required nutrition of the user. The invention solves the problems of low efficiency and high cost of artificial customized nutritious food, and is convenient for people to use.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an artificial intelligence recommended dish matching platform and a dish matching customizing method.
Background
The nutrition required daily varies from person to person due to their physical condition. For different people, in order to pursue health and longevity, people customize fitness and diet plans for themselves, but unreasonable exercise, unreasonable diet or other living conditions can affect the absorption of nutrition, and a result is obtained. Some gymnasiums or health maintenance meetings put out various nutrition collocation aiming at three meals a day of different clients, but generally the price is expensive, the nutrition collocation is not suitable for most people, and special nutrition collocation teachers and fitness coaches need to be reserved to make plans for individuals, so that the time is consumed, and the nutrition collocation is inconvenient for people to use. Therefore, a platform and a method for recommending dish collocation, which are suitable for more extensive people, can process big data by using artificial intelligence, greatly improve the use efficiency and provide convenience for people, are needed at present.
Disclosure of Invention
The invention aims to provide an artificial intelligence dish recommending platform which can recommend dishes by analyzing virtual figures of users through artificial intelligence, promote healthy diet and nutrition balance of the whole people and improve the customizing efficiency of nutrition matching plans.
Another object of the present invention is to provide a method for customizing a meal collocation, which can analyze a virtual portrait of a user through a neural network model, so as to obtain a diet customization plan satisfying daily nutrition requirements of the user.
Another object of the present invention is to provide a method for customizing a meal collocation, which enables a nutrition collocation user to analyze a virtual portrait of the user, improves efficiency of customizing a nutrition meal, and improves individuation and satisfaction of the nutrition meal through user feedback.
Another object of the present invention is to provide a method for customizing a meal collocation, which enables a nutrition collocation user to analyze a virtual portrait of a user, improves efficiency of customizing a nutrition meal, and further improves personalization of the nutrition meal through recommendation of the nutrition collocation user.
The embodiment of the invention is realized by the following steps:
an artificial intelligence recommended dish collocation platform comprises a user acquisition unit, an image generation unit, a dish database and a dish analysis unit, wherein the user acquisition unit is used for a user to log in and input a plurality of user characteristics; the vegetable recommending unit is used for recommending vegetables to the user, and the image analyzing unit is connected with the image generating unit and used for analyzing the virtual portrait to obtain the daily required nutrition of the user; the dish recommending unit is respectively connected with the image analyzing unit and the dish analyzing unit so as to recommend a dish combination containing a plurality of dishes according to daily required nutrition of the user.
In some embodiments of the present invention, the system further comprises a user feedback unit connected to the user acquisition unit and configured for a user to input recommended dishes and to select each dish in the dish combination; the user feedback unit is in data connection with the dish database to upload recommended dishes; the dish recommending unit is connected with the user feedback unit to replace dishes which are not selected by the user.
In some embodiments of the invention, the image analysis unit comprises a visitor recommendation unit for visitor entry and entry of a dish combination comprising a plurality of dishes; the dish recommending unit is respectively connected with the user collecting unit and the image generating unit to obtain a virtual portrait of the user; the person recommending unit and the dish recommending unit are connected to send the dish combination to the user.
In some embodiments of the invention, the reach recommendation unit comprises a reach recognition module for uploading each of a combination of dishes entered by a reach; the person identification module is in data connection with the dish database to judge whether the dishes in the input dish collocation belong to the dish database or not, and if not, the dish is uploaded to the dish database.
A dish matching customizing method based on an artificial intelligence recommended dish matching platform comprises the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the dish recommending unit is used for analyzing the dish analyzing unit and the image analyzing unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and the three dish combinations are sent to the dish recommending unit to be checked by the users.
