Cooking menu recommendation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a cooking menu recommendation method and device, equipment and a storage medium, and the cooking menu recommendation method comprises the steps: obtaining the storage information of candidate food materials and a preset cooking menu, and determining the incidence relation between the cooking menu and the candidate food materials; receiving the reported identity information of the dining users, and determining the number of the current dining users based on the identity information; constructing candidate menus under multiple dimensions according to the identity information, the association relationship and the storage information; and determining a target menu based on the number of the dining users and the candidate menu, and recommending the target menu. According to the scheme, a menu with a higher matching degree can be provided for the user, and diversified choices are provided for the user.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a cooking menu recommendation method, device, equipment and storage medium. Background Art

[0002] With the accelerated pace of modern life, automatic cooking machines have emerged and gradually entered people's lives. Automatic cooking machines can save cooking time and provide convenience for people.

[0003] However, current automatic cooking machines often recommend menus by listing a large number of menus to users. On the one hand, the listed menus may not match the existing food reserves, causing users to choose menus that cannot be cooked. On the other hand, recommendations are not made in combination with the user's situation, and the recommended menus are not highly matched. Therefore, the current cooking menu recommendation solution cannot meet the needs of users. Summary of the invention

[0004] The present application provides a menu recommendation method, device, equipment and storage medium, which can provide users with menus with higher matching degrees and provide users with diversified choices.

[0005] In a first aspect, the present application provides a cooking menu recommendation method, comprising:

[0006] Obtain storage information of candidate ingredients and preset cooking menus;

[0007] Determine the association between cooking menus and candidate ingredients;

[0008] Receive the identity information of the dining users reported, and determine the current number of dining users based on the identity information;

[0009] Construct candidate menus in multiple dimensions based on identity information, association relationships, and storage information;

[0010] The target menu is determined based on the number of dining users and candidate menus, and the target menu is recommended.

[0011] In a second aspect, the present application provides a cooking menu recommendation device, comprising:

[0012] Acquisition module: used to obtain storage information of candidate ingredients and preset cooking menus;

[0013] Determination module: used to determine the association between the cooking menu and the candidate ingredients;

[0014] Receiving module: used to receive the identity information of the dining users reported, and determine the current number of dining users based on the identity information;

[0015] Construction module: used to construct candidate menus in multiple dimensions based on identity information, association relationships and storage information;

[0016] Recommendation module: used to determine the target menu based on the number of dining users and candidate menus, and recommend the target menu.

[0017] Optionally, in some embodiments of the present application, the building blocks include:

[0018] A first acquisition unit, used to acquire health detection data and taste preference data according to identity information;

[0019] A second acquisition unit, used to acquire the ingredient weight of the candidate ingredient according to the storage information;

[0020] The construction unit is used to construct a candidate menu in the health dimension, taste dimension and ingredient validity period dimension according to the health detection data, taste preference data, association relationship and ingredient weight.

[0021] Optionally, in some embodiments of the present application, the first acquisition unit includes:

[0022] A first extraction subunit is used to extract voiceprint data from identity information to obtain voiceprint data;

[0023] The recognition subunit is used to recognize the voiceprint data and obtain the corresponding health detection data according to the recognition result.

[0024] Optionally, in some embodiments of the present application, the first acquisition unit includes:

[0025] A first acquisition subunit, used for acquiring corresponding historical menu data according to the identity information;

[0026] The second extraction subunit is used to extract taste preference from the historical menu data to obtain taste preference data.

[0027] Optionally, in some embodiments of the present application, the second acquisition unit includes:

[0028] A second acquisition subunit acquires the storage date and shelf life of the candidate food according to the storage information;

[0029] The subunits are determined, and the ingredient weights of the candidate ingredients are determined according to the storage date and shelf life of the ingredients.

[0030] Optionally, in some embodiments of the present application, an update module is further included, and the update module is specifically used to:

[0031] A multi-dimensional selection page showing a candidate menu;

[0032] In response to a user's selection operation on any one or more dimensions in the multi-dimensional selection page, obtaining the user's selection information;

[0033] The candidate menu is updated according to the selection information.

[0034] Optionally, in some embodiments of the present application, the recommendation module includes:

[0035] A display unit for displaying a menu of dish proportion selections;

[0036] A dish ratio acquisition unit, for acquiring corresponding dish ratio data in response to a user's selection operation on a dish ratio selection menu;

[0037] The target menu determination unit is used to determine the target menu based on the dish ratio data, the number of dining users and the candidate menus.

