CN117976145A - Personal long-term meal recommendation supervision method, system, terminal and storage medium - Google Patents
Personal long-term meal recommendation supervision method, system, terminal and storage medium Download PDFInfo
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Abstract
The application relates to a method, a system, a terminal and a storage medium for supervising personal long-term meal recommendation, and relates to the field of meal recommendation. The method comprises the following steps: acquiring daily nutritional requirements, caloric requirements, dietary preferences, addresses and planned starting times of a user; determining a cooking mode of a user and a cookable food corresponding to the cooking mode according to the diet preference, the address and the planned starting time of the user; according to the cooking mode, matching the nutrition loss rate and the heat change rate corresponding to the cooking mode; obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes in unit weight and the heat of the cookable dishes in unit weight according to the cookable food and the cooking mode and the corresponding nutrition loss rate and heat change rate; a long-term meal recommendation is generated based on the caloric and nutritional needs of the user. The application has the effect of carrying out accurate meal recommendation for users with different demands.
Description
Technical Field
The application relates to the technical field of meal recommendation, in particular to a method, a system, a terminal and a storage medium for supervising long-term meal recommendation of a person.
Background
With the improvement of living standard, the enriched diet also increases the probability of suffering from hypertension, diabetes and other noble diseases. There is therefore a need for rational diet management to reduce diet-induced health problems.
In the related art, basic diet suggestions are generally provided to users according to standards of food pyramid, diet guide and the like. Although this method is simple and easy to understand and easy to implement, it lacks pertinence and individualization, and cannot meet the needs of different users.
Disclosure of Invention
The application provides a method, a system, a terminal and a storage medium for supervising personal long-term meal recommendation, which achieve the effect of accurately recommending meals for users with different demands.
In a first aspect, the application provides a method for recommending and supervising long-term diet of a person, which adopts the following technical scheme:
A personal long-term meal recommendation supervision method comprising:
acquiring daily nutritional requirements, caloric requirements, dietary preferences, addresses and planned starting times of a user;
Determining a cooking mode of a user and a cookable food corresponding to the cooking mode according to the diet preference, the address and the planned starting time of the user;
according to the cooking mode, matching a nutrition loss rate and a heat change rate corresponding to the cooking mode;
Obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes in unit weight and the heat of the cookable dishes in unit weight according to the cookable food and the cooking mode and the corresponding nutrition loss rate and heat change rate;
according to the names of the cookable dishes, matching corresponding recommended eating periods from a pre-stored database;
and generating a long-term meal recommendation scheme according to the heat requirement and the nutrition requirement of the user and the recommended eating period of the cookable dishes.
By adopting the technical scheme, the heat and the energy demand of the user are firstly obtained, then the user's eating habit, address and plan starting time are combined, the user's convenient-to-use cookable food materials are screened, and then the long-term diet recommendation scheme of the user is generated according to the user's nutritional demand, energy demand, eating habit and cookable food materials.
In one possible implementation, obtaining daily nutritional and caloric needs of a user includes:
Acquiring basic information, health information and motion information of a user; the basic information includes the height, weight, age and sex of the user; the health information is a disease problem of a user; the motion information comprises a motion type and an average motion duration;
Calculating basic nutritional requirements and basic caloric requirements of the user according to the height, weight, age and gender of the user;
According to the health state, the daily additional nutrition requirement caused by the corresponding health state is called;
Calculating the daily additional heat demand of the user according to the exercise type and the average exercise duration;
and obtaining daily nutritional requirements and caloric requirements of the user according to the basic nutritional requirements, the basic caloric requirements, the daily additional nutritional requirements and the daily additional caloric requirements.
In one possible implementation, determining a cooking mode of the user and a cookable food material corresponding to the cooking mode according to the user's dietary preference, address, and planned start time includes: according to the address of the user, calling the category of the food material at the location of the address of the user;
Matching the category of the food materials in the season according to the planned starting time;
Obtaining the current available food category of the user according to the food category of the place and the season food category;
Determining a cooking mode and dislike food materials of the user according to the diet preference of the user, and removing the dislike food materials from the available food material types;
And further screening available food material categories according to the cooking modes of the users to obtain the cookable food materials of the users.
