CN114203279A - Intelligent diet nutrition blending and optimizing method and device and electronic equipment - Google Patents
Intelligent diet nutrition blending and optimizing method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the specification provides a method for intelligently balancing and optimizing dietary nutrition, which comprises the steps of constructing a nutrition state prediction model by acquiring biological state information of a food demander, predicting nutrient unbalance states of the food demander according to the biological state information, including unbalanced nutrient types and unbalance amounts thereof, calculating nutrient deviation data by combining preset nutrient target data, screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending a dietary strategy for the food demander according to the screened food combinations. The nutrition types and the unbalance amount of the nutrition are predicted through the nutrition state prediction model, the deviation is automatically calculated, various food combinations are screened, manual balancing is not needed, and the efficiency and the accuracy are high.
Description
Technical Field
The application relates to the field of computers, in particular to a method and a device for intelligent diet nutrition blending and optimization and electronic equipment.
Background
With the improvement of living standard of people, more and more groups and individuals pay attention to the reasonability of dietary nutrition, so that the people can have a healthier body. In recent years, the informatization level of various industries is increasing, and the nutritional field also needs informatization and intelligent solutions. The intelligent diet nutrition balancing method is characterized in that advanced informatization technology is applied to the diet nutrition field, so that individuals and groups can quickly find a diet balancing and optimizing scheme.
The diet nutrition matching and optimization application is intensively developed around 2010 and mainly applied to hospital scenes and kindergarten scenes, so that doctors in nutrition departments of hospitals can be helped to professionally prepare meals for patients and meet personal physiological characteristics, and the kindergarten scenes can be helped by schools to scientifically prepare meals for infants and meet the requirements of national conservation.
However, by far, most dietary nutrition balancing and optimization applications still remain in manual adjustment of nutrients, and the method depends on highly professional human balancing, so that the balancing result is subjective and extensive, and the accuracy is low.
Disclosure of Invention
The embodiment of the specification provides a method, a device and an electronic device for intelligent diet nutrition distribution and optimization, and the method, the device and the electronic device are used for improving efficiency and accuracy.
The embodiment of the specification provides a method for intelligently matching and optimizing dietary nutrition, which comprises the following steps:
acquiring biological state information of a food demander;
constructing a nutrition state prediction model, predicting the nutrient unbalance state of the food demander according to the biological state information, including the unbalanced nutrient category and the unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data;
and screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending a diet strategy for the food demanders according to the screened food combinations.
Optionally, the constructing a nutritional status prediction model comprises:
obtaining the height, weight, body fat and blood pressure information of a sample user and the nutritional state identification result of the sample user, setting a label according to the nutritional state identification result of the sample user, and training a nutritional state prediction model.
Optionally, the screening of various food combinations in combination with the nutrient deviation data and nutrient data corresponding to various foods includes:
constructing a multi-objective function which takes each nutrient as a target and takes the amount of each food as a variable, configuring corresponding coefficients for the variables of the multi-objective function according to the content of each nutrient in each food, and constructing a constraint function of the multi-objective function;
and screening variable value combinations meeting the nutrient deviation data and the food amount under the combinations according to the constraint function.
Optionally, the constructing a constraint function of the multi-objective function includes:
constructing a constraint function of the multi-objective function in combination with the weight and price of various foods, the constraint function having a cost index and a total weight index.
Optionally, the method further comprises:
and calculating nutrient data after nutrient interference in the food combination by combining the influence coefficients among the nutrients.
Optionally, the acquiring the biological state information of the food demander comprises:
the method comprises the steps of obtaining a facial image of a food demander, carrying out facial recognition on the food demander, and obtaining height, weight, body fat and blood pressure information of the food demander from a database according to the recognized identity.
The present specification further provides an apparatus for intelligent balancing and optimization of dietary nutrition, including:
the information acquisition module is used for acquiring biological state information of the food demander;
the unbalanced state module is used for constructing a nutrition state prediction model, predicting the nutrient unbalanced state of the food demander according to the biological state information, including unbalanced nutrient types and unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data;
and the diet strategy module is used for screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending diet strategies for the food demanders according to the screened food combinations.