[0038] In a third aspect, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0039] In a fourth aspect, the present application also provides a storage medium on which a computer program is stored, wherein the computer program implements the steps of the above method when executed by a processor.

[0040] As described above, the present application provides a cooking menu recommendation method, device, electronic device and storage medium, which obtain storage information of candidate ingredients and a preset cooking menu, determine the association relationship between the cooking menu and the candidate ingredients, receive the identity information of the reported dining users, and determine the current number of dining users based on the identity information. Then, candidate menus in multiple dimensions are constructed according to the identity information, the association relationship and the storage information. Finally, a target menu is determined based on the number of dining users and the candidate menus, and the target menu is recommended. This scheme obtains the storage information of the candidate ingredients and the association relationship between the cooking menu and the candidate ingredients, and combines the reserve situation of the ingredients to make menu recommendations to the dining users, thereby avoiding recommending to the customers a cooking menu that does not match the reserve of ingredients. By receiving the identity information of the dining users, a cooking menu with a higher degree of match can be recommended to the dining users. By providing the dining users with cooking menus in multiple dimensions and determining and recommending the target menu according to the number of dining users, a variety of choices are provided to the dining users. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] in:

[0043] Figure 1 is a schematic diagram of a scenario of a cooking menu recommendation method provided in an embodiment of the present application;

[0044] Figure 2 is a flowchart of a cooking menu recommendation method provided in an embodiment of the present application;

[0045] Figure 3 is a schematic diagram of a candidate dimension list selection interface in a cooking menu recommendation method provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of a dish ratio list selection interface in a cooking menu recommendation method provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of the structure of a cooking menu recommendation device provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of another cooking menu recommendation device provided in an embodiment of the present application;

[0049] Figure 7 It is a schematic diagram of the structure of a cooking machine provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0053] The present application provides a cooking menu recommendation method, device, equipment and storage medium.

[0054] The cooking menu recommendation device can be integrated into an electronic device, which can be a smart phone, a tablet computer, a laptop computer, a desktop computer or an intelligent cooking machine, but is not limited thereto. The electronic device can be directly or indirectly connected to the server through wired or wireless communication. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. This application does not limit this.

[0055] For example, see Figure 1 The cooking menu recommendation device is integrated in the intelligent cooking machine 10, and the intelligent cooking machine may include an interactive display screen, through which the user can interact with the intelligent cooking machine 10. The intelligent cooking machine 10 makes cooking menu recommendations in response to the cooking menu recommendation instruction, wherein the cooking menu recommendation instruction may be issued by the user, or may be automatically triggered according to a preset condition, such as automatically triggered when a predetermined time is reached. The cooking menu recommendation step includes obtaining storage information of candidate ingredients and a preset cooking menu, determining the association relationship between the cooking menu and the candidate ingredients, then receiving the identity information of the reported dining user, and determining the current number of dining users based on the identity information, then constructing candidate menus under multiple dimensions based on the identity information, the association relationship, and the storage information, and finally determining the target menu based on the number of dining users and the candidate menus, and recommending the target menu.

[0056] The cooking menu recommendation method provided by the present application obtains storage information of candidate ingredients and a preset cooking menu, determines the association relationship between the cooking menu and the candidate ingredients, receives the identity information of the reported dining users, and determines the current number of dining users based on the identity information. Next, candidate menus in multiple dimensions are constructed based on the identity information, the association relationship and the storage information. Finally, a target menu is determined based on the number of dining users and the candidate menus, and the target menu is recommended. This scheme obtains the storage information of the candidate ingredients and the association relationship between the cooking menu and the candidate ingredients, that is, combines the reserve situation of the ingredients to make menu recommendations to the dining users, thereby avoiding recommending to the customers a cooking menu that does not match the reserve of ingredients. By receiving the identity information of the dining users, a cooking menu with a higher degree of match can be recommended to the dining users. By providing the dining users with cooking menus in multiple dimensions and determining and recommending the target menu based on the number of dining users, a variety of choices are provided to the dining users.

[0057] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0058] A cooking menu recommendation method comprises: obtaining storage information of candidate ingredients and a preset cooking menu; determining the association relationship between the cooking menu and the candidate ingredients; receiving identity information of reported dining users, and determining the current number of dining users based on the identity information; constructing candidate menus in multiple dimensions according to the identity information, the association relationship and the storage information; determining a target menu based on the number of dining users and the candidate menus, and recommending the target menu.

