CN111782902A - Food material recommendation method and system, electronic device and storage medium - Google Patents
Food material recommendation method and system, electronic device and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a system for recommending food materials, electronic equipment and a storage medium, wherein the method for recommending food materials comprises the following steps: acquiring a target food material image to be processed; identifying different types of target food materials in the target food material image; acquiring target characteristic dimension information corresponding to the target food material, and acquiring a first edible value of the target food material according to the target characteristic dimension information; and obtaining a food material recommendation result according to the first edible value of each target food material and recommending the food material recommendation result. According to the method, the corresponding eating priority of each food material can be obtained, the higher the eating priority is, the more the food materials are recommended to be eaten up as soon as possible, so that the food material value is maximized, and the waste of the food materials is effectively reduced; in addition, the menu with higher score is determined according to the eating priority of the food materials so as to recommend the appropriate menu, so that the user can conveniently match the food materials, the eating value of the food materials is utilized to the maximum, and the use experience of the user is improved.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a method and a system for recommending food materials, electronic equipment and a storage medium.
Background
If too much food is stored for a long time in the refrigerator, the food in the refrigerator is rotten. If the food is not taken out in time, other fresh food materials are also affected by the odor of the rotten food, the rotten speed is increased, the whole refrigerator environment is damaged, and strong peculiar smell is generated.
Currently, an air freshener is generally used to absorb peculiar smell, or a reminder is set to notify a user regularly to check whether food materials in a refrigerator are rotten or not so as to be processed in time. However, the treatment mode of the refrigerator air freshener is 'addressing the symptoms and not addressing the causes', and the mode of reminding the user to take out the food after the food is not fresh does not solve the problem of the non-fresh food from a high source, so that the actual use requirement of the user cannot be met.
Disclosure of Invention
The invention aims to overcome the defects that in the prior art, a user cannot be informed of various food materials in a refrigerator in time, food waste is easily caused, and the like, and aims to provide a food material recommending method, a food material recommending system, electronic equipment and a storage medium.
The invention solves the technical problems through the following technical scheme:
the invention provides a food material recommendation method, which comprises the following steps:
acquiring a target food material image to be processed;
identifying different types of target food materials in the target food material image;
acquiring target characteristic dimension information corresponding to the target food material, and acquiring a first edible value of the target food material according to the target characteristic dimension information;
and obtaining a food material recommendation result according to the first edible value of each target food material and recommending the food material recommendation result.
Preferably, the step of obtaining and recommending food material recommendation results according to the first food value of each target food material further includes:
acquiring a first food material corresponding to each menu;
obtaining a second edible value of each said recipe according to said first edible value of said first food material;
and obtaining a menu recommendation result according to the second edible value of each menu and recommending.
Preferably, the step of obtaining and recommending food material recommendation results according to the first food value of each target food material includes:
determining the eating priority of each target food material according to the first eating value;
wherein the higher the first consumption value of the target food material, the higher the consumption priority;
and sequentially sorting according to the food priority to generate a food material recommendation table, and recommending food materials according to the food material recommendation table.
Preferably, the step of obtaining a second eating value of each recipe according to the first eating value of the first food material comprises:
acquiring a recommendation score corresponding to each first food material in the menu according to the eating priority of each first food material;
the step of obtaining and recommending the recipe recommendation result according to the second edible value of each recipe comprises the following steps:
and sequentially sorting according to the recommendation scores of each menu to generate a menu recommendation table, and recommending the menu according to the menu recommendation table.
Preferably, the step of obtaining the first food value of the target food material according to the target feature dimension information further includes:
acquiring historical edible value and historical characteristic dimension information corresponding to each target food material;
taking the historical characteristic dimension information as input and the historical edible value as output, and establishing an edible value determination model of the target food material;
the step of obtaining the first edible value of the target food material according to the target characteristic dimension information comprises the following steps:
inputting the target characteristic dimension information into the edible value determination model to obtain the first edible value of the target food material.
Preferably, the formula corresponding to the step of inputting the target feature dimension information into the edible value determination model to obtain the first edible value of the target food material is as follows:
wherein k represents a k type of target food material, i represents the number of the target characteristic dimension information, and Wi (k)Representing the weight corresponding to the ith target characteristic dimension information,
representing the ith dimension information of the target feature,
a bias parameter F (X) representing the dimension information of the ith target feature(k)) Representing the first food value; wherein i is more than or equal to 2 and is a positive integer.