An embodiment of the present specification further provides an electronic device, where the electronic device includes:
a processor; and a memory storing a computer executable program which, when executed, causes the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.
According to various technical schemes provided by the embodiment of the specification, a nutrition state prediction model is constructed by obtaining biological state information of a food demander, the nutrition unbalance state of the food demander is predicted according to the biological state information, the nutrition unbalance state comprises unbalanced nutrition types and unbalance amount thereof, nutrition deviation data is calculated by combining preset nutrition target data, various food combinations are screened by combining the nutrition deviation data and the nutrition data corresponding to various foods, and a diet strategy is recommended for the food demander according to the screened food combinations. The nutrition types and the unbalance amount of the nutrition are predicted through the nutrition state prediction model, the deviation is automatically calculated, various food combinations are screened, manual balancing is not needed, and the efficiency and the accuracy are high.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a method for intelligent balancing and optimizing dietary nutrition provided in an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of an apparatus for intelligent balancing and optimizing dietary nutrition provided in an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for intelligently balancing and optimizing dietary nutrition according to an embodiment of the present disclosure, where the method may include:
s101: obtaining the biological state information of the food demander.
In the embodiment of the present specification, the biological state information may be height, weight, body fat, blood pressure, and other information reflecting the health status of the human body, so as to reflect whether the ingested nutrients are too much or too little.
In an embodiment of the present specification, the acquiring biological status information of the food demander includes:
the method comprises the steps of obtaining a facial image of a food demander, carrying out facial recognition on the food demander, and obtaining height, weight, body fat and blood pressure information of the food demander from a database according to the recognized identity.
S102: and constructing a nutrition state prediction model, predicting the nutrient unbalance state of the food demander according to the biological state information, including the unbalanced nutrient category and the unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data.
In order to be able to accurately identify nutrient imbalance states from biological state information, we can construct a nutrient state prediction model.
In the embodiment of the present specification, the nutrition state prediction model may be constructed in a machine learning manner, and may also be constructed in a regression manner, which is not described in detail herein.
The unbalanced nutrient category refers to nutrition which is too much or too little ingested, and the nutrient category can be vitamin A, B, C and the like, can also be trace elements such as calcium, iron, zinc and the like, and can also be energy-consuming substances such as protein, fat, sugar and the like.
In an embodiment of the present disclosure, the constructing a nutritional status prediction model may include:
obtaining the height, weight, body fat and blood pressure information of a sample user and the nutritional state identification result of the sample user, setting a label according to the nutritional state identification result of the sample user, and training a nutritional state prediction model.
Wherein the nutritional status identification result may include the identified unbalanced nutrient class and the unbalance amount thereof.
Therefore, after the height, weight, body fat and blood pressure information of the food demander to be eaten are obtained, the unbalanced nutrient type and the unbalanced amount thereof can be accurately identified by using the model.
Thus, in combination with normal nutrient intake, it is possible to obtain what nutrients should be supplemented or taken up to a lesser extent, and specific amounts thereof.
Wherein the nutrient target data may be a standard intake.
Wherein the nutrient deviation data refers to the difference between the current nutritional status of the food consumer and the standard intake.
This is corrected by using the equivalent amount of the nutrient at the current time. Can not completely absorb or decompose the used nutrients which are taken in the past, and avoid excessive intake.
S103: and screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending a diet strategy for the food demanders according to the screened food combinations.
The method comprises the steps of obtaining biological state information of a food demander, constructing a nutrition state prediction model, predicting nutrient unbalance states of the food demander according to the biological state information, including unbalanced nutrient types and unbalance amounts thereof, calculating nutrient deviation data by combining preset nutrient target data, screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending a diet strategy for the food demander according to the screened food combinations. The nutrition types and the unbalance amount of the nutrition are predicted through the nutrition state prediction model, the deviation is automatically calculated, various food combinations are screened, manual balancing is not needed, and the efficiency and the accuracy are high.
Wherein each food combination has a specific ratio of food amounts.
The screening may be performed by linear programming.