In one possible implementation, generating a long-term meal recommendation according to the recommended eating period of the cookable dish according to the caloric and nutritional needs of the user comprises:
firstly adding the cookable dishes into a list of breakfast, lunch and dinner according to the recommended eating period of the cookable dishes; the recommended eating period includes at least one of breakfast, lunch, and dinner;
According to the caloric requirement of the user, respectively determining caloric value of breakfast, lunch and dinner based on a preset caloric intake proportion;
Generating a daily menu set of breakfast, lunch and dinner according to the heat values of breakfast, lunch and dinner and the daily nutritional requirements of a user, and scoring the daily menu according to the satisfaction degree of the nutritional requirements to obtain the score of the daily menu;
Sorting the daily menu sets according to the scores, and eliminating daily menus with scores lower than preset scores;
selecting daily menus from the daily menu set according to a preset selection rule, forming a weekly menu set, and calculating the total score of the weekly menus;
And carrying out waveform sorting on the weekly menu set according to the total score to generate a long-term meal recommendation scheme.
In one possible implementation, selecting daily menus from the daily menu set according to a preset selection rule, and forming a weekly menu set includes:
Performing similarity calculation and sequencing by taking the first daily menu in the daily menu set as a standard to obtain a similarity table of the daily menu set;
dividing the similarity table into seven sub-tables with seven days as a period;
And extracting a daily menu from each sub-table in turn according to the sequence to form a weekly menu set.
In one possible implementation, the method further comprises adjusting a user follow-up dietary regimen based on the user feedback:
acquiring feedback information of a user, wherein the feedback information comprises modification information of initial parameters of a meal scheme by the user;
and obtaining a follow-up meal scheme according to the daily nutritional requirements, caloric requirements, diet preference, address and planned starting time of the user input by the user in the modification information.
In one possible implementation, the method further includes:
acquiring cloud data and a meal library;
according to the cloud data, a meal recommendation scheme template corresponding to the basic information, the health information and the movement information of the user is called;
and searching dishes from a meal library according to the diet preference of the user to adjust the meal recommendation scheme template, and generating an initial meal recommendation scheme of the user.
In a second aspect, the application provides a personal long-term meal recommendation monitoring system, which adopts the following technical scheme:
A personal long-term meal recommendation supervision system comprising:
The input acquisition module is used for acquiring daily nutritional requirements, caloric requirements, diet preference, addresses and planned starting time of a user;
The analysis screening module is used for determining a cooking mode of a user and a cookable food material corresponding to the cooking mode according to the diet preference, the address and the planned starting time of the user;
The calculating module is used for obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes in unit weight and the heat of the cookable dishes in unit weight according to the cookable food and the corresponding nutrition loss rate and heat change rate;
the scheme generation module is used for matching corresponding recommended eating periods from a pre-stored database according to the names of the cookable dishes; according to the caloric requirement and the nutritional requirement of the user, a long-term meal recommendation scheme is generated according to the recommended eating period of the cookable dishes.
In a third aspect, the application provides a terminal having the feature of personal long-term meal recommendation supervision.
The third object of the present application is achieved by the following technical solutions:
A terminal comprising a memory and a processor, said memory having stored thereon a computer program capable of being loaded by the processor and performing the above-described personal long-term meal recommendation supervision method.
In a fourth aspect, the present application provides a computer storage medium capable of storing a corresponding program, having features that facilitate long-term meal recommendation supervision of individuals.
The fourth object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program capable of being loaded by a processor and executing any one of the above-described personal long-term meal recommendation monitoring methods.
In summary, the present application includes at least one of the following beneficial technical effects: firstly, acquiring heat and energy requirements of a user, then screening the cookable food materials which are convenient for the user to use by combining with the eating habit, address and plan starting time of the user, and then generating a long-term diet recommendation scheme of the user according to the nutrition requirements, energy requirements, eating habit and cookable food materials of the user.
Drawings
FIG. 1 is a flow chart of a personal long-term meal recommendation monitoring method in accordance with one embodiment of the present application.