Preferably, the target feature dimension information includes at least one of market average price information, freshness information, historical eating history information of the user, eating preference score information of the user, and nutritional value information.
The invention also provides a food material recommendation system, which comprises:
the image acquisition module is used for acquiring a target food material image to be processed;
the food material identification module is used for identifying different types of target food materials in the target food material image;
the dimension information acquisition module is used for acquiring target characteristic dimension information corresponding to the target food material;
the first edible value acquisition module is used for acquiring a first edible value of the target food material according to the target characteristic dimension information;
and the food material recommending module is used for acquiring and recommending food material recommending results according to the first eating value of each target food material.
Preferably, the recommendation system further comprises:
the first food material acquisition module is used for acquiring first food materials corresponding to each menu;
a second edible value obtaining module, configured to obtain a second edible value of each recipe according to the first edible value of the first food material;
and the menu recommendation module is used for obtaining and recommending menu recommendation results according to the second edible value of each menu.
Preferably, the food material recommending module comprises:
the food priority determining unit is used for determining the food priority of each target food material according to the first food value;
wherein the higher the first consumption value of the target food material, the higher the consumption priority;
and the food material recommending unit is used for sequentially sorting according to the food priority to generate a food material recommending table and recommending food materials according to the food material recommending table.
Preferably, the second eating value acquiring module is configured to acquire a recommendation score corresponding to each of the recipes according to the eating priority of each of the first food materials in the recipe;
the menu recommending module is used for sequentially sequencing according to the recommendation scores of all the menus to generate a menu recommending table and recommending the menus according to the menu recommending table.
Preferably, the recommendation system further comprises:
the historical information acquisition module is used for acquiring historical edible value and historical characteristic dimension information corresponding to each target food material;
the model establishing module is used for establishing an edible value determining model of the target food material by taking the historical characteristic dimension information as input and the historical edible value as output;
the first edible value obtaining module is used for inputting the target characteristic dimension information into the edible value determining model to obtain the first edible value of the target food material.
Preferably, the formula corresponding to the target characteristic dimension information input into the edible value determination model by the first edible value obtaining module to obtain the first edible value of the target food material is as follows:
wherein k represents a k type of target food material, i represents the number of the target characteristic dimension information, and Wi (k)Representing the weight corresponding to the ith target characteristic dimension information,
representing the ith dimension information of the target feature,
a bias parameter F (X) representing the dimension information of the ith target feature(k)) Representing the first food value; wherein i is more than or equal to 2 and is a positive integer.
Preferably, the target feature dimension information includes at least one of market average price information, freshness information, historical eating history information of the user, eating preference score information of the user, and nutritional value information.
The invention further provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the food material recommendation method when executing the computer program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for recommending food materials as described above.
On the basis of the common knowledge in the field, the preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
according to the method, the types of different food materials in the refrigerator are obtained through an image recognition technology, then the eating values of the different food materials are determined according to the market average price information, the freshness information, the historical eating record information of the user, the eating preference score information of the user, the nutritive value information and the like of each food material so as to obtain the eating priority of each food material, the food materials are recommended to be eaten up as soon as possible as the eating priority is higher, the food material value is maximized, and the waste of the food materials is effectively reduced; in addition, the menu with higher score is determined according to the eating priority of the food materials so as to recommend the appropriate menu, so that the user can conveniently match the food materials, the eating value of the food materials is utilized to the maximum, and the use experience of the user is improved.
Drawings
Fig. 1 is a flowchart of a food material recommendation method according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a food material recommendation method according to embodiment 2 of the present invention.
Fig. 3 is a schematic structural diagram of a food material recommendation system according to embodiment 3 of the present invention.
Fig. 4 is a schematic structural diagram of a food material recommendation system according to embodiment 4 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a food material recommendation method according to embodiment 4 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the method for recommending food materials of this embodiment includes:
s101, obtaining a target food material image to be processed;
s102, identifying different types of target food materials in the target food material image;
for example, an image recognition apparatus is installed in a refrigerator to capture images of various food materials in the refrigerator, and then analyze and process the images and recognize types of food materials actually stored in the refrigerator, such as broccoli, tomatoes, cucumbers, and the like. Identifying specific food materials from the images is a well-established technique in the art and therefore will not be described herein.