[0059] See also Figure 2 , Figure 2 This is a flow chart of the cooking menu recommendation method provided by this application. The specific process of the cooking menu recommendation method can be as follows:

[0060] 101. Obtain storage information of candidate ingredients and preset cooking menus.

[0061] The candidate ingredients represent the stored ingredients, and the storage information is used to represent the storage information of the ingredients, such as the storage quantity, storage date, shelf life of the ingredients, etc. The preset cooking menu may be pre-stored in the smart cooking machine, or downloaded through the network after receiving the menu recommendation instruction. The storage information may be obtained by the smart cooking machine through connection with the smart home, such as establishing a wireless connection with the smart refrigerator to obtain the storage information of the candidate ingredients stored in the smart refrigerator.

[0062] 102. Determine the association between the cooking menu and the candidate ingredients.

[0063] The association relationship is used to indicate the relationship between the ingredients required to complete the cooking menu and the candidate ingredients. As an example, the ingredients required to complete the dishes in the cooking menu and the candidate ingredients can be compared to determine the association relationship between the cooking menu and the candidate ingredients. For example, a cooking menu includes two dishes, garlic fried cucumber and blanched prawns. The ingredients required to complete the garlic fried cucumber include cucumber, garlic and ginger, and the ingredients required to complete the blanched prawns include prawns, green onions and ginger. The above required ingredients are compared with the candidate ingredients. If the candidate ingredients include the above required ingredients, the association relationship is determined to be an association.

[0064] In another example, the weights of different ingredients in the cooking menu included in the candidate ingredients can also be used to determine different associations between the cooking menu and the candidate ingredients. For example, the cooking menu includes blanched prawns, and the weights of prawns, green onions and ginger required for this dish are 500g, 5g and 5g respectively. If the weights of prawns, green onions and ginger in the candidate ingredients are less than 300g, 3g and 3g respectively, then the association between the cooking menu and the candidate ingredients is determined to be unassociated; if the weights of prawns, green onions and ginger in the candidate ingredients are greater than or equal to 300g, 3g and 3g respectively, but less than 500g, 5g and 5g respectively, then the association is determined to be weakly associated; if the weights of prawns, green onions and ginger in the candidate ingredients are greater than or equal to 500g, 5g and 5g respectively, then the association is determined to be strongly associated. In the menu recommendation process, cooking menus with strongly associated relationships can be given priority. After all strongly associated cooking menus have been recommended, weakly associated cooking menus are selected for recommendation, and unassociated cooking menus are not recommended.

[0065] 103. Receive the reported identity information of the user, and determine the current number of dining users based on the identity information.

[0066] The identity information is used to characterize the identity of the user. The identity information can be obtained based on user input or automatically acquired by the smart cooking machine, and the number of dining users is determined based on the identity information. In one example, the smart cooking machine obtains audio data within a preset time period, such as audio data within 20 minutes before the current time, wherein the current time is the execution time of step 101, and the audio data can be collected, saved, and sent to the smart cooking machine by a smart home, such as a smart refrigerator, a smart TV, and a smart table lamp. The audio data includes the audio of the conversation of the dining users. When making a cooking menu recommendation, the audio data is obtained, and voiceprint information is extracted from the audio data to obtain voiceprint information, wherein the voiceprint information includes voiceprint data of one or more persons, and the number of dining users can be determined based on the voiceprint data included in the voiceprint information. For each person's voiceprint data, the identity attributes of each dining user can be obtained based on a preset identity attribute mapping table.

[0067] In one example, some keywords in the audio data may be identified to obtain more accurate identity information. For example, if the audio data is identified to include keywords such as "eat", "eat here" and "eat a lot", the voiceprint corresponding to the keyword is determined to be the voiceprint of the dining user. If the audio data is identified to include keywords such as "don't eat here", "have eaten" and "not eating tonight", the voiceprint corresponding to the keyword is determined to be the voiceprint of the non-dining user. For the non-dining user, no corresponding cooking menu recommendation is made.

[0068] In one example, identity information is obtained through a smart home, such as an image taken by a smart camera. Specifically, the smart camera takes a picture of the user and uploads the captured image to a cloud server. The cloud server identifies the user in the image based on face recognition technology to obtain identity information, and the smart cooking machine obtains the identity information through the cloud server.

[0069] 104. Construct a candidate menu in multiple dimensions based on identity information, association relationship and storage information.

[0070] The candidate menu is a menu under one or more dimensions obtained according to the identity information, the association relationship and the storage information. The multiple dimensions include age dimension, food storage dimension and cooking scene dimension.