A dish matching customizing method based on an artificial intelligence recommended dish matching platform comprises the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; analyzing the dish analysis unit and the image analysis unit through the dish recommendation unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and sending the three dish combinations to the dish recommendation unit for the users to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
A dish matching customizing method based on an artificial intelligence recommended dish matching platform comprises the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user through the person arrival recommending unit to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the person-to-person recommending unit obtains three dish combinations which meet the daily nutrition requirements of the user and are respectively used for three meals a day and contain a plurality of dishes, and sends the three dish combinations to the dish recommending unit for the user to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
The embodiment of the invention at least has the following advantages or beneficial effects:
1. the user acquisition unit is used for logging in and inputting user characteristics, so that a plurality of users can be conveniently acquired at the same time, and the customization efficiency of the nutritious food is improved;
2. the image generation unit is used for acquiring a plurality of characteristic factors of the user characteristics to correlate a plurality of user characteristics of the correlated user, so that different user groups are distinguished, and the user groups are materialized, and the individual nutrition plan is conveniently customized for each user;
3. nutritional components of dishes in the dish database are analyzed through the nutritional analysis unit, the virtual portrait is analyzed through the image analysis unit, the dish recommendation unit quickly matches the results with nutritional catering plans of different users through a data algorithm, and the intelligence and individualization of individual nutritional meal customization are improved;
4. the image analysis unit analyzes the virtual portrait of the user through the person-arriving recommendation unit to judge the daily required nutrient components of the user and obtain a dish combination which accords with the daily required nutrition of the user and contains a plurality of dishes, so that the individuation of customizing a nutritional meal for the user is enhanced;
5. recommended dishes are input through the user feedback unit, and the practicability and the satisfaction degree of the nutrition meal customization are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of a dish collocation artificial intelligence recommendation platform according to embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of an artificial intelligence dish collocation recommending platform according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are usually placed in when used, the orientations or positional relationships are only used for convenience of describing the present invention and simplifying the description, but the terms do not indicate or imply that the devices or elements indicated must have specific orientations, be constructed in specific orientations, and operate, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not require that the components be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, "a plurality" represents at least 2.
In the description of the embodiments of the present invention, it should be further noted that unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example 1
Referring to fig. 1, fig. 1 shows an artificial intelligence recommended dish matching platform, and the present embodiment provides an artificial intelligence recommended dish matching platform, which includes a user acquisition unit, an image generation unit connected to the user acquisition unit, a dish database, a dish analysis unit connected to the dish database, an image analysis unit connected to the image generation unit, and a dish recommendation unit respectively connected to the image analysis unit and the dish analysis unit.
In detail, the user acquisition unit comprises a login module for user login and a feature module connected with the login module for a user to input a plurality of user features. The image generation unit is connected with the user acquisition unit to acquire a plurality of user characteristics of the user, and generates a virtual portrait of the user according to the plurality of user characteristics (including age, gender and various physical factors). A plurality of dishes are recorded in the dish database, and the dish analysis unit analyzes the nutritional ingredients contained in each dish according to the components of each dish. The dish database is a network cloud disk. The dish recommending unit is connected with the image generating unit to acquire the virtual portrait of the user. The dish recommending unit is connected with the dish analyzing unit to obtain the nutrient components of each dish. The dish recommending unit analyzes the nutritional ingredients required by the user every day/meal by using the virtual portrait of the user, reasonably collocates dishes to meet the requirements of the user, and sends the matched dish combination to the user.
The login module and the characteristic module of the user acquisition unit respectively comprise an input end for user login and user characteristic input and a display end for checking the login state and the user characteristic when the user logs in. The dish recommending unit comprises a display end for a user to view dish combinations. The image generation unit comprises a display end used for a user to view the virtual portrait. When the invention is applied to a computer, the input end of the user acquisition unit is connected with an input keyboard of the computer; and the display end of the dish recommending unit and the display end of the image generating unit are respectively connected with the display screen of the computer. The input end of the dish database is connected with an input keyboard of the computer to input various dishes, and the display end of the dish database is connected with a display screen of the computer to allow a user to check various dishes.
When the system is used, the system is applied to a computer, a user logs in through a login module of a user acquisition unit, and a plurality of user characteristics of the user are input through a characteristic module after the user logs in. And the dish analysis unit analyzes the composition of each dish according to the menu of each dish in the dish database so as to obtain the nutritional ingredients contained in each dish. The recipe may be stored in a dish analysis unit or a dish database. The image generation unit is associated with the human body internal and external factors by analyzing a plurality of user features, thereby combining into a virtual portrait of the user. Such as forming an external appearance of the virtual figure according to age, motion, sex and weight included in the user's characteristics; and the internal conditions of the virtual portrait are formed according to diseases, blood types, constitutions and diet preferences contained in the user characteristics, and are marked by colors and characters, so that the personal characteristics of the user can be conveniently and intuitively shown. The image analysis unit judges the nutrition and diet contraindication required by the user every day/meal by identifying and comparing the correlation and importance degree of the external image and the internal condition of the virtual portrait. The dish recommending unit obtains a dish combination suitable for the user through results of the image analyzing unit and the dish analyzing unit, and sends a menu corresponding to each dish in the dish combination to the display screen for the user to check.
It should be noted that the above mentioned is only a further explanation of the protection scheme, and does not fall outside the scope of protection, and the simple transformations made are all included in the scope of protection as understood by those skilled in the art, such as the transformation applied to the mobile terminal and adding hair length or expression to the virtual portrait instead.
As a better implementation mode, the artificial intelligence recommended dish matching platform further comprises a user feedback unit connected with the user acquisition unit; the user feedback unit is in data connection with the dish database; the dish recommending unit is connected with the user feedback unit.