Linear programming is an important tool for solving operational research optimization problems. One mathematical programming involves determining decision variables, presenting an objective function, and listing constraints. When in mathematical programming both the objective function and the constraint function are linear functions, the mathematical programming is called linear programming. In the problem, the objective function is to obtain a catering scheme with a high score, i.e. an optimal or near-optimal solution, according to a given weekly cuisine.
In an embodiment of the present specification, the screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods includes:
constructing a multi-objective function which takes each nutrient as a target and takes the amount of each food as a variable, configuring corresponding coefficients for the variables of the multi-objective function according to the content of each nutrient in each food, and constructing a constraint function of the multi-objective function;
and screening variable value combinations meeting the nutrient deviation data and the food amount under the combinations according to the constraint function.
In an embodiment of the present specification, the constructing a constraint function of the multi-objective function includes:
constructing a constraint function of the multi-objective function in combination with the weight and price of various foods, the constraint function having a cost index and a total weight index.
In the embodiment of this specification, still include:
and calculating nutrient data after nutrient interference in the food combination by combining the influence coefficients among the nutrients.
There is a certain period in which absorption and exertion of nutrients are considered, and there are effects such as calcium, vitamins.
These periods may span multiple eating times, for example, where a certain nutrient is taken in the morning and no longer needed at noon.
Therefore, in the embodiments of the present specification, the calculating of the nutrient deviation data in combination with the preset nutrient target data includes:
acquiring the nutrition duration of each nutrient, determining the equivalent amount of each nutrient implementing the historical dietary strategy at the current moment in the current intelligent balancing period according to the nutrition duration, calculating nutrient deviation data by combining nutrient target data, the unbalanced nutrient category and the unbalanced amount thereof, and correcting the nutrient deviation data by using the equivalent amount of the nutrient at the current moment.
Wherein, the nutrition duration is the duration of the effect of the nutrient, and the intelligent balancing period can be one week or two weeks.
Wherein the equivalent amount at the present time may be the nutrient intake minus the amount already absorbed and utilized.
In an embodiment of the present specification, a recipe table may be generated.
In specific implementation, the method can comprise the following steps:
step 1: determining a weekly diet. The method mainly comprises the following steps of arranging meals a week for days, classifying each meal in one day, using raw materials for dishes of each meal, the weight, price and content of nutrients in each raw material, adjusting the weight parameters of each food material raw material, and grading standards of daily demand and actual intake of each nutrient;
step 2: determining whether the dishes are adjustable in equal proportion;
and step 3: determining a decision variable xsjI.e. the weight of the jth food material in the s day, j ∈ Nl，NlNumbering food materials;
and 4, step 4: judging whether the quality and the price pass the inspection;
step 4.1, if the test is passed, a constraint function of the multi-objective function is called;
and 4.2, if the food material quality fails to pass the inspection (the total quality of the food material exceeds or falls short of the quality of the food material or the total price exceeds or falls short of the quality of the food material on a certain day), outputting a corresponding prompt, and adjusting the menu according to the prompt. Return to step 1
And 5: the maximum value of the weekly total score is obtained according to the constraint function of the multi-objective function, and the daily total score value and the variable value (namely the amount of various foods) corresponding to the solution are simultaneously output.
Fig. 2 is a schematic structural diagram of an apparatus for intelligently balancing and optimizing dietary nutrition provided in an embodiment of the present disclosure, where the apparatus may include:
the information acquisition module 201 acquires the biological state information of the food demander;
the unbalanced state module 202 is used for constructing a nutrition state prediction model, predicting the nutrient unbalanced state of the food demander according to the biological state information, including unbalanced nutrient types and unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data;
and the diet strategy module 203 is used for screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending diet strategies for the food demanders according to the screened food combinations.
Optionally, the constructing a nutritional status prediction model comprises:
obtaining the height, weight, body fat and blood pressure information of a sample user and the nutritional state identification result of the sample user, setting a label according to the nutritional state identification result of the sample user, and training a nutritional state prediction model.
