Menu recommendation method, device and equipment
By obtaining food storage and dietary preference information, generating ingredient combinations and recommending recipes, the problem that smart home appliances cannot effectively associate ingredients is solved, and a smarter and more adaptable recipe recommendation is achieved.
Patent Information
- Application Number
- CN202510194108.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-07-11
AI Technical Summary
Existing smart home appliances cannot effectively associate the ingredients in the user's refrigerator, and cannot recommend recipes suitable for users, resulting in insufficient intelligence in the recipe recommendations.
By obtaining the current food storage information and the dietary preference information of the target object, the food analysis and processing are carried out, the ingredients are generated, and the recipe recommendation is generated based on this information.
It realizes smarter, diverse and highly adaptable recipe recommendations, improving user satisfaction and quality of life.
Smart Images
Figure CN120296240A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and particularly to a recipe recommendation method, apparatus, and device. Background Art
[0002] In recent years, with the rise of the Internet of Things concept, products in the home appliance industry have accelerated their intelligence. Almost all products in the home appliance industry have launched intelligent series products, and most of the current intelligent home appliances still simply achieve remote control or only implement specific functions for a single category. This obviously fails to achieve true intelligence. For example, household appliances and kitchen appliances that are closely related to people's lives cannot be associated with the existing ingredients in the user's refrigerator, and cannot recommend recipes that can be immediately processed and are suitable for the user. Summary of the Invention
[0003] To improve the intelligence of recipe recommendation, this application provides a recipe recommendation method, apparatus, and device. The technical solutions are as follows:
[0004] In a first aspect, this application provides a recipe recommendation method, which includes:
[0005] Obtain the current ingredient reserve information and obtain the dietary preference information of the target object; the current ingredient reserve information includes the reserve information of at least one ingredient;
[0006] According to the reserve information of the at least one ingredient, perform ingredient analysis processing to obtain the current ingredient analysis result, where the current ingredient analysis result represents the independent recommendation degree of each ingredient in the at least one ingredient and the combined recommendation degree between every two of the ingredients;
[0007] According to the current ingredient analysis result, perform ingredient combination processing to obtain at least one ingredient combination, and each ingredient combination includes at least any one of the at least one ingredient;
[0008] According to the at least one ingredient combination and the dietary preference information, perform recipe generation processing to obtain at least one target recipe;
[0009] Display the at least one target recipe to recommend recipes to the target object.
[0010] Optionally, the current ingredient analysis result includes a first analysis result and a second analysis result. The step of performing ingredient analysis processing according to the reserve information of the at least one ingredient to obtain the current ingredient analysis result includes:
[0011] Determine the nutritional attribute information, growth attribute information, and cultural attribute information of each of the at least one ingredient based on the reserve information of the at least one ingredient;
[0012] Determine at least one independent recommendation index for each of the ingredients and at least one combined recommendation index between each pair of the ingredients based on the nutritional attribute information, growth attribute information, and cultural attribute information of each of the ingredients;
[0013] Determine the first analysis result based on at least one independent recommendation index for each of the ingredients, where the first analysis result represents the independent recommendation degree of each of the ingredients;
[0014] Determine the second analysis result based on at least one combined recommendation index between each pair of the ingredients, where the second analysis result represents the combined recommendation degree between each pair of the ingredients.
[0015] Optionally, the at least one independent recommendation index includes a first independent recommendation index, the at least one combined recommendation index includes a first combined recommendation index, and the first independent recommendation index and the first combined recommendation index are determined in the following manner:
[0016] Perform component analysis based on the nutritional attribute information of each of the ingredients to obtain the first independent recommendation index for each of the ingredients and the first combined recommendation index between each pair of the ingredients.
[0017] Optionally, the at least one independent recommendation index includes a second independent recommendation index, the at least one combined recommendation index includes a second combined recommendation index, and the second independent recommendation index and the second combined recommendation index are determined in the following manner:
[0018] Perform spatio-temporal analysis based on the growth attribute information of each of the ingredients to obtain the second independent recommendation index for each of the ingredients and the second combined recommendation index between each pair of the ingredients.
