CN115910284A - Dining recommendation method and device, electronic equipment and storage medium - Google Patents
Dining recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- CN115910284A CN115910284A CN202211642340.5A CN202211642340A CN115910284A CN 115910284 A CN115910284 A CN 115910284A CN 202211642340 A CN202211642340 A CN 202211642340A CN 115910284 A CN115910284 A CN 115910284A
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Abstract
The embodiment of the invention provides a dining recommendation method, which comprises the steps of obtaining historical diet data and daily activity data of target students in a campus; determining nutrition intake data of the target students according to historical diet data, and determining intake demand data of the target students according to daily activity data; and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the meals to the target students based on the meal recommendation data. Through historical diet data and the activity data of the day of the student in the campus, can be according to historical diet data analysis student's nutrition intake data, confirm student's intake demand data according to activity data of the day, and then according to nutrition intake data and intake demand data generation dining recommendation data, can recommend the food that accords with self nutrition demand to the student, avoid the student to lead to nutrition imbalance because of the dish of long-term selection hobby, the intelligent level of campus having improved the meal.
Description
Technical Field
The invention relates to the field of smart campuses, in particular to a dining recommendation method and device, electronic equipment and a storage medium.
Background
Along with the continuous improvement of the attention degree of education, the life of students in a campus is also widely concerned, modern education not only focuses on the education management of the students in the school, but also focuses on the life management of the students in the school, the students are in a long-term health, are vigorous in metabolism, are lively and lively, and nutrition has profound influence on body types and functions of teenagers, intelligence and emotion, so that the students having meals in the school should be guaranteed in nutrition. In the existing campus canteen, a catering supply method is used for selecting dishes for students according to preferences of the students, the students often cannot judge the nutrition types of the dishes according to the dishes, and the conditions of unbalanced nutrition of the students can be caused by selecting the favorite dishes for a long time.
Disclosure of Invention
The embodiment of the invention provides a dining recommendation method, and aims to solve the problem that in an existing campus dining room, a dining supply method is that students select dishes according to preferences of the students, the students often cannot judge the nutrition types of the dishes according to the dishes, and the nutrient imbalance of the students can be caused by selecting the favorite dishes for a long time. Through historical diet data and the activity data of the day of the student in the campus, can go out student's nutrition intake data according to historical diet data analysis, confirm student's intake demand data according to activity data of the day, and then generate the recommended data of having dinner according to nutrition intake data and intake demand data, can recommend the food that accords with self nutritional demand to the student, avoid the student to lead to the nutrition imbalance because of the dish of long-term selection hobby, the intelligent level of having dinner in the campus has been improved.
In a first aspect, an embodiment of the present invention provides a dining recommendation method, where the method includes:
acquiring historical diet data and daily activity data of target students in a campus;
determining nutrient intake data of the target student according to the historical diet data, and determining intake demand data of the target student according to the daily activity data;
and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the target students to meals based on the meal recommendation data.
Optionally, the acquiring historical diet data of the target student in the campus includes:
acquiring body data of the target student;
determining a data acquisition time period according to the body data;
and searching data in a database based on the data acquisition time period to obtain historical diet data of the target students in the campus.
Optionally, the acquiring the activity data of the target student on the current day in the campus includes:
acquiring a campus monitoring video of the target student in the campus on the same day;
and carrying out image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus.
Optionally, the performing image analysis on the campus monitoring video to obtain the daily activity data of the target student in the campus includes:
performing action recognition on the campus monitoring video to obtain action types of the target students in the campus on the same day and action time corresponding to each type of action;
and determining the day activity data of the target student in the campus based on the action type and the corresponding action time.
Optionally, the determining the nutrient intake data of the target student according to the historical dietary data comprises:
determining historical nutrient intake types and historical intake amounts corresponding to various types of nutrients according to the historical diet data;
and determining the nutrient intake type of the target student according to the historical nutrient intake type and the historical intake amount corresponding to each type of nutrition.