Optionally, the screening of various food combinations in combination with the nutrient deviation data and nutrient data corresponding to various foods includes:
constructing a multi-objective function which takes each nutrient as a target and takes the amount of each food as a variable, configuring corresponding coefficients for the variables of the multi-objective function according to the content of each nutrient in each food, and constructing a constraint function of the multi-objective function;
and screening variable value combinations meeting the nutrient deviation data and the food amount under the combinations according to the constraint function.
Optionally, the constructing a constraint function of the multi-objective function includes:
constructing a constraint function of the multi-objective function in combination with the weight and price of various foods, the constraint function having a cost index and a total weight index.
Optionally, the method further comprises:
and calculating nutrient data after nutrient interference in the food combination by combining the influence coefficients among the nutrients.
Optionally, the acquiring the biological state information of the food demander comprises:
the method comprises the steps of obtaining a facial image of a food demander, carrying out facial recognition on the food demander, and obtaining height, weight, body fat and blood pressure information of the food demander from a database according to the recognized identity.
The device constructs a nutrition state prediction model by acquiring biological state information of a food demander, predicts the nutrient unbalance state of the food demander according to the biological state information, including unbalanced nutrient types and unbalance amount thereof, calculates nutrient deviation data by combining preset nutrient target data, screens various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommends a diet strategy for the food demander according to the screened food combinations. The nutrition types and the unbalance amount of the nutrition are predicted through the nutrition state prediction model, the deviation is automatically calculated, various food combinations are screened, manual balancing is not needed, and the efficiency and the accuracy are high.
Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.
Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.
The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache storage unit 3202, and may further include a read only memory unit (ROM) 3203.
The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAI D systems, tape drives, and data backup storage systems, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.
Fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (9)
1. An apparatus for intelligent dietary nutrition balancing and optimization, comprising:
the information acquisition module is used for acquiring biological state information of the food demander;
the unbalanced state module is used for constructing a nutrition state prediction model, predicting the nutrient unbalanced state of the food demander according to the biological state information, including unbalanced nutrient types and unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data;
and the diet strategy module is used for screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending diet strategies for the food demanders according to the screened food combinations.
2. The apparatus of claim 1, wherein the constructing a nutritional status prediction model comprises:
obtaining the height, weight, body fat and blood pressure information of a sample user and the nutritional state identification result of the sample user, setting a label according to the nutritional state identification result of the sample user, and training a nutritional state prediction model.
3. The apparatus of claim 1, wherein said screening of various food combinations in combination with said nutrient deviation data and nutrient data corresponding to various foods comprises:
constructing a multi-objective function which takes each nutrient as a target and takes the amount of each food as a variable, configuring corresponding coefficients for the variables of the multi-objective function according to the content of each nutrient in each food, and constructing a constraint function of the multi-objective function;
and screening variable value combinations meeting the nutrient deviation data and the food amount under the combinations according to the constraint function.
4. The apparatus of claim 3, wherein the constructing the constraint function of the multi-objective function comprises:
constructing a constraint function of the multi-objective function in combination with the weight and price of various foods, the constraint function having a cost index and a total weight index.
5. The apparatus of claim 1, further comprising:
and calculating nutrient data after nutrient interference in the food combination by combining the influence coefficients among the nutrients.
6. The apparatus of claim 1, wherein the obtaining of the biological status information of the food demander comprises:
the method comprises the steps of obtaining a facial image of a food demander, carrying out facial recognition on the food demander, and obtaining height, weight, body fat and blood pressure information of the food demander from a database according to the recognized identity.
7. A method for intelligent blending and optimization of dietary nutrition, comprising:
acquiring biological state information of a food demander;
constructing a nutrition state prediction model, predicting the nutrient unbalance state of the food demander according to the biological state information, including the unbalanced nutrient category and the unbalanced amount thereof, and calculating nutrient deviation data by combining preset nutrient target data;
and screening various food combinations by combining the nutrient deviation data and nutrient data corresponding to various foods, and recommending a diet strategy for the food demanders according to the screened food combinations.
8. An electronic device, wherein the electronic device comprises:
a processor; and a memory storing a computer executable program which, when executed, causes the processor to perform the method of claim 7.
9. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claim 7.
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