FIG. 2 is a schematic diagram of a personal long-term meal recommendation monitoring system in accordance with one embodiment of the present application.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Reference numerals illustrate: 201. an input acquisition module; 202. an analysis and screening module; 203. a computing module; 204. a scheme generation module; 301. a CPU; 302. a ROM; 303. a RAM; 304. a bus; 305. an I/O interface; 306. an input section; 307. an output section; 308. a storage section; 309. a communication section; 310. a driver; 311. removable media.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The application is described in further detail below with reference to fig. 1 to 3.
With the increase of living standard, diet health problems are increasingly emphasized. To reduce the health problems caused by poor eating habits, diet management is required. Existing meal recommendation methods generally provide basic meal suggestions to users according to standards such as food pyramids, meal guidelines, and the like. Although this method is simple and easy to understand and easy to implement, it lacks pertinence and individualization, and cannot meet the needs of different users.
In a first aspect, the application provides a personal long-term meal recommendation supervision method for accurately recommending meals for users with different demands.
Referring to fig. 1, a personal long-term meal recommendation supervision method comprises the steps of:
S101: daily nutritional needs, caloric needs, dietary preferences, addresses and planned start times of the user are obtained.
Specifically, the user's diet preference, address and planned starting time all directly acquire the information entered by the user. The daily nutritional requirements and caloric requirements of the user can be directly input by the user on one hand, and basic information, health status and exercise information can be input by the user on the other hand for calculation. Among these, the daily nutritional requirements of the users include essential proteins, vitamins, trace elements, etc., and the caloric requirements of the users are essential requirements for the users to meet the normal metabolism and production life. The basic information of the user includes the height, weight, age and sex of the user. The health information of the user is a disease problem existing in the user. The motion information of the user includes a motion type and an average motion duration.
In one implementation scenario, estimating the nutritional and caloric needs of the user from the base information, health status, and exercise information further comprises: the basic nutritional requirements and basic caloric requirements of the user are calculated according to the height, weight, age and sex of the user. This step brings the height, weight, age and gender entered by the user into the harris-ben-zendisk formula to obtain the user's basic caloric needs. And according to the gender input by the user, retrieving the nutrient standard intake of the corresponding gender from a pre-stored database.
After the basic nutrition requirement and the basic caloric requirement of the user are obtained in the previous implementation scene, targeted calculation is further required according to the health state and the exercise information of the user, so that the daily additional nutrition requirement and the daily additional caloric requirement of the user are obtained.
In one implementation scenario, according to the health status and exercise information of the user, obtaining the daily additional nutrition requirement and daily additional caloric requirement of the user further comprises: according to the health state of the user, the daily additional nutrition requirement caused by the corresponding health state is called, for example, the patient suffers from night blindness, the night blindness is known to be lack of vitamin A by inquiry, and the user needs to correspondingly take more vitamin A, so that the daily additional nutrition requirement is to take more vitamin A. And calculating the daily extra heat demand of the user according to the exercise type and the average exercise duration. Firstly, the heat consumption efficiency of the corresponding exercise type is called from a pre-stored database, and then the product of the heat consumption efficiency and the average exercise duration of the user is obtained, so that the daily extra heat requirement of the user is obtained.
S102: determining a cooking mode of the user and a cookable food material corresponding to the cooking mode according to the diet preference, the address and the planned starting time of the user.
Specifically, the personalized meal recommendations of the user are influenced by the user's dietary preferences, as well as by the geographical location and season in which they are located. The food preference affects the cooking mode of the user on one hand, and affects the food material selection of the user on the other hand, and the geographical position and the planned starting time of the user further limit the selection range of the food material.
In one implementation scenario, the selection of the cookable food item further includes: firstly, according to the address of the user, the food material category of the location of the address of the user is called from a pre-stored database. For example, coastal cities have rich aquatic food material types, inland regions have relatively less aquatic food material, and for example, partial regions have limited food material such as bacteria, bamboo shoots and the like. And matching the food material categories to be used in the season from the food material categories at the location according to the planned starting time to obtain the food material categories currently available to the user, for example, the bamboo shoots are generally harvested in spring festival and are food to be used in the season. Then on the other hand, according to the diet preference of the user, the cooking mode and dislike food materials of the user are determined, and the dislike food materials are removed from the available food material types. And finally, further screening available food material categories according to the cooking mode of the user to obtain the cookable food material of the user. For example, if a user prefers a cooking mode such as frying, a large food which needs to be stewed is unsuitable for the user to cook.