S103, obtaining target characteristic dimension information corresponding to the target food material, and obtaining a first edible value of the target food material according to the target characteristic dimension information;
the target feature dimension information includes, but is not limited to, market average price information, freshness information, historical eating record information of the user, eating preference score information of the user, and nutritional value information.
For the average market price information X1, the cloud server may calculate the average market price information according to the stored prices of the food materials in different local markets by sending the food material types to the cloud server.
As for the freshness degree information X2, the food color, the surface state, and the like in the target food material image can be acquired by an image processing technique, and the freshness degree of each food material is determined by a gas sensor (a specific gas concentration of ethanol, sulfide, ammonia, and the like) arranged in the refrigerator.
Wherein, the freshness degree is classified into edible and inedible; the edible food is further classified into a plurality of grades (L grades), and the higher the grade is, the lower the freshness is. The food material recommendation is not performed on the unavailable food material, but the user is informed to process the unavailable food material in time by means of a mode of displaying reminding information on a screen and the like (when a display screen is arranged on a refrigerator).
For food materials whose freshness cannot be determined by images and flavors, such as cans, the freshness X2 can be determined by the user manually inputting the production date and the expiration date, and the calculation method is as follows:
wherein, tJinjin teaIndicates input of production date tCutting blockIndicating the expiration date of consumption.
For the historical eating record information X3 of the user, the times i and the content M of taking out a certain food material from the refrigerator within a period of time by the user are recordediAnd time ti(ii) a The eating record value X3 was calculated as follows:
t0the larger the value of X3, which represents the current time, is the closer the time is to the last eating of the food material or the excessive eating of the food material. In particular, if there is no record of consumption of the food material for a period of time, X3 is 0.
For the eating preference score information X4 of the user, acquiring which food material the user puts into the refrigerator each time by recording an accumulated value of a certain food material stored in the refrigerator by the user within a period of time, namely acquiring the content of the food material by image recognition and the like, wherein the content recording mode includes but is not limited to the volume, the weight and the average meal amount of the food material; the larger the accumulated value is, the higher the preference score of the user for the food material is.
For the nutritional value information X5, analyzing the nutritional ingredients ingested by the user within a period of time through the food record to obtain the most lacking nutritional ingredients currently of the user, deducing whether the food material contains the nutritional ingredients lacking in the user, and if not, recording X5 as 0; if so, the X5 value is determined by the content of the several nutritional ingredients contained in the food material. If the analysis result shows that the current user lacks A, B and C, but the current food material only contains A, B nutritional ingredients a and b in unit weight, and the nutritional value score of the food material is X5 ═ k1a+k2b, wherein k1And k2Are all constants, and the specific values are determined or adjusted according to actual conditions.
Of course, other related information, such as the physical state information of the user, can also be incorporated into the food material recommendation process according to the actual situation, and the re-determination and adjustment can be performed according to the specific actual situation, so as to meet the higher use recommendation requirement.
And S104, obtaining and recommending a food material recommending result according to the first edible value of each target food material.
In the embodiment, the types of different food materials in the refrigerator are obtained through an image recognition technology, then the eating values of the different food materials are determined according to the market average price information, the freshness information, the historical eating record information of the user, the eating preference score information of the user, the nutritive value information and the like of each food material so as to obtain the eating priority of each food material, the higher the eating priority is, the more the food materials are recommended to be eaten up as soon as possible, so that the food material value maximization is achieved, the waste of the food materials is effectively reduced, and the use experience of the user is improved.
Example 2
As shown in fig. 2, the method for recommending food materials in this embodiment is a further improvement of embodiment 1, specifically:
after step S102 and before step S103, the method further includes:
s10301, obtaining historical edible value and historical characteristic dimension information corresponding to each target food material;
s10302, taking the historical characteristic dimension information as input and the historical edible value as output, and establishing an edible value determination model of the target food material.
The step of determining the food value model may be performed before step S103.
Step S103 includes:
and S1031, inputting the target characteristic dimension information into the edible value determination model to obtain a first edible value of the target food material.