[0071] For example, a candidate menu under the age dimension can be constructed based on identity information and association relationships, a candidate menu under the food reserve dimension can be constructed based on storage information and association relationships, and a candidate menu under the cooking scene dimension can be constructed based on identity information, association relationships, and storage information. Among them, the candidate menu under the age dimension is a menu suitable for the age distribution of dining users. For example, if the dining users include infants, the candidate menu may include liquid food suitable for infants; the candidate menu under the food reserve dimension is a menu suitable for food consumption. For example, for food with high food reserve, the candidate menu will include dishes using the food, avoiding excessive stockpiling of a certain food; the candidate menu under the cooking scene dimension is a menu suitable for the current cooking scene. For example, in the scene of meeting guests, the candidate menu includes dishes suitable for meeting guests.

[0072] In some embodiments, the step of “building a candidate menu in multiple dimensions according to identity information, association relationships, and storage information” may specifically include:

[0073] (11) Obtain health test data and taste preference data based on identity information;

[0074] (12) obtaining the ingredient weight of the candidate ingredient according to the storage information;

[0075] (13) Construct candidate menus in the dimensions of health, taste, and ingredient expiration date based on health test data, taste preference data, association relationships, and ingredient weights.

[0076] Among them, the health detection data is used to characterize the health status of the dining user, and the taste preference data is used to indicate the taste preference of the dining user. Storage information is used to indicate the storage information of the ingredients. The ingredient weight is used to indicate the consumption priority of the candidate ingredients. For example, for the candidate ingredients with large storage volume, short shelf life and long storage time, in order to avoid excessive storage and inability to consume in a short time, which leads to food deterioration, the corresponding ingredient weight can be increased for such ingredients, thereby reducing the storage volume. The candidate menu of the taste dimension is a candidate menu constructed for the taste preferences of the dining user, the candidate menu of the ingredient validity period dimension is a candidate menu constructed according to the validity period of the ingredient, and the candidate menu of the health dimension is a candidate menu constructed for the health detection data of the dining user. As an example, the health detection data includes one or more health indicators, for example, including blood pressure indicators, blood lipid indicators and blood sugar indicators. If any one or more indicators are not within the corresponding indicator range, the candidate menu of the health dimension will give priority to the dishes corresponding to the indicators. For example, eggplant is suitable for people with high blood pressure, and the blood pressure indicator in the health detection data exceeds the corresponding indicator range, then the candidate menu of the health dimension will give priority to dishes made of eggplant.

[0077] Furthermore, if the diners include p1, p2 and p3, where the health test data of p1 includes high blood pressure, the health test data of p2 includes high blood pressure and high blood lipids, and the health test data of p3 includes high blood lipids, high blood pressure and high blood sugar, and the ingredients corresponding to high blood pressure, high blood lipids and high blood sugar are eggplant, carrot and cucumber respectively, then for the health test data of p1, p2 and p3, the priority of the above ingredients is increased respectively. As an example, the initial priority of an ingredient is 0. For each indicator corresponding to an ingredient in the health detection data, the priority of the ingredient is increased by 1. The higher the priority of an ingredient, the more preferred the dishes made from the ingredient are when constructing a candidate menu. According to the health detection data of p1, p2 and p3 above, it can be obtained that the priorities of eggplant, carrot and cucumber are 3, 2 and 1 respectively. When constructing a candidate menu under the health dimension, it can be constructed according to the priorities of the above three ingredients, that is, in constructing a candidate menu, first select dishes made with eggplant. After the dishes made with eggplant included in the menu meet the preset conditions, for example, the number of dishes made with eggplant reaches the preset number of dishes, or the weight of the eggplant included in the dish meets the preset weight; then select dishes made with carrots; and finally select dishes made with cucumbers.

[0078] In one example, the voiceprint data of each dining user can be obtained, and the health detection data can be obtained based on the preset voiceprint data and health detection data mapping table; the user ID of the corresponding dining user can be obtained based on the voiceprint data, and the historical menu data corresponding to the user ID can be obtained, and the taste preference data of the dining user can be obtained through the historical menu data corresponding to the user ID; based on the storage information of the ingredients, the ingredient weight of the candidate ingredients can be obtained. Finally, the candidate menus of health dimension, taste dimension and ingredient validity period dimension are constructed based on the health detection data, taste preference data, association relationship and ingredient weight.

[0079] In some embodiments, the step of “obtaining health detection data and taste preference data according to identity information” may specifically include:

[0080] (21) extracting voiceprint data from the identity information to obtain voiceprint data;

[0081] (22) Identify the voiceprint data and obtain the corresponding health test data based on the identification results.

