CN111754118A - Intelligent menu optimization system based on self-adaptive learning - Google Patents
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- 230000003044 adaptive effect Effects 0.000 claims abstract description 14
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- 238000013528 artificial neural network Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
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- 235000019643 salty taste Nutrition 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 235000019654 spicy taste Nutrition 0.000 description 1
- 238000003756 stirring Methods 0.000 description 1
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Abstract
The invention relates to the technical field of intelligent kitchens, in particular to an intelligent menu optimization system based on adaptive learning, which comprises the following components: the dish evaluation system comprises a cooking information acquisition module, a dish evaluation module and a dish management module, wherein the cooking information acquisition module is used for acquiring cooking information of a user in a cooking process; the self-adaptive cooking influence factor analysis module is used for carrying out self-adaptive learning according to the menu, the cooking information and the evaluation information and updating the cooking influence factor model; the negative index extraction module is used for extracting the dish parameters and negative indexes in the evaluation information; and the menu optimization module is used for adjusting the menu according to the negative direction index and the cooking influence factor model. The intelligent menu optimization system based on the self-adaptive learning can adjust the menu of the user based on the self-learning technology to form the personalized menu which accords with the taste of the user.
Description
Technical Field
The invention relates to the technical field of intelligent kitchens, in particular to an intelligent menu optimization system based on self-adaptive learning.
Background
People's consciousness to health improves gradually, and consequently more and more people can want to do the dish in at home, and there is the webpage or the application system that a lot of menus were shared at present, can push the menu to the user, contains eating material list and culinary art operation in the page of menu, and the user can accomplish the culinary art according to the menu.
However, due to insufficient cooking experience, inaccurate fire control and the like, the dishes are easily not cooked or fried to be burnt, and the taste of the dishes is poor. In addition, in the cooking process of the dishes, the taste of the dishes is affected by the duration of heat, the addition of seasonings, the cooking time and the like, and different users have different requirements on the taste and the flavor of the dishes. If dishes are cooked according to a fixed menu, the cooked dishes are difficult to meet the requirements of most people.
Disclosure of Invention
The invention aims to provide an intelligent menu optimization system based on self-adaptive learning, which can adjust a menu of a user based on a self-learning technology to form an individualized menu according with the taste of the user.
The application provides the following technical scheme:
intelligent menu optimizing system based on self-adaptation study includes:
the cooking information acquisition module is used for acquiring cooking information of a user in a cooking process, wherein the cooking information comprises cooking operation, cooking parameters and dish parameters;
the dish evaluation module is used for acquiring evaluation information of a user on dishes;
the self-adaptive cooking influence factor analysis module is used for carrying out self-adaptive learning according to the menu, the cooking information and the evaluation information and updating the cooking influence factor model;
the negative index extraction module is used for extracting the dish parameters and negative indexes in the evaluation information;
and the menu optimization module is used for adjusting the menu according to the negative direction index and the cooking influence factor model.
According to the technical scheme, the cooking information in the cooking process of the user can be collected through the cooking information collection module, such as the data of the mixture ratio of food materials, the cooking duration, the adding time of the food materials, the color and shape changes of dishes in the cooking process and the like, the influence relation of various parameter data on the cooking result in the cooking process is analyzed through the self-adaptive cooking influence factor analysis module in combination with the collected cooking information, the dish recipe and the evaluation information, a cooking influence factor model is formed, and when the user evaluation information is not satisfied with a certain parameter of the dishes, the dish recipe can be adjusted through the cooking influence factor model. The cooking influence factor model can continuously carry out iterative optimization through self-learning along with the accumulation of data of a user, so that the model is more accurate, and the recipe is more accurately adjusted.
The system comprises a recipe generation module, an error analysis module and an advice generation module, wherein the recipe generation module is used for generating a recipe according to the recipe, and the advice generation module is used for matching the cooking operation and the cooking parameter corresponding to the negative direction index according to the negative direction index and the cooking influence factor model when the cooking operation and the cooking parameter have errors; the recipe optimization module is used for adjusting the recipe when the cooking operation and the cooking parameters have no errors.