[0019] Optionally, the at least one independent recommendation index includes a third independent recommendation index, the at least one combined recommendation index includes a third combined recommendation index, and the third independent recommendation index and the third combined recommendation index are determined in the following manner:
[0020] Perform sensitivity analysis based on the cultural attribute information of each of the ingredients to obtain the third independent recommendation index for each of the ingredients and the third combined recommendation index between each pair of the ingredients.
[0021] Optionally, performing ingredient combination processing based on the current ingredient analysis result to obtain at least one ingredient combination, including:
[0022] Combining the at least one ingredient to obtain at least one candidate ingredient combination;
[0023] Determining the combined recommendation degree of each candidate ingredient combination in the at least one candidate ingredient combination according to the current ingredient analysis result; each candidate ingredient combination includes at least one ingredient;
[0024] Screening the at least one candidate ingredient combination according to the combined recommendation degree of each candidate ingredient combination to obtain the at least one ingredient combination.
[0025] Optionally, performing recipe generation processing based on the at least one ingredient combination and the dietary preference information to obtain at least one target recipe, including:
[0026] Performing recipe generation processing according to the at least one ingredient combination to obtain a candidate recipe set, the candidate recipe set includes at least one candidate recipe;
[0027] Determining the predicted recommendation index data of each candidate recipe in the at least one candidate recipe;
[0028] Screening at least one candidate recipe in the candidate recipe set according to the predicted recommendation index data of each candidate recipe and the dietary preference information to determine at least one target recipe.
[0029] Optionally, determining the predicted recommendation index data of each candidate recipe in the at least one candidate recipe includes:
[0030] Determining the first predicted index data of each candidate recipe, the first predicted index data characterizing the nutritional matching degree of the corresponding candidate recipe;
[0031] Determining the second predicted index data of each candidate recipe, the second predicted index data characterizing the taste matching degree of the corresponding candidate recipe;
[0032] Determining the third predicted index data of each candidate recipe, the third predicted index data characterizing the production matching degree of the corresponding candidate recipe;
[0033] Determining the predicted recommendation index data of each candidate recipe according to the first predicted index data of each candidate recipe, the second predicted index data of each candidate recipe and the third predicted index data of each candidate recipe.
[0034] In a second aspect, the present application provides a recipe recommendation device, the device comprising:
[0035] An acquisition module, configured to acquire current food ingredient reserve information and acquire the dietary preference information of a target object; the current food ingredient reserve information includes the reserve information of at least one food ingredient;
[0036] A food ingredient matching and analysis module, configured to perform food ingredient analysis processing according to the reserve information of the at least one food ingredient to obtain a current food ingredient analysis result, the current food ingredient analysis result representing the independent recommendation degree of each food ingredient in the at least one food ingredient and the combined recommendation degree between every two of the food ingredients;
[0037] A food ingredient combination module, configured to perform food ingredient combination processing according to the current food ingredient analysis result to obtain at least one food ingredient combination, and each food ingredient combination includes at least any one of the at least one food ingredient;
[0038] A recipe generation module, configured to perform recipe generation processing according to the at least one food ingredient combination and the dietary preference information to obtain at least one target recipe;
[0039] A display module, configured to display the at least one target recipe to recommend recipes to the target object.
[0040] In a third aspect, the present application provides a computer-readable storage medium, in which at least one instruction or at least one segment of program is stored, and the at least one instruction or at least one segment of program is loaded and executed by a processor to implement a recipe recommendation method as described in the first aspect.
[0041] In a fourth aspect, the present application provides a computer device, the computer device comprising a processor and a memory, and at least one instruction or at least one segment of program is stored in the memory, and the at least one instruction or at least one segment of program is loaded and executed by the processor to implement a recipe recommendation method as described in the first aspect.