[0082] In one example, by acquiring audio data of a user's conversation and performing voiceprint extraction on the audio data, the voiceprint of each extracted user is identified. If the voiceprint is identified as a resident's voiceprint, that is, the voiceprint is a pre-recorded voiceprint, the corresponding health detection data can be searched according to a preset mapping table of voiceprints and health detection data. If the voiceprint is not identified, that is, the voiceprint is not the resident's voiceprint, the preset health detection data can be used, or the user's input can be received. The preset health detection data can be normal health detection data, that is, health detection data without health problems.

[0083] For example, the extracted voiceprint data includes voiceprint a, voiceprint b, voiceprint c and voiceprint d. The above voiceprint data are identified, and it is found that voiceprint a corresponds to householder 1, voiceprint b corresponds to householder 2, and voiceprint c corresponds to householder 3. Voiceprint d is an unidentified voiceprint. Based on the preset mapping table of voiceprints and health test data, the health test data of householder 1, householder 2 and householder 3 can be obtained through voiceprint a, voiceprint b and voiceprint c. For voiceprint d, the preset health test data is used as the health test data corresponding to voiceprint d.

[0084] In some embodiments, the step of “obtaining taste preference data according to identity information” may specifically include:

[0085] (31) Obtaining corresponding historical menu data based on identity information;

[0086] (32) Extract taste preference from historical menu data to obtain taste preference data.

[0087] Among them, the identity information can be identified to obtain the user identification, the historical menu data includes the cooking menus selected once or multiple times by the dining user in the historical dining, and the taste preference data represents the taste preference characteristics in the historical menu data. In one example, the user's voiceprint data can be obtained and the voiceprint data can be identified to obtain the user identification. Based on the user identification, the cooking menus selected more than or equal to 3 times in the dining user's historical dining are searched, and the cooking menus selected less than 3 times are not processed. After the cooking menu is found, the taste preference is extracted from the cooking menu, including the flavor type, cuisine and ingredients in the cooking menu.

[0088] For example, by extracting taste preferences from a cooking menu, it is found that the flavors that appear more than a preset number of times in the cooking menu are spicy, hot and sour, and salty and sweet; the cuisines that appear more than a preset number of times are Sichuan cuisine, Hunan cuisine, and Shandong cuisine; and the ingredients that appear more than a preset number of times are chicken, beef, and mutton, so the corresponding taste preference data can be obtained, wherein the preset number of times can be set according to actual conditions.

[0089] In some embodiments, the step of “obtaining the ingredient weight of the candidate ingredient according to the storage information” may specifically include:

[0090] (41) obtaining the storage date and shelf life of the candidate food according to the storage information;

[0091] (42) Determine the ingredient weight of the candidate ingredient based on the storage date and shelf life of the ingredient.

[0092] Specifically, the validity period of the candidate ingredient can be obtained through the storage date and shelf life of the candidate ingredient. The validity period indicates the time until the ingredient expires, and the ingredient weight of the candidate ingredient is determined based on the validity period. To prevent the ingredient from spoiling and becoming inedible due to being stored for too long, the shorter the validity period of the candidate ingredient, the greater the ingredient weight of the candidate ingredient. For candidate ingredients with larger weights, the higher the recommendation priority.

[0093] In one example, the candidate ingredients include eggplant, carrot and broccoli, and the corresponding storage dates and shelf lives are October 3 and 7 days, October 4 and 10 days, and October 3 and 14 days, respectively. According to the storage date and shelf life, it can be obtained that the eggplant, carrot and broccoli will expire on October 10, October 14 and October 17, respectively. The distance from the current time to the expiration time is calculated to obtain the effective time. For example, if the current time is October 1, the effective periods of the eggplant, carrot and broccoli are 2, 13 and 16 days, respectively. Finally, the ingredient weight is obtained according to the effective period of the ingredient. Specifically, the ingredient weight can be obtained by subtracting the effective period from the weight base. For example, if the weight base is 100, the ingredient weights of eggplant, carrot and broccoli are 98, 87 and 84, respectively. When making menu recommendations, ingredients with higher weight values ​​are given priority.

[0094] In some embodiments, after the step of "building a candidate menu in multiple dimensions according to the identity information, the association relationship and the storage information", the following steps are further included:

[0095] (51) Displaying a multi-dimensional selection page for a candidate menu;

[0096] (52) in response to a user's selection operation on any one or more dimensions in the multi-dimensional selection page, obtaining user selection information;

[0097] (54) Update the candidate menu according to the selection information.