S103: according to the cooking mode, the nutrition loss rate and the heat change rate corresponding to the cooking mode are matched.
Specifically, different cooking modes are used for cooking, and the nutrition loss conditions of food materials are different, so that corresponding nutrition loss rates and heat change rates need to be matched from a pre-stored database according to the cooking modes of users.
S104: and obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes in unit weight and the heat of the cookable dishes in unit weight according to the cookable food and the cooking mode and the corresponding nutrition loss rate and heat change rate.
Specifically, according to the cookable food, matching corresponding cookable dishes from a pre-stored database, and then calculating the nutritional ingredients and the heat of the cookable dishes in unit weight according to the nutritional loss rate and the heat change rate.
After the culinary dish is obtained, the culinary dish is also required to be distributed according to the recommended eating period of the culinary dish. Each dish is pre-stored in the database for a recommended period of time, for example, the food with higher oil content is suitable for lunch.
S105: according to the caloric requirement and the nutritional requirement of the user, a long-term meal recommendation scheme is generated according to the recommended eating period of the cookable dishes.
Specifically, after the cookable dishes and the eating period of the cookable dishes are obtained, the diet of the user can be targeted managed.
Generating the long-term meal recommendation includes: generating a daily menu, forming a weekly menu according to the daily menu, and further forming a long-term meal recommendation scheme.
In one implementation scenario, the generation and screening of the daily menu includes: the culinary dish is added to a list of breakfast, lunch and dinner according to the recommended eating period of the culinary dish. Wherein the recommended eating period comprises at least one of breakfast, lunch and dinner and the recommended eating period of an egg, for example, comprises breakfast, lunch and dinner. And then respectively determining the caloric value of breakfast, lunch and dinner based on the preset caloric intake proportion according to the caloric requirement of the user. The caloric proportion of breakfast, lunch and dinner is typically 30%, 40%, 30%. And generating a daily menu set of breakfast, lunch and dinner according to the heat values of breakfast, lunch and dinner and the daily nutritional requirements of the user, and scoring the daily menus according to the satisfaction degree of the nutritional requirements to obtain the score of the daily menus. The daily menu is generated by distributing the types and weights of dishes on the premise of meeting the heat demands of users. Score of daily menu was calculated after the allocation was completed: . Wherein N is the total number of nutritional ingredients in the nutritional requirement,/> For a preset weight of each nutritional ingredient,/>Daily demand for users of each nutritional ingredient,/>Is the actual amount of each nutrient in the daily dish. And then sorting the daily menu sets according to the scores, and simultaneously eliminating daily menus with scores lower than preset scores.
In one implementation scenario, generating the weekly menu includes: and firstly, carrying out similarity calculation by taking the first daily menu in the daily menu set as a standard, and sequencing to obtain a similarity table of the daily menu set, so that the similarity between the daily menus can be determined. Then, in order to reduce the similarity in the weekly menu to be too high, the similarity table is divided into seven sub-tables with a period of seven days. And finally, sequentially extracting a daily menu from each sub-table according to the sequence to form a weekly menu set.
In one implementation scenario, after the weekly menu set is obtained, the weekly menu is also required to be scored, and the calculation mode is the sum of scores of the daily menus. In order to make nutrition more balanced, waveform ordering is needed for the weekly menus, and the total score of the weekly menus is ordered in a high-low-high mode by directly adopting waveform ordering because the score of the daily menus in the weekly menus is screened, and the total score of the weekly menus is small in difference. Thus, a personal long-term meal recommendation is obtained.
Further, since some users may not know themselves, the taste preference, cooking mode, etc. are not well known, and direct matching for such users is also included, including: acquiring cloud data and a meal library, then according to the cloud data, calling a meal recommendation scheme template corresponding to basic information, health information and movement information of a user, and according to the template, the user can adjust the meal preference, and according to the meal preference of the user, dishes are searched from the meal library to adjust the meal recommendation scheme template, so that an initial meal recommendation scheme of the user is generated.
Still further, the user's subsequent dietary regimen can be adjusted based on the user feedback, including: and acquiring feedback information of the user, wherein the feedback information comprises modification information of initial parameters of the meal scheme by the user. And obtaining a follow-up meal scheme according to the daily nutritional requirements, caloric requirements, diet preference, address and planned starting time of the user input by the user in the modification information.