[0042] In a fifth aspect, the present application provides a computer program product, the computer program product comprising computer instructions, and when the computer instructions are executed by a processor, a recipe recommendation method as described in the first aspect is implemented.
[0043] The technical solution provided by the present application has the following technical effects:
[0044] The technical solution provided by this application performs ingredient analysis and processing based on the inventory information of at least one ingredient included in the current ingredient inventory information, and obtains the current ingredient analysis result. The current ingredient analysis result represents the independent recommendation degree of each ingredient in at least one ingredient and the combined recommendation degree between every two ingredients; combining the current ingredient analysis result for ingredient combination processing can obtain at least one ingredient combination, and each ingredient combination includes at least any one of at least one ingredient; combining at least one ingredient combination and the dietary preference information of the target object for recipe generation processing can obtain at least one target recipe, and recommend the recipe to the target object by displaying at least one target recipe on the electrical device. The technical solution provided by this application is based on the current ingredient inventory information and the dietary preference information of the target object, and can more intelligently recommend a rich variety of and more adaptable recipes to the target object, improving user satisfaction.
[0045] Additional aspects and advantages of this application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0047] Figure 1 is a schematic flowchart of a recipe recommendation method provided by an embodiment of this application;
[0048] Figure 2 is a schematic flowchart of a method for obtaining current ingredient inventory information and dietary preference information provided by an embodiment of this application;
[0049] Figure 3 is another schematic flowchart of a method for obtaining current ingredient inventory information and dietary preference information provided by an embodiment of this application;
[0050] Figure 4 is a schematic diagram of a display interface provided by an embodiment of this application;
[0051] Figure 5 is a schematic diagram of a recipe recommendation device provided by an embodiment of this application;
[0052] Figure 6 The hardware structure schematic diagram of the device for implementing a recipe recommendation method provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.
[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.
[0055] The following will detail various exemplary embodiments, features, and aspects of the present application with reference to the drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.
[0056] The special term "exemplary" herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" here does not have to be construed as superior to or better than other embodiments.
[0057] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.
[0058] In addition, in order to better illustrate the present application, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present application can be implemented without some specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail in order to highlight the gist of the present application.
[0059] Please refer to Figure 1, Figure 1 is a flowchart of a recipe recommendation method provided by an embodiment of the present application. The present application provides the method operation steps as described in the embodiment or flowchart, but based on routine or non-creative labor, it may include more or fewer operation steps. The step order listed in the embodiment is only one way among the execution orders of numerous steps and does not represent the only execution order. When the actual system or server product executes, it can be executed in the order of the method shown in the embodiment or the accompanying drawings or executed in parallel (for example, in an environment of parallel processors or multi-threaded processing). As Figure 1 shown, a recipe recommendation method provided by an embodiment of the present application may specifically include the following steps:
[0060] S110: Obtain the current ingredient reserve information and obtain the dietary preference information of the target object; the current ingredient reserve information includes the reserve information of at least one ingredient.
[0061] In an embodiment of the present application, the current ingredient reserve information indicates the reserve information of at least one ingredient stored in the electrical appliance device. The reserve information of each ingredient may include, but is not limited to: ingredient name, ingredient quantity, ingredient freshness, purchase time, shelf life, etc.
[0062] In an embodiment of the present application, the target object is the user who currently requests a recipe recommendation or the previous user of the electrical appliance device. The dietary preference information may indicate the preferences of the target object in terms of dietary taste, health status, nutritional needs, etc.
[0063] In an embodiment of the present application, as Figure 2 shown, by means of the target object inputting preference data or restrictive conditions and ingredient data, the current ingredient reserve information and dietary preference information are generated and stored in the ingredient library.