Specifically, the target characteristic dimension information is input into the edible value determination model to obtain a formula corresponding to the step of obtaining the first edible value of the target food material:
wherein k represents k types of target food materials, i represents the number of target characteristic dimension information, and Wi (k)Representing the weight corresponding to the dimension information of the ith target feature,
representing the dimension information of the ith target feature,
a bias parameter F (X) corresponding to the dimension information of the ith target feature(k)) Representing a first food value; wherein i is more than or equal to 2 and is a positive integer.
Step S104 includes:
s1041, determining the eating priority of each target food material according to the first eating value;
wherein the higher the first eating value of the target food material is, the higher the eating priority is;
s1042, sequentially sorting according to the food priority to generate a food material recommendation table, and recommending food materials according to the food material recommendation table.
The food material recommendation table can be formed by sequencing various food materials according to the descending order of the food priority, the food materials are more recommended to be used up as soon as the food materials are used up before the sequencing is higher, so that the food material value is maximized, the waste of the food materials is effectively reduced, and the higher use requirement of a user is met.
Step S104 is followed by:
s105, obtaining a first food material corresponding to each menu;
s106, acquiring a second edible value of each menu according to the first edible value of the first food material;
and S107, obtaining a menu recommendation result according to the second edible value of each menu and recommending.
Specifically, step S106 includes:
acquiring a recommendation score corresponding to each menu according to the eating priority of each first food material in the menu;
step S107 includes:
and sequentially sorting according to the recommendation scores of all the recipes to generate a recipe recommendation table, and recommending the recipes according to the recipe recommendation table.
For example, in the food material recommendation table, the corn is ranked first, the eating priority is F1, the spareribs are ranked third, the eating priority is F3, the green beans are ranked fifth, and the eating priority is F5, so that the priority of the recommended recipe should be better than that of the corn roasted green beans (F1+ F3> F1+ F5).
The various recipes can be sorted in a descending order according to the recommendation scores to form a recipe recommendation table, the more the sorting is, the more the foods are recommended, the higher or the highest scored recipes are determined to serve as the recipes to be recommended according to the food priority of the food materials, the food materials are conveniently collocated by a user, the food value of the food materials is utilized to the maximum, and the use experience of the user is improved.
In the embodiment, the types of different food materials in the refrigerator are obtained through an image recognition technology, and then the eating values of the different food materials are determined according to the market average price information, the freshness information, the historical eating record information of the user, the eating preference score information of the user, the nutritive value information and the like of each food material so as to obtain the eating priority of each food material, the higher the eating priority is, the more the food materials are recommended to be eaten up as soon as possible, so that the food material value maximization is achieved, and the waste of the food materials is effectively reduced; in addition, the menu with higher score is determined according to the eating priority of the food materials so as to recommend the appropriate menu, so that the user can conveniently match the food materials, the eating value of the food materials is utilized to the maximum, and the use experience of the user is improved.
Example 3
As shown in fig. 3, the food material recommending system of the present embodiment includes an image obtaining module 1, a food material identifying module 2, a dimension information obtaining module 3, a first edible value obtaining module 4, and a food material recommending module 5.
The image acquisition module 1 is used for acquiring an image of a target food material to be processed.
The food material identification module 2 is used for identifying different types of target food materials in the target food material image.
For example, an image recognition apparatus is installed in a refrigerator to capture images of various food materials in the refrigerator, and then analyze and process the images and recognize types of food materials actually stored in the refrigerator, such as broccoli, tomatoes, cucumbers, and the like. Identifying specific food materials from the images is a well-established technique in the art and therefore will not be described herein.
The dimension information acquisition module 3 is used for acquiring target characteristic dimension information corresponding to a target food material;
the target feature dimension information includes, but is not limited to, market average price information, freshness information, historical eating record information of the user, eating preference score information of the user, and nutritional value information.
For the average market price information X1, the cloud server may calculate the average market price information according to the stored prices of the food materials in different local markets by sending the food material types to the cloud server.
As for the freshness degree information X2, the food color, the surface state, and the like in the target food material image can be acquired by an image processing technique, and the freshness degree of each food material is determined by a gas sensor (a specific gas concentration of ethanol, sulfide, ammonia, and the like) arranged in the refrigerator.
Wherein, the freshness degree is classified into edible and inedible; the edible food is further classified into a plurality of grades (L grades), and the higher the grade is, the lower the freshness is. The food material recommendation is not performed on the unavailable food material, but the user is informed to process the unavailable food material in time by means of a mode of displaying reminding information on a screen and the like (when a display screen is arranged on a refrigerator).