In detail, the user feedback unit includes a dish input module connected with a login module of the user feedback unit to input recommended dishes after the user logs in. The dish input module is connected with the dish database to search dishes similar to the recommended dishes in the dish database for direct selection of the user. The user feedback unit is in data connection with the dish database to upload recommended dishes. The menu of the recommended dishes can be uploaded to a dish database or be in data connection with a dish analysis unit and uploaded to the dish database, or the menu of the recommended dishes can be searched in a network manner by connecting the dish analysis unit with a network terminal of a computer. The user feedback unit also comprises a dish selection module which is respectively connected with the login module and the dish recommending unit and is used for the user to select each dish in the unnecessary dish combination after the dish recommending unit recommends the dish combination suitable for the user, so that the dish recommending unit can replace the selected dish with one or more dishes with the same or similar nutrient components. The dish recommending unit is characterized in that the dish input module is connected with the dish input module, so that the dish recommending unit is preferentially replaced by the input recommended dishes after the dishes are selected to be replaced.
As a preferred embodiment, the image analysis unit comprises a person-of-arrival recommendation unit; the dish recommending unit is respectively connected with the user collecting unit and the image generating unit; the person recommending unit is connected with the dish recommending unit.
In detail, the arrival recommending unit comprises an arrival authenticating module used for arrival login to authenticate the arrival and an arrival recommending module connected with the arrival authenticating module and used for inputting a dish combination comprising a plurality of dishes after the arrival login. The person-arriving recommending unit is connected with the login module of the user collecting unit to obtain user login information, and is connected with the image generating unit to obtain a virtual portrait corresponding to a user of the login module. The person-to-reach recommending unit is connected with the dish recommending unit to send a dish combination to the user corresponding to the login module. Optionally, the person-to-person recommendation module is connected with the dish database data to obtain each dish for composing the dish combination. The input module is also used for inputting attention information for dish collocation, such as alternative dishes, eating methods and dosage. When the device is used, the user and the user are connected through a network between different computers to realize data transmission between the user and the user, such as connection between a dish recommending unit and the user acquiring unit and connection between the user recommending unit and the image generating unit.
As a preferred embodiment, the acquaintance recommending unit comprises an acquaintance recognizing module; the person identification module is connected with the dish database data.
In detail, the person-arriving recognition module is used for uploading each dish in the dish combination input by the person who arrives, the person-arriving recognition module is connected with the dish database to judge whether the dishes in the dish collocation belong to each dish in the dish database, and new dishes which do not belong to the dish database are uploaded to the dish database.
As a preferred embodiment, the plurality of user characteristics includes age, gender, physical condition, illness, digestion, allergens, taste preferences, work and rest arrangements and exercise.
As a preferred embodiment, the dish recommendation unit further comprises a recipe and a eating regime for each dish in the recommended dish combination and notes on diet, sleep and exercise.
In a preferred embodiment, the plurality of user characteristics includes a particular period selected by the user, such as an operative period, a post-operative recovery period, a weight loss period, a fitness period, a pregnancy period, and a menstrual period.
According to the embodiment, the virtual portrait is generated according to a plurality of user characteristics by logging in and inputting the user characteristics, and the image analysis unit analyzes the virtual portrait through the neural network model, so that reasonable dish collocation of the user is obtained, the nutrition meal collocation efficiency is improved, and the requirement of people on nutrition balance is met.
Example 2
Referring to fig. 2, the present embodiment provides a dish collocation customizing method of an artificial intelligence recommended dish collocation platform, and the dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on embodiment 1 includes the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the dish recommending unit is used for analyzing the dish analyzing unit and the image analyzing unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and the three dish combinations are sent to the dish recommending unit to be checked by the users.
In the embodiment, multiple characteristic factors of multiple user characteristics of a user are collected, the virtual portrait of the user is generated according to the multiple characteristic factors, the image analysis unit analyzes the virtual portrait of the user by using the neural network model to obtain multiple nutritional ingredients required by the user every day, and the nutritional ingredients of various dishes in the dish database are analyzed according to the dish analysis unit to obtain dish collocation suitable for people, so that healthy diet development of nutrient balance of the whole people is promoted, and the efficiency and intellectualization of nutrition meal customization are improved.
Example 3
The embodiment provides a dish collocation customizing method of an artificial intelligence recommended dish collocation platform, and the dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on the embodiment 1 comprises the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; analyzing the dish analysis unit and the image analysis unit through the dish recommendation unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and sending the three dish combinations to the dish recommendation unit for the users to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
Compared with the embodiment 2, the method and the device have the advantages that the dishes are uploaded through the user feedback unit, the dish types matched with the dishes are automatically upgraded, and accordingly individuation and satisfaction of nutrition meal customization are improved.