[0064] In an embodiment of the present application, the current ingredient reserve information of the stored ingredients can also be determined by means of the sensing device of the electrical appliance device. As Figure 3 shown, the perceived current ingredient reserve information is entered into the ingredient library as offline data, and the attribute information of various ingredients in the ingredient library is updated in real time through networked data. In addition, the target object can input physical sign data, dietary data, etc. in personal information to track and analyze the personal health status of the target object in combination with the daily recipe.
[0065] In an embodiment of the present application, the dietary preference information of the target object can also be analyzed and determined by synchronizing the personal information of the target object in the cloud.
[0066] Exemplarily, an embodiment of the present application also provides a program definition for ingredients and ingredient libraries, including:
[0067] # Define a food item class
[0068] class FoodItem:
[0069] def __init__(self, name, calories, protein, fat):
[0070] self.name = name
[0071] self.calories = calories
[0072] self.protein = protein
[0073] self.fat = fat
[0074] # Create a food database
[0075] food_database = []
[0076] # Add food items to the food database
[0077] food_database.append(FoodItem("Apple", 52, 0.3, 0.2))
[0078] food_database.append(FoodItem("Chicken breast", 165, 31, 3.6))
[0079] food_database.append(FoodItem("Egg", 68, 6, 5))
[0080] # Print the nutritional information of food items in the food database
[0081] for food in food_database:
[0082] print(f"Food item: {food.name}, Calories: {food.calories}, Protein: {food.protein}, Fat: {food.fat}")
[0083] Among them, the FoodItem class is used to represent food items, including the name, calories, protein, and fat content of the food items. Then, a food_database list is created to store the nutritional composition data of the food items, and several exemplary food items are added to it. Finally, the nutritional attribute information of each food item in the food database is printed through a loop.
[0084] It should be noted that there are some coding errors in the original text, such as incorrect indentation and capitalization in variable names. The translation has been made based on the corrected code logic.Exemplarily, the acquisition of the current ingredient reserve information and the dietary preference information of the target object can be achieved through a data collection module. The program content of the data collection module is as follows:
[0085] def collect_data():
[0086] ingredients_data = {} # Store the nutritional component data of ingredients
[0087] user_info = {} # Store the personal information and preferences of the user
[0088] # Collect ingredient data
[0089] ingredients_data['apple'] = {'calories': 52, 'protein': 0.3, 'fat': 0.2}
[0090] ingredients_data['banana'] = {'calories': 89, 'protein': 1.1, 'fat': 0.3}
[0091] # Collect user information
[0092] user_info['name'] = input("Please enter your name:")
[0093] user_info['age'] = int(input("Please enter your age:"))
[0094] user_info['goal'] = input("Please enter your goal (weight loss / muscle gain):")
[0095] Return ingredients_data, user_info
[0096] S120: According to the reserve information of at least one ingredient, perform ingredient analysis and processing to obtain the current ingredient analysis result, where the current ingredient analysis result represents the independent recommendation degree of each ingredient in at least one ingredient and the combined recommendation degree between every two ingredients.
[0097] In the embodiments of the present application, ingredient analysis is performed based on the reserve information of each ingredient to obtain the current ingredient analysis result that can represent the independent recommendation degree of each ingredient and the combined recommendation degree between every two ingredients. Specifically, the ingredient analysis process may include, but is not limited to, analyzing indicators such as the nutritional components, freshness, seasonality, regionality, and matching degree with local eating tastes of each ingredient, analyzing the interactions between every two ingredients, the coincidence degree of cooking methods, and the matching degree of cooking duration. Based on the above analysis indicators and the importance degrees corresponding to each analysis indicator, the independent recommendation degree of each ingredient and the combined recommendation degree between every two ingredients can be determined.
[0098] Among them, in the case of only one ingredient, the combined recommendation degree between every two ingredients is equivalent to the independent recommendation degree of the single ingredient.