Optionally, the determining intake demand data of the target student according to the activity data of the current day includes:
determining energy expenditure data of the target student based on the action type and the corresponding action time;
and determining the daily intake demand of the target student according to the energy consumption data.
Optionally, the generating meal recommendation data of the target student according to the nutrient intake data and the intake requirement data includes:
determining a recommended food in the current-day food according to the nutrient intake type, wherein the recommended food comprises the nutrient intake type;
determining the recommended intake of the recommended food according to the intake demand of the day;
and generating dining recommendation data of the target student according to the recommended food and the recommended intake.
In a second aspect, an embodiment of the present invention provides a dining recommendation apparatus, where the apparatus includes:
the acquisition module is used for acquiring historical diet data and daily activity data of target students in a campus;
the determining module is used for determining the nutrition intake data of the target students according to the historical diet data and determining the intake demand data of the target students according to the daily activity data;
and the recommending module is used for generating the dining recommending data of the target students according to the nutrition intake data and the intake demand data, and recommending the dining to the target students based on the dining recommending data.
In a third aspect, an embodiment of the present invention provides an electronic device, including: the meal recommendation method comprises the steps of a meal recommendation method provided by the embodiment of the invention when the processor executes the computer program.
In a fourth aspect, the embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps in the dining recommendation method provided by the embodiment of the present invention.
In the embodiment of the invention, historical diet data and daily activity data of target students in a campus are acquired; determining nutrient intake data of the target student according to the historical diet data, and determining intake demand data of the target student according to the daily activity data; and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the target students to meals based on the meal recommendation data. Through historical diet data and the activity data of the day of the student in the campus, can be according to historical diet data analysis student's nutrition intake data, confirm student's intake demand data according to activity data of the day, and then according to nutrition intake data and intake demand data generation dining recommendation data, can recommend the food that accords with self nutrition demand to the student, avoid the student to lead to nutrition imbalance because of the dish of long-term selection hobby, the intelligent level of campus having improved the meal.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a meal recommendation method provided by an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a meal recommendation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, fig. 1 is a flowchart of a dining recommendation method according to an embodiment of the present invention, and as shown in fig. 1, the dining recommendation method includes the following steps:
101. historical diet data of target students in a campus and daily activity data are obtained.
In the embodiment of the invention, the target student can be a student entering a campus dining room for preparing to eat, specifically, identity information identification equipment can be arranged at a meal taking window of the campus dining room, and identity information of the student before the meal taking window is identified through the identity information identification equipment.
The identity information recognition device can be an image collection device, the image collection device is used for collecting face images of students in front of the window, face recognition is carried out according to the face images to obtain identity information of the students in front of the window, and after the identity information of the students in front of the window is determined, the students in front of the window are further determined to be target students.
The identity information identification device can also be a magnetic card induction device, magnetic card information of students in front of the window is induced through the magnetic card induction device, identity information of the students in front of the window is determined according to the magnetic card information, and after the identity information of the students in front of the window is determined, the students in front of the window are further determined to be target students.
After the target student is determined, historical diet data and daily activity data of the target student in the campus can be searched in the database according to the identity information of the target student. Specifically, a student file can be established for each student who needs to have a meal in the campus, historical diet data of the student who has a meal in the campus canteen at each time are recorded in the student file, and activity data of the student in the campus at each moment are also recorded. The student files of the target students can be searched in the database through the identity information of the target students, and historical diet data of the target students in the campus and daily activity data of the target students in the campus are obtained according to the student files.
The historical eating data may include meal items, meal times, meal consumption and the like, and the activity data may include activity types and activity times.
102. And determining the nutrient intake data of the target students according to the historical diet data, and determining the intake demand data of the target students according to the daily activity data.