[0098] For example, see Figure 3 The present application provides a multi-dimensional selection page for a candidate menu, which displays a candidate dimension list, including a "health dimension" option corresponding to the health dimension, a "taste dimension" option corresponding to the taste dimension, and an "ingredient expiration date dimension" option corresponding to the ingredient expiration date dimension. In response to a user's selection operation on the candidate dimension list, the menu of the health dimension corresponding to the selected "health dimension" option is updated to the candidate menu.

[0099] 105. Determine a target menu based on the number of dining users and candidate menus, and recommend the target menu.

[0100] According to the number of dining users, a suitable menu can be selected from the candidate menus as the target menu, and the target menu can be recommended. Specifically, assuming that the number of dining users is n, a menu with n+1 or n+2 dishes can be selected as the target menu. For example, taking the number of dining users as 6, the candidate menu M includes menu m1, menu m2 and menu m3, where menu m1 has 5 dishes, menu m2 has 10 dishes, and menu m3 has 7 dishes. According to the number of dining users, a menu with 7 or 8 dishes is selected as the target menu, so menu m3 is determined as the target menu.

[0101] In some embodiments, the step of “determining a target menu based on the number of dining users and candidate menus, and recommending the target menu” includes:

[0102] (61) Displaying a menu with a selection of dishes in proportion;

[0103] (62) in response to a user's selection operation on a dish ratio selection menu, obtaining corresponding dish ratio data;

[0104] (63) Determine a target menu based on the number of dining users and candidate menus, and recommend the target menu.

[0105] Among them, the dish ratio data is the proportion of each type of dish in the menu. The type of dish can be meat or vegetable dishes, or the dish can be soup, stir-fry or steamed, etc. The number of dishes is determined according to the number of diners, and the number of dishes of each type is obtained according to the number of dishes combined with the dish ratio data. Taking the number of diners as 6 as an example, first, the number of dishes can be determined based on the number of diners. For example, the number of dishes is determined to be 8. If the dish ratio data is a ratio of meat to vegetarian dishes of 3:1, then the number of meat dishes included in the menu is 6, and the number of vegetarian dishes included is 2. If the dish ratio data is a ratio of soup, stir-fry and steamed, it is 2:2:4, then the number of soup dishes included in the menu is 2, the number of stir-fry dishes is 2, and the number of steamed dishes is 4.

[0106] Specifically, see Figure 4 The present application provides a dish ratio selection interface, which includes a dish ratio selection list. The dish ratio selection list includes three dish ratios, wherein the dish ratio is the ratio of meat dishes to vegetarian dishes, which are 1:1, 3:1 and 7:1 respectively. In response to the user's selection operation, the dish ratio of meat dishes to vegetarian dishes is 1:1 as the dish ratio data.

[0107] The above is the recommended cooking menu process for this application.

[0108] As described above, the present application provides a cooking menu recommendation method, which obtains storage information of candidate ingredients and a preset cooking menu, determines the association relationship between the cooking menu and the candidate ingredients, receives the identity information of the reported dining users, and determines the current number of dining users based on the identity information. Next, candidate menus in multiple dimensions are constructed based on the identity information, the association relationship, and the storage information. Finally, a target menu is determined based on the number of dining users and the candidate menus, and the target menu is recommended. This scheme obtains the storage information of the candidate ingredients and the association relationship between the cooking menu and the candidate ingredients, and combines the reserve situation of the ingredients to make menu recommendations to the dining users, thereby avoiding recommending cooking menus that do not match the reserve of ingredients to customers. By receiving the identity information of the dining users, cooking menus with a higher degree of match can be recommended to the dining users. Finally, cooking menus in multiple dimensions are provided to the dining users, providing the dining users with a variety of choices.

[0109] In order to better implement the cooking menu recommendation method of the present application embodiment, the present invention embodiment also provides a cooking menu recommendation device based on the above. The meanings of the terms are the same as those in the above cooking menu recommendation method, and the specific implementation details can refer to the description in the method embodiment.

[0110] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of a cooking menu recommendation device provided in an embodiment of the present application, wherein the cooking menu recommendation device may include an acquisition module 201, a determination module 202, a receiving module 203, a construction module 204, and a recommendation module 205, which may be specifically as follows:

[0111] Acquisition module 201: used to acquire storage information of candidate ingredients and preset cooking menus;

[0112] The preset cooking menu may be pre-stored in the smart cooking machine, or may be downloaded through the network after receiving the menu recommendation instruction. The storage information may be obtained by the smart cooking machine through connection with the smart home, for example, establishing a wireless connection with the smart refrigerator to obtain the storage information of the candidate ingredients stored in the smart refrigerator.