[0099] In the embodiments of the present application, the current ingredient analysis result includes a first analysis result and a second analysis result. The first analysis result represents the independent recommendation degree of each ingredient, and the second analysis result represents the combined recommendation degree between every two ingredients. Step S120 can be specifically implemented as follows:
[0100] S121: According to the reserve information of at least one ingredient, determine the nutritional attribute information, growth attribute information, and cultural attribute information of each ingredient in the at least one ingredient.
[0101] Among them, the nutritional attribute information can indicate the nutritional component data of the corresponding ingredient, such as protein, fat, carbohydrates, sodium content, vitamins, minerals, etc. The growth attribute information can indicate the growth place, regional type, etc. of the corresponding ingredient, and the cultural attribute information can indicate whether the culture involved in the corresponding ingredient is sensitive.
[0102] Figure 3 A method for real-time updating various attribute information of ingredients in the ingredient library through network data is shown.
[0103] S122: According to the nutritional attribute information, growth attribute information, and cultural attribute information of each ingredient, determine at least one independent recommendation indicator for each ingredient and at least one combined recommendation indicator between every two ingredients.
[0104] In a specific embodiment, at least one independent recommendation indicator includes a first independent recommendation indicator, and at least one combined recommendation indicator includes a first combined recommendation indicator. The first independent recommendation indicator and the first combined recommendation indicator are determined in the following manner:
[0105] According to the nutritional attribute information of each ingredient, ingredient analysis is performed to obtain the first independent recommendation index for each ingredient and the first combined recommendation index between every two ingredients.
[0106] In a specific embodiment, at least one independent recommendation index includes a second independent recommendation index, and at least one combined recommendation index includes a second combined recommendation index. The second independent recommendation index and the second combined recommendation index are determined in the following manner:
[0107] According to the growth attribute information of each ingredient, spatio-temporal analysis is performed to obtain the second independent recommendation index for each ingredient and the second combined recommendation index between every two ingredients.
[0108] In a specific embodiment, at least one independent recommendation index includes a third independent recommendation index, and at least one combined recommendation index includes a third combined recommendation index. The third independent recommendation index and the third combined recommendation index are determined in the following manner:
[0109] According to the cultural attribute information of each ingredient, sensitivity analysis is performed to obtain the third independent recommendation index for each ingredient and the third combined recommendation index between every two ingredients.
[0110] Specifically, according to whether the nutritional attribute information of each ingredient is similar or complementary, whether the growth attribute information of each ingredient is similar or opposite, and whether the cultural attribute information of each ingredient conflicts, etc., at least one independent recommendation index for each ingredient and at least one combined recommendation index between every two ingredients can be determined. The independent recommendation index indicates the recommendation degree of the corresponding ingredient, and the combined recommendation index indicates the recommendation degree of the corresponding pair of ingredients.
[0111] Exemplarily, a Food Interaction class is defined to represent the interaction class between every two ingredients to determine the combined recommendation degree between every two ingredients, including the names of two ingredients and the interaction type. Then, a monitoring_database list is created to store the ingredient interaction data, and several groups of ingredient interactions are added to it. Finally, the information of each group of ingredient interactions in the monitoring library is printed through a loop. The program content is as follows:
[0112] # Define an ingredient interaction class
[0113] Class Food Interaction:
[0114] def__init__(self,food1,food2,interaction_type):
[0115] self.food1 = food1
[0116] self.food2 = food2
[0117] self.interaction_type = interaction_type
[0118] # Create a monitoring database
[0119] monitoring_database = []
[0120] # Add food interactions to the monitoring database
[0121] monitoring_database.append(Food Interaction("Apple", "Egg", "Suitable combination"))
[0122] monitoring_database.append(Food Interaction("Chicken breast", "Egg", "Excluded combination"))
[0123] # Print the food interaction information in the monitoring database