When the fact that the dishes are not delicious due to the cooking operation of the user and the errors of cooking parameters are detected, the user is helped to improve the cooking operation capacity, when the operation of the user is not problematic, the reason that the personal taste preferences of the user are different is shown, and the taste adjustment is realized by adjusting the menu.
The system further comprises a training guidance module, and the training guidance module is used for matching teaching videos according to the improvement suggestions and pushing the teaching videos to the user terminal.
By pushing the teaching video to the user, the user is helped to quickly improve the cooking operation capability.
The cooking preference portrait module is used for generating the cooking preference portrait according to the menu, the cooking operation and the cooking parameters.
The taste preference portrait and the cooking preference portrait are generated through the taste preference portrait module and the cooking preference portrait module, and the user can be conveniently classified and identified.
Further, the cooking influence factor model includes an influence relation model and an influence coefficient model, the influence relation model includes a plurality of groups of influence relations, the influence coefficient model includes influence coefficients of each group of influence relations, all users share the influence relation model, and each user has a separate influence coefficient model.
The perception sensitivity of each user to different parameter attributes of dishes is different, so that an independent influence coefficient model is set for each user to realize targeted adjustment and meet the requirements of different users.
And the influence coefficient recommending module is used for matching the influence coefficient models of other users according to the user taste preference image and the cooking preference image and pushing the influence coefficient models to the user terminal.
Through the pushing of the influence coefficient model, the influence coefficient model of the user similar to the portrait of the user can be directly used by the user, so that the adjustment accuracy is improved, and the learning iteration duration and times of the influence coefficient model of the user are reduced.
The recipe recommendation module is used for pushing the adjusted recipe to a user terminal of a user with similar taste preference portrait and cooking preference portrait.
And sharing and recommending the menu based on the user portrait so that the user can obtain the menu suitable for the user.
The device further comprises a recording module and a counting module, wherein the recording module is used for recording the cooking operation, the cooking parameters and the error condition of each cooking of the user, and the counting module is used for generating a counting report according to the data recorded by the recording module.
And a statistical report is generated, so that a user can conveniently check the defects in the cooking process, and further, the targeted improvement and adjustment are realized.
The cooking habit improving effect verifying module is used for judging whether the cooking habits of the user are improved or not according to the statistical report, and the menu optimizing module is used for optimizing and adjusting the menu when the cooking habits of the user are not improved.
The cooking habits of the user are difficult to correct, and the user is helped to improve the cooking effect of the dishes by optimizing the menu.
Drawings
FIG. 1 is a flowchart of a first embodiment of an intelligent recipe optimization system based on adaptive learning according to the present application;
fig. 2 is a flowchart of a second embodiment of the intelligent recipe optimization system based on adaptive learning according to the present application.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, the intelligent recipe optimization system based on adaptive learning of this embodiment includes a cooking information collection module, a dish evaluation module, an adaptive cooking influence factor analysis module, a negative direction index extraction module, a recipe optimization module, an error analysis module, and a suggestion generation module.
The cooking information acquisition module is used for acquiring cooking information of a user in a cooking process, wherein the cooking information comprises cooking operation, cooking parameters and dish parameters; the cooking operation refers to actions of a user in a cooking process, such as stirring, water adding, ladle turning and the like, cooking parameters comprise food material proportion, firepower, cooking time and the like, and dish parameters are used for feeding back the state of dishes, such as color, shape, pan pasting and the like. The cooking information acquisition module realizes data acquisition through an intelligent terminal installed in a user home, the intelligent terminal comprises but is not limited to an intelligent stove, a cooking appliance and the like, and related parameters can be acquired through special equipment, such as user action, dish color and shape and the like through a camera.
The dish evaluation module is used for acquiring evaluation information of a user on dishes; in this embodiment, the dish evaluation module obtains evaluation information according to data submitted by the user terminal, where the evaluation information includes evaluations of three aspects of color, smell, and taste, and each aspect specifically includes a plurality of subclasses, such as a degree of salty taste, a degree of spicy taste, a degree of delicious taste, and the like, which are different according to different evaluation indexes of a recipe.
The self-adaptive cooking influence factor analysis module is used for performing self-adaptive learning according to the menu, the cooking information and the evaluation information and updating the cooking influence factor model;
the negative index extraction module is used for extracting the dish parameters and negative indexes in the evaluation information; in this embodiment, the negative direction index extraction module performs matching and extraction of the negative direction index based on the keyword.