Example 4
The embodiment provides a dish collocation customizing method of an artificial intelligence recommended dish collocation platform, and the dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on the embodiment 1 comprises the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user through the person arrival recommending unit to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the person-to-person recommending unit obtains three dish combinations which meet the daily nutrition requirements of the user and are respectively used for three meals a day and contain a plurality of dishes, and sends the three dish combinations to the dish recommending unit for the user to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
Compared with embodiment 3, the method and the system have the advantages that dish collocation of the user by the user is realized through the user recommending unit, and the nutrition meal customizing efficiency and individuation are further improved.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An artificial intelligence recommended dish collocation platform is characterized by comprising a user acquisition unit, an image generation unit, a dish database and a dish analysis unit, wherein the user acquisition unit is used for a user to log in and input a plurality of user characteristics; the vegetable recommending unit is used for recommending vegetables to the user, and the image analyzing unit is connected with the image generating unit and used for analyzing the virtual portrait to obtain the daily required nutrition of the user; the dish recommending unit is respectively connected with the image analyzing unit and the dish analyzing unit so as to recommend a dish combination containing a plurality of dishes according to daily required nutrition of the user.
2. The artificial intelligence recommended dish collocation platform of claim 1, further comprising a user feedback unit connected with the user acquisition unit and used for a user to input recommended dishes and select each dish in the dish combination; the user feedback unit is in data connection with the dish database to upload recommended dishes; the dish recommending unit is connected with the user feedback unit to replace dishes which are not selected by the user.
3. The artificial intelligence recommended dish collocation platform of claim 2, wherein the image analysis unit comprises a visitor recommendation unit for visitor login and input of a dish combination comprising a plurality of dishes; the dish recommending unit is respectively connected with the user collecting unit and the image generating unit to obtain a virtual portrait of the user; the person recommending unit and the dish recommending unit are connected to send the dish combination to the user.
4. The artificial intelligence recommended dish collocation platform of claim 3, wherein the reach recommendation unit comprises a reach recognition module for uploading each dish in the dish combination input by the reach; the person identification module is in data connection with the dish database to judge whether the dishes in the input dish collocation belong to the dish database or not, and if not, the dish is uploaded to the dish database.
5. The artificial intelligence recommended dish collocation platform of claim 1, wherein the plurality of user characteristics include age, gender, physical condition, illness, digestion, allergens, taste preferences, work and rest arrangements, and exercise conditions.
6. The artificial intelligence recommended dish collocation platform of claim 1, wherein the dish recommendation unit further comprises a menu and a eating method of each dish in the recommended dish combination and cautions on diet, sleep and exercise.
7. The artificial intelligence recommended dish collocation platform of claim 1, wherein the plurality of user characteristics include a period of operation, a period of postoperative recovery, a period of weight loss, a period of fitness, a period of pregnancy, and a period of menstruation, for user selection.
8. The dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on claim 1, characterized by comprising the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the dish recommending unit is used for analyzing the dish analyzing unit and the image analyzing unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and the three dish combinations are sent to the dish recommending unit to be checked by the users.
9. The dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on claim 2, characterized by comprising the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user by utilizing the neural network model so as to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; analyzing the dish analysis unit and the image analysis unit through the dish recommendation unit to obtain three dish combinations which meet the daily nutrition requirements of users and are respectively used for three meals a day and contain a plurality of dishes, and sending the three dish combinations to the dish recommendation unit for the users to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
10. The dish collocation customizing method of the artificial intelligence recommended dish collocation platform based on claim 3, characterized by comprising the following steps: after logging in a user through a user acquisition unit, inputting a plurality of user characteristics; the image generation unit acquires a plurality of characteristic factors according to a plurality of user characteristics input by a user and generates a virtual portrait of the user according to the characteristic factors; analyzing the nutritional ingredients of the dishes stored in the dish database through a dish analysis unit; the image analysis unit analyzes the virtual portrait of the user through the person arrival recommending unit to judge nutritional ingredients such as protein, vitamins and fibers required by the user every day; the person-to-person recommending unit obtains three dish combinations which meet the daily nutrition requirements of the user and are respectively used for three meals a day and contain a plurality of dishes, and sends the three dish combinations to the dish recommending unit for the user to check; the user inputs a recommended dish and an unsatisfactory dish through the user feedback unit, and the user unit stores the recommended dish into the dish database; the dish recommending unit replaces the dishes which are unsatisfied by the user input by other dishes which meet daily required nutrition of the user, and preferentially selects the recommended dishes input by the user.
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