[0124] For interaction in monitoring_database:
[0125] print(f"Food 1: {interaction.food1}, Food 2: {interaction.food2}, Interaction type: {interaction.interaction_type}")
[0126] Exemplarily, a simple GUI application can also be created using the Tkinter library to display the information of the food database and the monitoring database, and it can run offline. The program is as follows:
[0127] import Tkinter as tk
[0128] class Food DatabaseApp:
[0129] def __init__(self, root):
[0130] self.root = root
[0131] self.root.title("Food database and monitoring database")
[0132] # Create a display area for the food database
[0133] self.food_database_label = tk.Label(root, text="Food Database")
[0134] self.food_database_label.pack()
[0135] self.food_database_listbox = tk.Listbox(root)
[0136] for food in food_database:
[0137] self.food_database_listbox.insert(tk.END, f"{food.name}-Calories: {food.calories}")
[0138] self.food_database_listbox.pack()
[0139] # Create a display area for the monitoring database
[0140] self.monitoring_database_label = tk.Label(root, text="Monitoring Database")
[0141] self.monitoring_database_label.pack()
[0142] self.monitoring_database_listbox = tk.Listbox(root)
[0143] For interaction in monitoring_database:
[0144] self.monitoring_database_listbox.insert(tk.END, f"{interaction.food1} and {interaction.food2}-{interaction.interaction_type}")
[0145] self.monitoring_database_listbox.pack()
[0146] # Create the root window
[0147] root = tk.Tk()
[0148] app = Food DatabaseApp(root)
[0149] root.mainloop()
[0150] Among them, this code can also be saved as an independent.py file and then run offline to see a GUI interface containing information on the ingredient library and monitoring library.
[0151] In the above embodiment, considering the recommendation degree of ingredients from the dimensions of nutritional attribute information, growth attribute information, and cultural attribute information of ingredients, the attribute information of ingredients is more comprehensively investigated, so as to improve the adaptability of the ingredients used in the generated recipes. Considering the recommendation degree of a single ingredient and the recommendation degree of a pair of ingredients at the same time can enrich the diversity of ingredient use and combination, and then improve the diversity of the generated recipes.
[0152] S123: Determine the first analysis result according to at least one independent recommendation index of each ingredient.
[0153] In a feasible implementation manner, each independent recommendation index in the at least one independent recommendation index corresponds to a first index recommendation weight, and the first index recommendation weight indicates the importance of this independent recommendation index. By performing a weighted sum processing on each independent recommendation index and the corresponding first index recommendation weight, the first analysis result can be obtained.
[0154] In a feasible implementation manner, at least one independent recommendation index of each ingredient is directly used as the first analysis sub-result corresponding to each ingredient in the first analysis result.
[0155] The first analysis result can indicate whether to recommend each ingredient to the target object.
[0156] S124: Determine the second analysis result according to at least one combined recommendation index between every two ingredients.
[0157] In a feasible implementation manner, each combined recommendation index in the at least one combined recommendation index corresponds to a second index recommendation weight, and the second index recommendation weight indicates the importance of this combined recommendation index. By performing a weighted sum processing on each combined recommendation index and the corresponding second index recommendation weight, the second analysis result can be obtained.
[0158] In a feasible implementation manner, at least one combination recommendation index of each pair of food ingredients is directly used as the second analysis sub-result corresponding to each pair of food ingredients in the second analysis result.
[0159] The second analysis result can indicate whether to recommend each pair of food ingredients to the target object.
[0160] In the above embodiment, considering the recommendation degree of a single food ingredient and the recommendation degree of a pair of food ingredients can enrich the diversity of the use and combination of food ingredients, and thus improve the diversity of the generated recipes.
[0161] S130: According to the current food ingredient analysis result, perform food ingredient combination processing to obtain at least one food ingredient combination, and each food ingredient combination includes at least any one of the at least one food ingredient.
[0162] In the embodiment of the present application, when combining food ingredients, the obtained food ingredient combination can include one or more food ingredients. Combining the independent recommendation degree of each food ingredient indicated by the current food ingredient analysis result and the combination recommendation degree between every two food ingredients can reduce the number of food ingredient combinations and improve the quality of the obtained food ingredient combinations.