In the embodiment of the invention, the nutrition intake data of the target student can be determined according to the dinning items, the dinning time, the dinning amount and the like in the historical diet data, and the nutrition intake data can be understood as the type of nutrition which needs to be taken by the target student currently. Specifically, historical nutritional intake data of the target student may be determined based on historical dietary data, such that whether the target student lacks a certain type of nutrition or is over-nourished may be determined based on the historical nutritional intake data. For example, historical data of students A is analyzed, if the meal of the students A is mainly meat and the fruits and vegetables are few, the possible protein surplus and vitamin deficiency of the students A can be determined, and the nutrient intake data of the students A can be the increase of the vitamin intake and the appropriate decrease of the protein intake.
The intake requirement data of the target student, which can be understood as the amount of food that the student needs to intake, can be determined according to the activity type and the activity time in the activity data of the day. Specifically, the activity intensity of the target student before eating on the same day can be determined according to the activity data on the same day, the higher the activity intensity is, the higher the physical consumption of the target student is, the more food needs to be taken, and the lower the activity intensity is, the lower the physical consumption of the target student is, and the less food needs to be taken. For example, the daily activity data of the student a is analyzed, and if the student a sits during the course, the class room also sits at the position, and there is not much amount of exercise, it can be determined that the activity intensity of the student a is low, the physical consumption during the period is low, and the food needed to be taken in during dining is less. On the contrary, if the student A sits in the course of the day, runs and beats in the class, runs and kicks the ball in the physical education class, the student A can be determined to have higher activity intensity, higher physical consumption in the class and more food to be taken in during dining.
103. And generating meal recommendation data of the target student according to the nutrition intake data and the intake demand data, and recommending the meal to the target student based on the meal recommendation data.
In an embodiment of the present invention, the meal recommendation data may include a recommended meal item and a recommended intake amount, the recommended meal item includes a nutrition type that a target student needs to take currently, and the nutrition type that the target student needs to take currently is determined according to historical dietary data of the target student in a campus. The recommended intake is determined according to the daily activity data of the target students in the campus.
The campus dining room can set up display device beside the window of getting meal, and display device can be the LED display screen for show target student's recommended data of having dinner, so that target student can select a meal according to the recommended data of having dinner on the display device.
Specifically, identity information identification can be carried out on students in front of the window through identity information identification equipment on the meal window, after the identity information of the students in front of the window is determined, nutrition intake data and intake demand data of the students in front of the window are determined according to historical diet data of the students in front of the window in the campus and activity data of the day, meal recommendation data of target students are generated according to the nutrition intake data and the intake demand data, and the meal recommendation data are sent to display equipment to be displayed.
In the embodiment of the invention, historical diet data and daily activity data of target students in a campus are obtained; determining nutrient intake data of the target student according to the historical diet data, and determining intake demand data of the target student according to the daily activity data; and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the target students to the meals based on the meal recommendation data. Through historical diet data and the activity data of the day of the student in the campus, can be according to historical diet data analysis student's nutrition intake data, confirm student's intake demand data according to activity data of the day, and then according to nutrition intake data and intake demand data generation dining recommendation data, can recommend the food that accords with self nutrition demand to the student, avoid the student to lead to nutrition imbalance because of the dish of long-term selection hobby, the intelligent level of campus having improved the meal.
Optionally, in the step of obtaining historical diet data of the target student in the campus, body data of the target student may be obtained; determining a data acquisition time period according to the body data; and based on the data acquisition time period, performing data search in the database to obtain historical diet data of the target students in the campus.
In the embodiment of the present invention, the target student may be a student who enters a campus dining room to prepare for dining, specifically, an identity information identification device may be disposed at a dining window of the campus dining room, and identity information of the student before the dining window is identified by the identity information identification device. After the target student is determined, the body data of the target student can be searched in the database according to the identity information of the target student.