In addition, for food materials, such as cans, for which the freshness cannot be determined by the images and the flavors, the freshness X of the food material can be determined by manually inputting the production date and the expiration date by the user, and the calculation method is as follows:
wherein, tJinjin teaIndicates input of production date tCutting blockIndicating the expiration date of consumption.
For the historical eating history information X3 of the userRecording the times i and content M of a certain food material taken out of the refrigerator by a user within a period of timeiAnd time ti(ii) a The eating record value X3 was calculated as follows:
t0the larger the value of X3, which represents the current time, is the closer the time is to the last eating of the food material or the excessive eating of the food material. In particular, if there is no record of consumption of the food material for a period of time, X3 is 0.
For the eating preference score information X4 of the user, acquiring which food material the user puts into the refrigerator each time by recording an accumulated value of a certain food material stored in the refrigerator by the user within a period of time, namely acquiring the content of the food material by image recognition and the like, wherein the content recording mode includes but is not limited to the volume, the weight and the average meal amount of the food material; the larger the accumulated value is, the higher the preference score of the user for the food material is.
For the nutritional value information X5, analyzing the nutritional ingredients ingested by the user within a period of time through the food record to obtain the most lacking nutritional ingredients currently of the user, deducing whether the food material contains the nutritional ingredients lacking in the user, and if not, recording X5 as 0; if so, the X5 value is determined by the content of the several nutritional ingredients contained in the food material. If the analysis result shows that the current user lacks A, B and C, but the current food material only contains A, B nutritional ingredients a and b in unit weight, and the nutritional value score of the food material is X5 ═ k1a+k2b, wherein k1And k2Are all constants, and the specific values are determined or adjusted according to actual conditions.
Of course, other related information, such as the physical state information of the user, can also be incorporated into the food material recommendation process according to the actual situation, and the re-determination and adjustment can be performed according to the specific actual situation, so as to meet the higher use recommendation requirement.
The first edible value acquisition module 4 is used for acquiring a first edible value of the target food material according to the target characteristic dimension information;
the food material recommending module 5 is configured to obtain a food material recommending result according to the first food value of each target food material and recommend the food material recommending result.
In the embodiment, the types of different food materials in the refrigerator are obtained through an image recognition technology, then the eating values of the different food materials are determined according to the market average price information, the freshness information, the historical eating record information of the user, the eating preference score information of the user, the nutritive value information and the like of each food material so as to obtain the eating priority of each food material, the higher the eating priority is, the more the food materials are recommended to be eaten up as soon as possible, so that the food material value maximization is achieved, the waste of the food materials is effectively reduced, and the use experience of the user is improved.
Example 4
As shown in fig. 4, the food material recommendation system of the present embodiment is a further improvement of embodiment 3, specifically:
the recommendation system further comprises a history information acquisition module 6 and a model building module 7.
The historical information acquisition module 6 is used for acquiring historical edible value and historical characteristic dimension information corresponding to each target food material;
the model establishing module 7 is used for establishing an edible value determining model of the target food material by taking the historical characteristic dimension information as input and the historical edible value as output;
the first edible value obtaining module 4 is configured to input the target feature dimension information into the edible value determination model to obtain a first edible value of the target food material.
The first edible value obtaining module 4 inputs the target characteristic dimension information into the edible value determining model to obtain a formula corresponding to the first edible value of the target food material, and the formula is as follows:
k represents a target food material of k types, i represents the number of target characteristic dimension information, and Wi (k)Representing the weight corresponding to the dimension information of the ith target feature,
representing the dimension information of the ith target feature,
a bias parameter F (X) corresponding to the dimension information of the ith target feature(k)) Representing a first food value; wherein i is more than or equal to 2 and is a positive integer.
The food material recommending module 5 comprises an eating priority determining unit 8 and a food material recommending unit 9.
The eating priority determining unit 8 is used for determining the eating priority of each target food material according to the height of the first eating value;
wherein the higher the first eating value of the target food material is, the higher the eating priority is;
the food material recommending unit 9 is configured to sequentially sort according to the eating priorities to generate a food material recommending table, and recommend food materials according to the food material recommending table.