For ease of understanding, a personal long-term meal recommendation monitoring system of the present application is described herein in terms of software operations:
The user opens the meal management application program, and pops up two keys of the accurate scheme and the template scheme. The user clicks a key of an accurate scheme and jumps to an information input interface, wherein the information input interface comprises the following contents to be filled in: height, weight, age, gender, health information, exercise type, average exercise duration, diet preference, address, and planned starting time. After the user fills in the corresponding content, jumping to a scheme generation interface, and displaying a daily menu, a weekly menu and a long-term meal recommendation scheme on the interface. Daily menus, weekly menus also show scoring and specific nutritional and energy details. The user clicks the template scheme and jumps to the information input interface, and the content to be filled in comprises: height, weight, age, gender, health information, exercise type, and average exercise duration. In the scheme generation interface, the existing scheme can be modified, including the content which is recorded before modification and the actual diet condition of the user, and the new diet scheme is regenerated and displayed after modification.
In a second aspect, the application provides a personal long-term meal recommendation monitoring system, which adopts the following technical scheme:
Referring to fig. 2, a personal long-term meal recommendation monitoring system comprising:
The input acquisition module 201 is used to acquire the user's daily nutritional needs, caloric needs, dietary preferences, address and planned start time.
The analysis and screening module 202 is configured to determine a cooking mode of the user and a cookable food material corresponding to the cooking mode according to the user's diet preference, address and planned start time.
The calculating module 203 is configured to obtain the name of the cookable dish, the nutritional component of the cookable dish per unit weight, and the heat of the cookable dish per unit weight according to the cookable food, and the corresponding nutrition loss rate and heat change rate.
A scheme generating module 204, configured to match corresponding recommended eating periods from a pre-stored database according to names of the cookable dishes; according to the caloric requirement and the nutritional requirement of the user, a long-term meal recommendation scheme is generated according to the recommended eating period of the cookable dishes.
Fig. 3 shows a schematic diagram of a terminal suitable for implementing an embodiment of the application.
As shown in fig. 3, the terminal includes a central processing unit (Central Processing Unit, CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section into a random access Memory (Random Access Memory, RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read out therefrom is installed into the storage section 308 as needed.
In particular, the process described above with reference to flowchart 1 may be implemented as a computer software program according to an embodiment of the application. For example, embodiments of the application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
The computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (EPROM or flash Memory), an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, register file (REGISTER FILE, RF), or the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. 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.
The units or modules involved in the embodiments of the present application may be implemented in software or in hardware. The described units or modules may also be provided in a processor. Wherein the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present application also provides a computer-readable storage medium, which may be contained in the terminal described in the above embodiment; or may exist alone without being fitted into the terminal. The computer readable storage medium stores one or more programs which when executed by one or more processors perform the personal long-term meal recommendation monitoring method of the present application.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features which may be formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. A method of personal long-term meal recommendation supervision, comprising:
acquiring daily nutritional requirements, caloric requirements, dietary preferences, addresses and planned starting times of a user;
Determining a cooking mode of a user and a cookable food corresponding to the cooking mode according to the diet preference, the address and the planned starting time of the user;
according to the cooking mode, matching a nutrition loss rate and a heat change rate corresponding to the cooking mode;
Obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes in unit weight and the heat of the cookable dishes in unit weight according to the cookable food and the cooking mode and the corresponding nutrition loss rate and heat change rate;
according to the names of the cookable dishes, matching corresponding recommended eating periods from a pre-stored database;
and generating a long-term meal recommendation scheme according to the heat requirement and the nutrition requirement of the user and the recommended eating period of the cookable dishes.
2. The personal long-term meal recommendation monitoring method according to claim 1, wherein obtaining daily nutritional and caloric requirements of a user comprises:
Acquiring basic information, health information and motion information of a user; the basic information includes the height, weight, age and sex of the user; the health information is a disease problem of a user; the motion information comprises a motion type and an average motion duration;
Calculating basic nutritional requirements and basic caloric requirements of the user according to the height, weight, age and gender of the user;
According to the health state, the daily additional nutrition requirement caused by the corresponding health state is called;
Calculating the daily additional heat demand of the user according to the exercise type and the average exercise duration;
and obtaining daily nutritional requirements and caloric requirements of the user according to the basic nutritional requirements, the basic caloric requirements, the daily additional nutritional requirements and the daily additional caloric requirements.