The body state of the target student is determined through the body data, and the data acquisition time is determined according to the body state of the target student. Specifically, the body data may include height data and weight data, and the data acquisition time is determined according to the height data and the weight data of the target student. Specifically, the ratio of the weight to the height of the target student can be calculated, and the data acquisition time is determined according to the ratio of the weight to the height of the target student. More specifically, when the ratio of the weight to the height of the target student is within a preset range, the data time may be determined as a first preset time, and when the ratio of the weight to the height of the target student is outside the preset range, the data time may be determined as a second preset time. The preset range can be determined according to the ratio of the weight to the height of the student in the normal posture, if the ratio exceeds the preset range, the student can be judged to be fat or thin, the first preset time is shorter than the second preset time, the student who is fat or thin can obtain the historical diet data for a longer time, and the student in the normal posture can obtain the historical diet data for a shorter time, for example, the first preset time can be within one week, and the second preset time can be within one month. Therefore, the students with fatness or thinness posture can be subjected to nutrition analysis for a longer time, and then the nutrition intake data of the target students can be obtained more accurately, so that the recommendation result is more accurate.
Optionally, in the step of obtaining the activity data of the target student in the campus on the same day, a campus monitoring video of the target student in the campus on the same day may be obtained; and carrying out image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus. The campus monitoring videos may include classroom monitoring videos, corridor monitoring videos, playground monitoring videos, and the like.
In the embodiment of the invention, the image monitoring equipment is arranged in the campus, the campus monitoring video can be obtained through the image monitoring equipment, the face recognition can be carried out on the campus monitoring video to obtain the video appearing in the target student, and the video appearing in the target student is added into the student file of the target student.
After the target student is determined, the campus monitoring video of the target student in the campus on the same day can be searched in the database according to the identity information of the target student. After the campus monitoring videos of the target students in the campus on the same day are obtained, image analysis can be performed on the campus monitoring videos through an image analysis technology, and therefore the activity data of the target students in the campus on the same day are obtained.
In a possible embodiment, after the video of the appearance of the target student is obtained, the campus monitoring video is subjected to image analysis through an image analysis technology, so that the activity data of the target student in the campus is obtained, and the activity data of the target student in the campus is added into a student file of the target student. Thus, after the target student is determined, the activity data of the target student in the campus on the same day can be searched in the database.
The activity data can comprise activity types and activity time, the activity data can also be called behavior data, the behavior data can comprise behavior types of rest, walking, running, alarming, kicking, basketball playing, singing and the like, and campus monitoring videos can be identified through behavior identification technology to obtain the current-day behavior data of the target students in the campus.
Optionally, in the step of performing image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus, action recognition may be performed on the campus monitoring video to obtain the daily action types of the target students in the campus and action time corresponding to each type of action; and determining the day activity data of the target students in the campus based on the action types and the corresponding action time.
In the embodiment of the present invention, the type of the activity may be determined according to the type of the motion, the type of the motion may include a hand motion, a foot motion, a body motion, a mouth motion, and the like, and one activity may be formed by combining a plurality of motions. The activity time may be determined according to an action time corresponding to each action in the activity.
The campus monitoring video can be subjected to action recognition through an action recognition algorithm, so that the action types of the target students in the campus on the day and action time corresponding to each type of action are obtained. The type of action made by the target student on the campus on the same day and the corresponding action time can be used as the activity data of the target student on the same day on the campus. Therefore, the activity types can be disassembled into different action combinations, the energy consumption is calculated according to different actions, the calculation granularity of the energy consumption is finer, the daily intake demand is more accurate, and the accuracy of dining recommendation is further improved.
Optionally, in the step of determining the nutrient intake data of the target student according to the historical dietary data, the historical nutrient intake types and the historical intake amounts corresponding to the types of nutrients can be determined according to the historical dietary data; and determining the nutrient intake type of the target student according to the historical nutrient intake type and the historical intake amount corresponding to each type of nutrition.
In embodiments of the present invention, the types of nutritional intake may include protein intake, starch intake, vitamin intake, mineral intake, oil intake, and the like.