[0113] Determining module 202: used to determine the association relationship between the cooking menu and the candidate ingredients;

[0114] The association relationship is used to represent the relationship between the ingredients required to complete the cooking menu and the candidate ingredients. As an example, the ingredients required to complete the dishes in the cooking menu and the candidate ingredients can be compared to determine the association relationship between the cooking menu and the candidate ingredients.

[0115] Receiving module 203: used to receive the identity information of the dining users reported, and determine the current number of dining users based on the identity information;

[0116] The identity information may be input by the user or automatically acquired by the intelligent cooking machine, and the number of dining users may be determined based on the identity information.

[0117] Construction module 204: used to construct candidate menus in multiple dimensions according to identity information, association relationships and storage information;

[0118] The candidate menus are one or more menus obtained according to the identity information, the association relationship and the storage information.

[0119] Optionally, in some embodiments, the construction module 204 may specifically include:

[0120] A first acquisition unit, used to acquire health detection data and taste preference data according to identity information;

[0121] A second acquisition unit, used to acquire the ingredient weight of the candidate ingredient according to the storage information;

[0122] The construction unit is used to construct a candidate menu in the health dimension, taste dimension and ingredient validity period dimension according to the health detection data, taste preference data, association relationship and ingredient weight.

[0123] Optionally, in some embodiments, the first acquiring unit may specifically include:

[0124] A first extraction subunit is used to extract voiceprint data from identity information to obtain voiceprint data;

[0125] The recognition subunit is used to recognize the voiceprint data and obtain the corresponding health detection data according to the recognition result.

[0126] Optionally, in some embodiments, the first acquiring unit may specifically include:

[0127] A first acquisition subunit, used for acquiring corresponding historical menu data according to the identity information;

[0128] The second extraction subunit is used to extract taste preference from the historical menu data to obtain taste preference data.

[0129] Optionally, in some embodiments, the second acquiring unit may specifically include:

[0130] A second acquisition subunit acquires the storage date and shelf life of the candidate food according to the storage information;

[0131] The subunits are determined, and the ingredient weights of the candidate ingredients are determined according to the storage date and shelf life of the ingredients.

[0132] Recommendation module 205: used to determine a target menu based on the number of dining users and candidate menus, and recommend the target menu.

[0133] Among them, according to the number of dining users, a suitable menu can be selected from the candidate menus as the target menu, and the target menu can be recommended

[0134] Optionally, in some embodiments, the recommendation module 205 may specifically include:

[0135] A display unit for displaying a menu of dish proportion selections;

[0136] A dish ratio acquisition unit, for acquiring corresponding dish ratio data in response to a user's selection operation on a dish ratio selection menu;

[0137] The target menu determination unit is used to determine the target menu based on the dish ratio data, the number of dining users and the candidate menus.

[0138] Optionally, in some embodiments, see Figure 6 The cooking menu recommendation device of the present application may also include an update module 206, which is specifically used to: display a multi-dimensional selection page of a candidate menu; obtain the user's selection information in response to the user's selection operation on any one or more dimensions in the multi-dimensional selection page; and update the candidate menu according to the selection information.

[0139] It can be seen that the embodiment of the present application provides a cooking menu recommendation device, wherein the acquisition module 20 acquires the storage information of the candidate ingredients and the preset cooking menu. After acquiring the storage information of the candidate ingredients and the preset cooking menu, the determination module 202 determines the association relationship between the cooking menu and the candidate ingredients. Then, the receiving module 203 receives the identity information of the reported dining user, and determines the current number of dining users based on the identity information. The construction module 204 constructs the candidate menus under multiple dimensions according to the identity information, the association relationship and the storage information. Finally, the recommendation module 205 determines the target menu based on the number of dining users and the candidate menu, and recommends the target menu. The cooking menu recommendation device provided by the present application obtains the storage information of the candidate ingredients and the association relationship between the cooking menu and the candidate ingredients, that is, combines the reserve situation of the ingredients to recommend menus to dining users, thereby avoiding recommending to customers cooking menus that do not match the reserve of ingredients. By receiving the identity information of the dining user, a cooking menu with a higher degree of matching can be recommended to the dining user. Finally, cooking menus of multiple dimensions are provided to the dining user, providing the dining user with a variety of choices.