3. The personal long-term meal recommendation monitoring method according to claim 2, wherein determining a user's cooking regimen and a corresponding cooking regimen's cookable food item according to the user's dietary preference, address and planned start time, comprises:
according to the address of the user, calling the category of the food material at the location of the address of the user;
Matching the category of the food materials in the season according to the planned starting time;
Obtaining the current available food category of the user according to the food category of the place and the season food category;
Determining a cooking mode and dislike food materials of the user according to the diet preference of the user, and removing the dislike food materials from the available food material types;
And further screening available food material categories according to the cooking modes of the users to obtain the cookable food materials of the users.
4. The personal long-term meal recommendation supervision method according to claim 2, wherein generating a long-term meal recommendation according to recommended eating periods of a cookable dish according to caloric and nutritional needs of the user comprises:
firstly adding the cookable dishes into a list of breakfast, lunch and dinner according to the recommended eating period of the cookable dishes; the recommended eating period includes at least one of breakfast, lunch, and dinner;
According to the caloric requirement of the user, respectively determining caloric value of breakfast, lunch and dinner based on a preset caloric intake proportion;
Generating a daily menu set of breakfast, lunch and dinner according to the heat values of breakfast, lunch and dinner and the daily nutritional requirements of a user, and scoring the daily menu according to the satisfaction degree of the nutritional requirements to obtain the score of the daily menu;
Sorting the daily menu sets according to the scores, and eliminating daily menus with scores lower than preset scores;
selecting daily menus from the daily menu set according to a preset selection rule, forming a weekly menu set, and calculating the total score of the weekly menus;
And carrying out waveform sorting on the weekly menu set according to the total score to generate a long-term meal recommendation scheme.
5. A personal long-term meal recommendation supervision method according to claim 3, wherein selecting daily menus from the daily menu set according to a preset selection rule, forming a weekly menu set, comprises:
Performing similarity calculation and sequencing by taking the first daily menu in the daily menu set as a standard to obtain a similarity table of the daily menu set;
dividing the similarity table into seven sub-tables with seven days as a period;
And extracting a daily menu from each sub-table in turn according to the sequence to form a weekly menu set.
6. The personal long-term meal recommendation monitoring method of claim 1, further comprising adjusting a user follow-up meal schedule based on user feedback:
acquiring feedback information of a user, wherein the feedback information comprises modification information of initial parameters of a meal scheme by the user;
and obtaining a follow-up meal scheme according to the daily nutritional requirements, caloric requirements, diet preference, address and planned starting time of the user input by the user in the modification information.
7. The personal long-term meal recommendation monitoring method according to claim 2, further comprising:
acquiring cloud data and a meal library;
according to the cloud data, a meal recommendation scheme template corresponding to the basic information, the health information and the movement information of the user is called;
and searching dishes from a meal library according to the diet preference of the user to adjust the meal recommendation scheme template, and generating an initial meal recommendation scheme of the user.
8. A personal long-term meal recommendation monitoring system, comprising:
An input acquisition module (201) for acquiring a user's daily nutritional needs, caloric needs, dietary preferences, addresses and planned start times;
an analysis screening module (202) for determining a cooking mode of a user and a cookable food material corresponding to the cooking mode according to the user's diet preference, address and planned starting time;
A calculating module (203) for obtaining the name of the cookable dishes, the nutritional ingredients of the cookable dishes per unit weight and the heat of the cookable dishes per unit weight according to the cookable food, and the corresponding nutrition loss rate and heat change rate;
a scheme generating module (204) for matching corresponding recommended eating periods from a pre-stored database according to the names of the cookable dishes; according to the caloric requirement and the nutritional requirement of the user, a long-term meal recommendation scheme is generated according to the recommended eating period of the cookable dishes.
9. A terminal comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 7.
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| CN202410211810.5A CN117976145A (en) | 2024-02-27 | 2024-02-27 | Personal long-term meal recommendation supervision method, system, terminal and storage medium |
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