And the menu optimization module adjusts the menu according to the negative indexes and the cooking influence factor model. In this embodiment, the cooking influence factor model includes an influence relationship model and an influence coefficient model, the influence relationship model includes a plurality of groups of influence relationships, the influence relationships include occurrence conditions, input quantities, and output quantities, the occurrence conditions refer to conditions in which the input quantities affect the output quantities, such as corresponding recipes and food material combination conditions, and the input quantities and the output quantities correspond to the influence factors and the influenced factors. The influence coefficient model comprises influence coefficients of each group of influence relations, namely the influence proportion of the input quantity to the output quantity.
All users share one influence relation model, and with the self-adaptive learning iteration of the self-adaptive cooking influence factor analysis module, the relations in the influence relation model are more and more accurate. Since each user experiences different influence coefficients of an influence relationship, for example, the amount of salt released can influence the taste blandness, and the influence relationship is a group of influence relationships which are objective and can not be different from person to person, but the specific relation coefficients of the salt amount and the blandness are different from person to person, each user has a separate influence coefficient model, so that a cooking influence factor model adapting to the taste of the user can be formed.
The error analysis module is used for judging whether the cooking operation and the cooking parameters have errors according to the menu. The suggestion generation module is used for matching wrong cooking operation and cooking parameters corresponding to the negative direction index according to the negative direction index and the cooking influence factor model when the cooking operation and the cooking parameters have errors, and forming a targeted improvement suggestion; the recipe optimization module is used for adjusting the recipe when the cooking operation and the cooking parameters have no errors.
In the embodiment, the intelligent terminal comprises a training guidance module, a taste preference portrait module and a cooking preference portrait module, wherein the training guidance module is used for matching teaching videos according to improvement suggestions and pushing the teaching videos to a user terminal. The taste preference portrait module is used for forming a taste preference portrait according to evaluation information of a user on dishes, and the cooking preference portrait module is used for generating a cooking preference portrait according to a menu, cooking operation and cooking parameters.
Example two
As shown in fig. 2, the difference between the present embodiment and the first embodiment is that the present embodiment further includes an influence coefficient recommending module, a recipe sharing module, a recipe recommending module, a recording module, a statistical module, and a cooking habit improvement effect verifying module, where the influence coefficient recommending module is configured to match influence coefficient models of other users according to the user taste preference image and the cooking preference image and push the influence coefficient models to the user terminal. And the menu recommending module is used for pushing the adjusted menu to the user terminals of the users with similar taste preference portraits and cooking preference portraits.
The recording module is used for recording the cooking operation, the cooking parameters and the error condition of each cooking of the user, and the statistical module is used for generating a statistical report according to the data recorded by the recording module. The cooking habit improvement effect verification module is used for judging whether the cooking habits of the user are improved or not according to the statistical report, and the menu optimization module is used for optimizing and adjusting the menu when the cooking habits of the user are not improved.
EXAMPLE III
In this embodiment, the adaptive cooking influence factor analyzing module includes an influence relationship model updating module and an influence coefficient model updating module.
The self-adaptive cooking influence factor analysis module updates the cooking influence factor model in two aspects, namely establishing and updating an influence relation model and mainly adding a new influence relation, and adjusting an influence proportion coefficient according to user operation and evaluation. In this embodiment, the influence relationship model updating module and the influence coefficient model updating module are respectively used for updating the two models.
In this embodiment, the influence relationship model updating module adopts a knowledge graph-based analysis algorithm for analyzing the influence relationship, establishes a node for each factor and result, establishes a connection relationship between nodes according to the acquired data, and simultaneously infers the influence relationship between the nodes which are not connected yet and the magnitude of the influence relationship according to the existing connection relationship, if the node a affects the node B, the node B affects the node C, and the node D affects the node B, it can be inferred that both the node a and the node D affect the node C, and meanwhile, the influence relationship between the node a and the node D is judged, and the inferred relationship is marked as a relationship to be verified; the influence coefficient model updating module adopts an LSTM-based long-short term memory neural network algorithm, data collected by the cooking information collecting module and the dish evaluating module are used as input, influence relations among factor nodes are output, iterative learning operation is carried out, meanwhile, influence coefficients are calculated for the relation to be verified, if the calculated influence coefficients are smaller than a preset value, it is judged that no influence relation exists between the two nodes, and if not, it is judged that the influence relation exists.