Specifically, the historical nutrient intake type of the target student and the historical intake amount corresponding to each type of nutrient can be determined according to dinning items, dinning time, dinning amount and the like in the historical dietary data. More specifically, the historical nutritional intake type of the target student can be determined through the dining product, and the historical intake amount corresponding to each type of nutrition of the target student can be determined according to the dining amount, for example, the dining product is a tomato fried egg, the dining amount is C g, and the protein intake amount a and the vitamin intake amount b corresponding to the C g tomato fried egg can be calculated according to the dining standard of a campus canteen, so that the historical nutritional intake type is protein intake and vitamin intake, the historical intake amount corresponding to the protein is a, and the historical intake amount corresponding to the vitamin is b.
The above-mentioned types of nutrient intake may include increasing intake of a certain nutrient type or decreasing intake of a certain nutrient type, and according to the historical intake and the historical intake corresponding to each type of nutrient, it is determined which nutrient type has been over-taken, which nutrient type has been under-taken or not-taken before the target student, the intake may be decreased for the over-taken nutrient type, and the intake may be increased for the under-taken or not-taken nutrient type, thereby obtaining the nutrient intake type of the target student.
Optionally, in the step of determining the intake demand data of the target student according to the activity data of the current day, the energy consumption data of the target student may be determined based on the action type and the corresponding action time; and determining the daily intake demand of the target student according to the energy consumption data.
In the embodiment of the invention, the energy required to be consumed in a unit time by one action type can be measured in advance by the existing measuring and calculating method, so that the energy consumption data of the target student can be determined based on the action type and the corresponding action time. For example, if the energy consumption data of the target student is 300 kcal, the daily intake demand of the target student may be 310 kcal.
Further, the daily intake demand of the target student may be slightly larger than the daily energy consumption, and at the time of the second meal, the daily intake demand needs to be subtracted from the daily intake demand at the time of the first meal, and at the time of the third meal, the daily intake demand needs to be subtracted from the daily intake demands at the time of the first meal and the second meal.
Optionally, in the step of generating the meal recommendation data of the target student according to the nutrition intake data and the intake demand data, a recommended meal item may be determined in the current-day meal item according to the nutrition intake type, where the recommended meal item includes the nutrition intake type; determining the recommended intake amount of the recommended food according to the intake demand on the day; and generating dining recommendation data of the target students according to the recommended food and the recommended intake.
In an embodiment of the present invention, the above-mentioned type of nutrient intake may include increasing intake of a certain nutrient type or decreasing intake of a certain nutrient type, and the recommended meal may be determined according to the nutrient type involved in the type of nutrient intake, and the recommended meal includes the nutrient type involved in the type of nutrient intake. The recommended intakes of the recommended food items can be calculated according to the daily intakes demand of the target students, for example, the recommended food items are E and F, the daily intake demand is G kcal, if the recommended intakes of the recommended food items E calculated according to the meal serving standard of the campus canteen are u grams and the recommended intakes of the recommended food items F are v grams, the energy corresponding to the u grams of the recommended food items E and the v grams of the recommended food items F is G kcal, and at this time, the meal recommendation data of the target students can be the u grams of the recommended food items E and the v grams of the recommended food items F.
According to the method and the device, the dining recommendation data can be generated according to the nutrition intake data and the intake demand data of the students, and the foods meeting the self nutrition demands are recommended to the students, so that the nutrition imbalance caused by long-term selection of favorite foods of the students is avoided, and the intelligent level of campus dining is improved.
It should be noted that the dining recommendation method provided by the embodiment of the present invention can be applied to devices such as an intelligent camera, an intelligent mobile phone, a computer, and a server that can perform the dining recommendation method.
Optionally, referring to fig. 2, fig. 2 is a schematic structural diagram of a dining recommendation device provided in an embodiment of the present invention, and as shown in fig. 2, the device includes:
the acquisition module 201 is configured to acquire historical diet data and daily activity data of target students in a campus;
a determining module 202, configured to determine nutritional intake data of the target student according to the historical diet data, and determine intake requirement data of the target student according to the activity data of the current day;
and the recommending module 203 is configured to generate meal recommending data of the target student according to the nutrition intake data and the intake demand data, and recommend meal to the target student based on the meal recommending data.