[0140] It should be noted that in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0141] Correspondingly, an embodiment of the present application also discloses a storage medium storing a computer program that can be loaded by a processor and execute the above method.

[0142] The present application also discloses a smart cooking device, such as Figure 7 As shown, it includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400 and a memory 500. The processor 100 is used to provide computing and control capabilities, the communication bus 200 is configured to achieve connection and communication between these components, the user interface 300 may include a display screen, the external communication interface 400 may include a standard wired interface and a wireless interface, and the memory 500 stores the XX method. The processor 100 is also used to adopt the above method when executing the cooking menu recommendation method stored in the memory 500.

[0143] Optionally, in some embodiments, the processor implements the following steps when executing the computer program:

[0144] Obtain storage information of candidate ingredients and preset cooking menus; determine the association between the cooking menu and the candidate ingredients; receive identity information of reported dining users, and determine the current number of dining users based on the identity information; construct candidate menus in multiple dimensions based on the identity information, association, and storage information; determine a target menu based on the number of dining users and the candidate menus, and recommend the target menu.

[0145] In an embodiment of the present application, by acquiring the storage information of candidate ingredients and the association between the cooking menu and the candidate ingredients, menu recommendations are made to diners based on the reserve situation of the ingredients, thereby avoiding recommending to customers a cooking menu that does not match the ingredient reserves. By receiving the identity information of the diners, a cooking menu with a higher degree of match can be recommended to the diners. Finally, a cooking menu with multiple dimensions is provided to the diners, providing them with a variety of choices.

[0146] It should be understood that "one embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0147] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0148] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0149] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0151] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0152] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device to execute all or part of the methods of each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0153] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A cooking menu recommendation method, characterized in that: include: Obtain storage information of candidate ingredients and preset cooking menus; Determining an association relationship between the cooking menu and the candidate ingredients; receiving identity information of dining users reported, and determining the current number of dining users based on the identity information; Constructing a candidate menu in multiple dimensions according to the identity information, association relationship and storage information; A target menu is determined based on the number of dining users and the candidate menus, and the target menu is recommended.

2. The method according to claim 1, characterized in that Constructing a candidate menu in multiple dimensions according to the identity information, association relationship and storage information, including: Obtain health test data and taste preference data based on the identity information; According to the storage information, obtaining the food weight of the candidate food; The candidate menu of health dimension, taste dimension and ingredient validity period dimension is constructed according to the health detection data, taste preference data, association relationship and ingredient weight.

3. The method according to claim 2, characterized in that The obtaining of health detection data according to the identity information includes: Extracting voiceprint data from the identity information to obtain voiceprint data; The voiceprint data is identified, and corresponding health detection data is obtained according to the identification result.

4. The method according to claim 2, wherein obtaining taste preference data according to the identity information comprises: Acquire corresponding historical menu data according to the identity information; The taste preference data is extracted from the historical menu data to obtain the taste preference data.

5. The method according to claim 2, characterized in that: Acquiring the ingredient weight of the candidate ingredient according to the storage information includes: Acquire the storage date and shelf life of the candidate food according to the storage information; The ingredient weight of the candidate ingredient is determined according to the storage date and shelf life of the ingredient.

6. The method according to any one of claims 1 to 5, characterized in that: After constructing the candidate menus in multiple dimensions according to the identity information, the association relationship and the storage information, the method further includes: Displaying a multi-dimensional selection page of the candidate menu; In response to a user's selection operation on any one or more dimensions in the multi-dimensional selection page, obtaining user selection information; The candidate menu is updated according to the selection information.

7. The method according to any one of claims 1 to 5, wherein determining a target menu based on the number of dining users and the candidate menus, and recommending the target menu comprises: Display the menu with the proportion of dishes; In response to a user's selection operation on the dish ratio selection menu, obtaining corresponding dish ratio data; A target menu is determined based on the dish ratio data, the number of dining users, and the candidate menus.

8. A cooking menu recommendation device, characterized in that: include: Acquisition module: used to obtain storage information of candidate ingredients and preset cooking menus; Determining module: used to determine the association relationship between the cooking menu and the candidate ingredients; Receiving module: used to receive the identity information of the dining users reported, and determine the current number of dining users based on the identity information; Construction module: used to construct candidate menus in multiple dimensions according to the identity information, association relationship and storage information; Recommendation module: used to determine a target menu based on the number of dining users and the candidate menus, and recommend the target menu.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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