The embodiment realizes the learning of the intelligent menu relationship through the influence relationship model updating module and the influence coefficient model updating module, and can provide a high-efficiency and accurate cooking influence factor model.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. Intelligent menu optimization system based on self-adaptation study, its characterized in that: the method comprises the following steps:
the cooking information acquisition module is used for acquiring cooking information of a user in a cooking process, wherein the cooking information comprises cooking operation, cooking parameters and dish parameters;
the dish evaluation module is used for acquiring evaluation information of a user on dishes;
the self-adaptive cooking influence factor analysis module is used for carrying out self-adaptive learning according to the menu, the cooking information and the evaluation information and updating the cooking influence factor model;
the negative index extraction module is used for extracting the dish parameters and negative indexes in the evaluation information;
and the menu optimization module is used for adjusting the menu according to the negative direction index and the cooking influence factor model.
2. The intelligent menu optimization system based on adaptive learning of claim 1, characterized in that: the system comprises a recipe generation module, an error analysis module and a suggestion generation module, wherein the recipe generation module is used for generating a recipe according to the recipe, the recipe generation module is used for generating a recipe according to the cooking operation and cooking parameters, and the suggestion generation module is used for matching wrong cooking operation and cooking parameters corresponding to negative indexes according to the negative indexes and the cooking influence factor models when the cooking operation and cooking parameters are wrong; the recipe optimization module is used for adjusting the recipe when the cooking operation and the cooking parameters have no errors.
3. The intelligent menu optimization system based on adaptive learning of claim 2, characterized in that: the system also comprises a training guidance module, wherein the training guidance module is used for matching the teaching video according to the improvement suggestion and pushing the teaching video to the user terminal.
4. The intelligent menu optimization system based on adaptive learning of claim 3, wherein: the intelligent cooking system is characterized by further comprising a taste preference portrait module and a cooking preference portrait module, wherein the taste preference portrait module is used for forming a taste preference portrait according to evaluation information of a user on dishes, and the cooking preference portrait module is used for generating a cooking preference portrait according to a menu, cooking operation and cooking parameters.
5. The intelligent menu optimization system based on adaptive learning of claim 4, wherein: the cooking influence factor model comprises an influence relation model and an influence coefficient model, the influence relation model comprises a plurality of groups of influence relations, the influence coefficient model comprises influence coefficients of each group of influence relations, all users share the influence relation model, and each user has an independent influence coefficient model.
6. The intelligent menu optimization system based on adaptive learning of claim 5, wherein: the influence coefficient recommending module is used for matching the influence coefficient models of other users according to the user taste preference image and the cooking preference image and pushing the influence coefficient models to the user terminal.
7. The intelligent menu optimization system based on adaptive learning of claim 6, wherein: the recipe recommendation module is used for pushing the adjusted recipe to a user terminal of a user with similar taste preference portrait and cooking preference portrait.
8. The intelligent menu optimization system based on adaptive learning of claim 7, wherein: the intelligent cooking system further comprises a recording module and a counting module, wherein the recording module is used for recording the cooking operation, the cooking parameters and the error condition of each cooking of the user, and the counting module is used for generating a counting report according to the data recorded by the recording module.
9. The intelligent menu optimization system based on adaptive learning of claim 8, wherein: the cooking habit improving effect verifying module is used for judging whether the cooking habits of the user are improved or not according to the statistical report, and the menu optimizing module is used for optimizing and adjusting the menu when the cooking habits of the user are not improved.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117319745A (en) * | 2023-09-28 | 2023-12-29 | 火星人厨具股份有限公司 | Interaction method, device, equipment and storage medium based on menu |
| CN117541359A (en) * | 2024-01-04 | 2024-02-09 | 江西工业贸易职业技术学院(江西省粮食干部学校、江西省粮食职工中等专业学校) | Dining recommendation method and system based on preference analysis |
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