The food material recommendation table can be formed by sequencing various food materials according to the descending order of the food priority, the food materials are more recommended to be used up as soon as the food materials are used up before the sequencing is higher, so that the food material value is maximized, the waste of the food materials is effectively reduced, and the higher use requirement of a user is met.
The recommending system further comprises a first food material acquiring module 10, a second food value acquiring module 11 and a menu recommending module 12.
The first food material obtaining module 10 is configured to obtain a first food material corresponding to each menu;
the second edible value acquiring module 11 is configured to acquire a second edible value of each recipe according to the first edible value of the first food material;
the recipe recommending module 12 is configured to obtain a recipe recommending result according to the second eating value of each recipe and recommend the recipe.
Specifically, the second edible value obtaining module 11 is configured to obtain a recommendation score corresponding to each recipe according to the edible priority of each first food material in the recipe;
the menu recommending module 12 is configured to sequentially sort the menus according to the recommendation scores of each menu to generate a menu recommending table, and recommend the menus according to the menu recommending table.
For example, in the food material recommendation table, the corn is ranked first, the eating priority is F1, the spareribs are ranked third, the eating priority is F3, the green beans are ranked fifth, and the eating priority is F5, so that the priority of the recommended recipe should be better than that of the corn roasted green beans (F1+ F3> F1+ F5).
The various recipes can be sorted in a descending order according to the recommendation scores to form a recipe recommendation table, the more the sorting is, the more the foods are recommended, the higher or the highest scored recipes are determined to serve as the recipes to be recommended according to the food priority of the food materials, the food materials are conveniently collocated by a user, the food value of the food materials is utilized to the maximum, and the use experience of the user is improved.
In the embodiment, the types of different food materials in the refrigerator are obtained through an image recognition technology, and then the eating values of the different food materials are determined according to the market average price information, the freshness information, the historical eating record information of the user, the eating preference score information of the user, the nutritive value information and the like of each food material so as to obtain the eating priority of each food material, the higher the eating priority is, the more the food materials are recommended to be eaten up as soon as possible, so that the food material value maximization is achieved, and the waste of the food materials is effectively reduced; in addition, the menu with higher score is determined according to the eating priority of the food materials so as to recommend the appropriate menu, so that the user can conveniently match the food materials, the eating value of the food materials is utilized to the maximum, and the use experience of the user is improved.
Example 5
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the method for recommending food materials in any one of embodiments 1 or 2 when executing the program. The electronic device 30 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a recommendation method of food materials in any one of embodiments 1 or 2 of the present invention, by running the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also 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 network adapter 36. As shown in FIG. 5, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the steps in the recommendation method for food materials in any one of embodiments 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention can also be implemented in a form of a program product, which includes program codes, and when the program product runs on a terminal device, the program codes are used for causing the terminal device to execute steps in a method for recommending food materials in any one of embodiments 1 or 2.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (16)
1. A method for recommending food materials, the method comprising:
acquiring a target food material image to be processed;
identifying different types of target food materials in the target food material image;
acquiring target characteristic dimension information corresponding to the target food material, and acquiring a first edible value of the target food material according to the target characteristic dimension information;
and obtaining a food material recommendation result according to the first edible value of each target food material and recommending the food material recommendation result.
2. The food material recommendation method of claim 1, wherein the step of obtaining and recommending food material recommendations according to the first food value of each target food material further comprises:
acquiring a first food material corresponding to each menu;
obtaining a second edible value of each said recipe according to said first edible value of said first food material;
and obtaining a menu recommendation result according to the second edible value of each menu and recommending.
3. The food material recommendation method of claim 2, wherein the step of obtaining and recommending food material recommendations according to the first food value of each target food material comprises:
determining the eating priority of each target food material according to the first eating value;
wherein the higher the first consumption value of the target food material, the higher the consumption priority;
and sequentially sorting according to the food priority to generate a food material recommendation table, and recommending food materials according to the food material recommendation table.
4. The food material recommendation method of claim 3, wherein the step of obtaining a second eating value of each said recipe according to said first eating value of said first food material comprises:
acquiring a recommendation score corresponding to each first food material in the menu according to the eating priority of each first food material;
the step of obtaining and recommending the recipe recommendation result according to the second edible value of each recipe comprises the following steps:
and sequentially sorting according to the recommendation scores of each menu to generate a menu recommendation table, and recommending the menu according to the menu recommendation table.