Optionally, the obtaining module 201 includes:
the acquisition sub-module is used for acquiring the body data of the target student;
the first determining submodule is used for determining a data acquisition time period according to the body data;
and the searching sub-module is used for searching data in a database based on the data acquisition time period to obtain historical diet data of the target students in the campus.
Optionally, the obtaining sub-module includes:
the acquisition unit is used for acquiring the campus monitoring video of the target student in the campus on the same day;
and the analysis unit is used for carrying out image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus.
Optionally, the analysis unit includes:
the identification subunit is used for carrying out action identification on the campus monitoring video to obtain the action types of the target students in the campus on the day and action time corresponding to each type of action;
and the determining subunit is used for determining the daily activity data of the target students in the campus based on the action types and the corresponding action time.
Optionally, the determining module 202 includes:
the second determining submodule is used for determining historical nutrient intake types and historical intake corresponding to various types of nutrients according to the historical diet data;
and the third determining submodule is used for determining the nutrient intake type of the target student according to the historical nutrient intake type and the historical intake corresponding to each type of nutrition.
Optionally, the determining module 202 includes:
a fourth determining sub-module, configured to determine energy consumption data of the target student based on the action type and the corresponding action time;
and the fifth determining submodule is used for determining the daily intake demand of the target student according to the energy consumption data.
Optionally, the recommending module 203 includes:
a sixth determining submodule, configured to determine, according to the nutrient intake type, a recommended food from the current-day food, where the recommended food includes the nutrient intake type;
the seventh determining submodule is used for determining the recommended intake of the recommended food according to the intake demand of the day;
and the generation module is used for generating the dining recommendation data of the target student according to the recommended meal and the recommended intake.
It should be noted that the dining recommendation device provided by the embodiment of the present invention can be applied to devices such as an intelligent camera, an intelligent mobile phone, a computer, and a server that can perform a dining recommendation method.
The dining recommendation device provided by the embodiment of the invention can realize each process realized by the dining recommendation method in the method embodiment, and can achieve the same beneficial effect. To avoid repetition, further description is omitted here.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 3, including: a memory 302, a processor 301 and a computer program stored on said memory 302 and operable on said processor 301, wherein:
the processor 301 is configured to call the computer program stored in the memory 302, and execute the following steps:
acquiring historical diet data and daily activity data of target students in a campus;
determining nutrient intake data of the target student according to the historical diet data, and determining intake demand data of the target student according to the daily activity data;
and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the target students to meals based on the meal recommendation data.
Optionally, the acquiring historical diet data of the target student in the campus performed by the processor 301 includes:
acquiring body data of the target student;
determining a data acquisition time period according to the body data;
and searching data in a database based on the data acquisition time period to obtain historical diet data of the target students in the campus.
Optionally, the acquiring of the day activity data of the target student in the campus performed by the processor 301 includes:
acquiring a campus monitoring video of the target student in the campus on the same day;
and carrying out image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus.
Optionally, the performing, by the processor 301, image analysis on the campus monitoring video to obtain the daily activity data of the target student in the campus includes:
performing action recognition on the campus monitoring video to obtain action types of the target students in the campus on the same day and action time corresponding to each type of action;
and determining the daily activity data of the target students in the campus based on the action types and the corresponding action time.
Optionally, the determining, by processor 301, nutrient intake data of the target student from the historical dietary data comprises:
determining historical nutrient intake types and historical intake amounts corresponding to various types of nutrients according to the historical diet data;
and determining the nutrient intake type of the target student according to the historical nutrient intake type and the historical intake amount corresponding to each type of nutrition.
Optionally, the determining intake requirement data of the target student according to the activity data of the current day by the processor 301 includes:
determining energy expenditure data of the target student based on the action type and the corresponding action time;
and determining the daily intake demand of the target student according to the energy consumption data.