5. The food material recommendation method of claim 1, wherein the step of obtaining the first eating value of the target food material according to the target characteristic dimension information is preceded by the step of:
acquiring historical edible value and historical characteristic dimension information corresponding to each target food material;
taking the historical characteristic dimension information as input and the historical edible value as output, and establishing an edible value determination model of the target food material;
the step of obtaining the first edible value of the target food material according to the target characteristic dimension information comprises the following steps:
inputting the target characteristic dimension information into the edible value determination model to obtain the first edible value of the target food material.
6. The food material recommendation method of claim 5, wherein the step of inputting the target characteristic dimension information into the eating value determination model to obtain the first eating value of the target food material corresponds to a formula:
wherein k represents a k type of target food material, i represents the number of the target characteristic dimension information, and Wi (k)Representing the weight corresponding to the ith target characteristic dimension information,
representing the ith dimension information of the target feature,
a bias parameter F (X) representing the dimension information of the ith target feature(k)) Representing the first food value; wherein i is more than or equal to 2 and is a positive integer.
7. The food material recommendation method of any one of claims 1-6, wherein the target characteristic dimension information comprises at least one of market average price information, freshness degree information, historical eating history information of the user, eating preference score information of the user, and nutritional value information.
8. A recommendation system for food materials, characterized in that the recommendation system comprises:
the image acquisition module is used for acquiring a target food material image to be processed;
the food material identification module is used for identifying different types of target food materials in the target food material image;
the dimension information acquisition module is used for acquiring target characteristic dimension information corresponding to the target food material;
the first edible value acquisition module is used for acquiring a first edible value of the target food material according to the target characteristic dimension information;
and the food material recommending module is used for acquiring and recommending food material recommending results according to the first eating value of each target food material.
9. The food material recommendation system of claim 8, further comprising:
the first food material acquisition module is used for acquiring first food materials corresponding to each menu;
a second edible value obtaining module, configured to obtain a second edible value of each recipe according to the first edible value of the first food material;
and the menu recommendation module is used for obtaining and recommending menu recommendation results according to the second edible value of each menu.
10. The food material recommendation system of claim 9, wherein the food material recommendation module comprises:
the food priority determining unit is used for determining the food priority of each target food material according to the first food value;
wherein the higher the first consumption value of the target food material, the higher the consumption priority;
and the food material recommending unit is used for sequentially sorting according to the food priority to generate a food material recommending table and recommending food materials according to the food material recommending table.
11. The food material recommendation system of claim 10, wherein the second eating value obtaining module is configured to obtain a recommendation score for each of the recipes according to the eating priority of each of the first food materials in the recipe;
the menu recommending module is used for sequentially sequencing according to the recommendation scores of all the menus to generate a menu recommending table and recommending the menus according to the menu recommending table.
12. The food material recommendation system of claim 8, further comprising:
the historical information acquisition module is used for acquiring historical edible value and historical characteristic dimension information corresponding to each target food material;
the model establishing module is used for establishing an edible value determining model of the target food material by taking the historical characteristic dimension information as input and the historical edible value as output;
the first edible value obtaining module is used for inputting the target characteristic dimension information into the edible value determining model to obtain the first edible value of the target food material.
13. The food material recommendation system of claim 12, wherein the first edibility value obtaining module inputs the target characteristic dimension information into the edibility value determination model to obtain a formula corresponding to the first edibility value of the target food material:
wherein k represents a k type of target food material, i represents the number of the target characteristic dimension information, and Wi (k)Representing the weight corresponding to the ith target characteristic dimension information,
representing the ith dimension information of the target feature,
a bias parameter F (X) representing the dimension information of the ith target feature(k)) Representing the first food value; wherein i is more than or equal to 2 and is a positive integer.
14. The food material recommendation system according to any one of claims 8-13, wherein the target characteristic dimension information comprises at least one of market average price information, freshness information, historical consumption record information of the user, consumption preference score information of the user, and nutritional value information.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for recommending food materials according to any of claims 1-7 when executing the computer program.
16. A computer readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the method for recommendation of food material according to any one of claims 1-7.
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| CN112396381A (en) * | 2020-12-01 | 2021-02-23 | 珠海优特智厨科技有限公司 | Ex-warehouse data processing method, storage medium and computer equipment |
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