Optionally, the generating, by the processor 301, meal recommendation data of the target student according to the nutrient intake data and the intake requirement data includes:
determining a recommended food in the current-day food according to the nutrient intake type, wherein the recommended food comprises the nutrient intake type;
determining the recommended intake amount of the recommended food according to the intake demand on the day;
and generating dining recommendation data of the target student according to the recommended food and the recommended intake.
The electronic equipment provided by the embodiment of the invention can realize each process realized by the dining recommendation method in the method embodiment, and can achieve the same beneficial effects. To avoid repetition, further description is omitted here.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the dining recommendation method provided in the embodiment of the present invention, and can achieve the same technical effect, and in order to avoid repetition, the computer program is not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only memory (RON), a random Access memory (RAN), or the like.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A method for recommending dining, comprising the steps of:
acquiring historical diet data and daily activity data of target students in a campus;
determining nutrient intake data of the target student according to the historical diet data, and determining intake demand data of the target student according to the daily activity data;
and generating meal recommendation data of the target students according to the nutrition intake data and the intake demand data, and recommending the target students to meals based on the meal recommendation data.
2. The meal recommendation method of claim 1, wherein said obtaining historical dietary data of target students on a campus comprises:
acquiring body data of the target student;
determining a data acquisition time period according to the body data;
and searching data in a database based on the data acquisition time period to obtain historical diet data of the target students in the campus.
3. The meal recommendation method of claim 2, wherein said obtaining day's activity data of target students on a campus comprises:
acquiring a campus monitoring video of the target student in the campus on the same day;
and carrying out image analysis on the campus monitoring video to obtain the daily activity data of the target students in the campus.
4. A dining recommendation method according to claim 3, wherein said image analyzing said campus surveillance video to obtain day's activity data of said target student on campus comprises:
performing action recognition on the campus monitoring video to obtain action types of the target students in the campus on the same day and action time corresponding to each type of action;
and determining the day activity data of the target student in the campus based on the action type and the corresponding action time.
5. The meal recommendation method of claim 4, wherein said determining nutrient intake data for said target student based on said historical dietary data comprises:
determining historical nutrient intake types and historical intake corresponding to various types of nutrients according to the historical dietary data;
and determining the nutrient intake type of the target student according to the historical nutrient intake type and the historical intake amount corresponding to each type of nutrition.
6. A meal recommendation method according to claim 5, wherein said determining intake requirement data of said target student based on said activities of the day data comprises:
determining energy expenditure data of the target student based on the action type and the corresponding action time;
and determining the daily intake demand of the target student according to the energy consumption data.
7. The meal recommendation method of claim 6, wherein generating meal recommendation data for the target student based on the nutrient intake data and the intake requirement data comprises:
determining a recommended food in the current-day food according to the nutrient intake type, wherein the recommended food comprises the nutrient intake type;
determining the recommended intake amount of the recommended food according to the intake demand on the day;
and generating dining recommendation data of the target students according to the recommended food and the recommended intake.
8. A meal recommendation device, the device comprising:
the acquisition module is used for acquiring historical diet data and daily activity data of the target students in the campus;
the determining module is used for determining the nutrition intake data of the target students according to the historical diet data and determining the intake demand data of the target students according to the daily activity data;
and the recommending module is used for generating meal recommending data of the target students according to the nutrition intake data and the intake demand data, and recommending the meals to the target students based on the meal recommending data.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the meal recommendation method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the dining recommendation method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116340597A (en) * | 2023-05-30 | 2023-06-27 | 平安云厨科技集团有限公司 | Accurate meal correction recommendation method and system |
| CN116682530A (en) * | 2023-04-12 | 2023-09-01 | 扬州万泓信息科技有限公司 | Big data-based analysis method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116682530A (en) * | 2023-04-12 | 2023-09-01 | 扬州万泓信息科技有限公司 | Big data-based analysis method |
| CN116340597A (en) * | 2023-05-30 | 2023-06-27 | 平安云厨科技集团有限公司 | Accurate meal correction recommendation method and system |
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