[0163] In an embodiment of the present application, step S130 may include:
[0164] S131: Combine at least one food ingredient to obtain at least one candidate food ingredient combination.
[0165] Exemplarily, in the case of N food ingredients, the result of direct combination is 2 N -1 kinds. Each candidate food ingredient combination may include i food ingredients, where i ≤ N.
[0166] S132: According to the current food ingredient analysis result, determine the combined recommendation degree of each candidate food ingredient combination in at least one candidate food ingredient combination; each candidate food ingredient combination includes at least one food ingredient.
[0167] In a feasible implementation manner, according to the independent recommendation degree of each food ingredient indicated by the current food ingredient analysis result and the combination recommendation degree between every two food ingredients, where the independent recommendation degree and the combination recommendation degree are floating-point data, sum the independent recommendation degrees of the food ingredients included in each candidate food ingredient combination, and sum the combination recommendation degrees directly between the food ingredients two by two to obtain the combined recommendation degree of each candidate food ingredient combination, and the combined recommendation degree is floating-point data.
[0168] In a feasible implementation manner, according to the independent recommendation degrees of each ingredient indicated by the current ingredient analysis result and the combined recommendation degrees between every two ingredients, where the independent recommendation degrees and the combined recommendation degrees are Boolean data, 1 indicates recommendable, and 0 indicates not recommendable, perform a Boolean operation on the independent recommendation degrees of the ingredients included in each candidate ingredient combination and the combined recommendation degrees between every two ingredients directly, to obtain the combined recommendation degree of each candidate ingredient combination. A combined recommendation degree of 1 indicates recommendable, and a combined recommendation degree of 0 indicates not recommendable.
[0169] S133: Screen at least one candidate ingredient combination according to the combined recommendation degree of each candidate ingredient combination, to obtain at least one ingredient combination.
[0170] In a feasible implementation manner, in the case where the combined recommendation degree is floating-point data, use the candidate ingredient combinations with a combined recommendation degree not lower than a preset threshold as at least one ingredient combination for the recipe to be generated.
[0171] In a feasible implementation manner, in the case where the combined recommendation degree is Boolean data, use the candidate ingredient combinations with a combined recommendation degree of 1 as at least one ingredient combination.
[0172] In the above embodiments, determine the combined recommendation degree of each candidate ingredient combination based on the current ingredient analysis result, and then perform combination screening in combination with the combined recommendation degree, to improve the effectiveness and adaptability of at least one ingredient combination of the recipe to be generated, thereby further improving the rationality and usability of the generated recipe.
[0173] S140: Perform recipe generation processing according to at least one ingredient combination and diet preference information, to obtain at least one target recipe.
[0174] In the embodiments of the present application, considering the personal preferences of the target object, such as nutritional requirements, taste preferences, allergens, production methods, etc., selectively perform recipe generation processing to improve the matching degree of at least one target recipe with the target object.
[0175] In an embodiment of the present application, step S140 may include:
[0176] S141: Perform recipe generation processing according to at least one ingredient combination, to obtain a candidate recipe set, and the candidate recipe set includes at least one candidate recipe.
[0177] In a feasible implementation manner, a recipe library may be pre-constructed, and the recipe library is indexed according to the ingredients in at least one ingredient combination, to obtain a candidate recipe set.
[0178] In a feasible implementation manner, for food combinations with a small number of food categories, common cooking methods such as steaming, boiling, and stir-frying can be directly applied to obtain corresponding candidate recipes.
[0179] S142: Determine the predicted recommendation index data for each candidate recipe in at least one candidate recipe.
[0180] In a specific embodiment, step S142 may include:
[0181] S1421: Determine the first predicted index data for each candidate recipe, where the first predicted index data represents the nutritional matching degree of the corresponding candidate recipe.
[0182] S1422: Determine the second predicted index data for each candidate recipe, where the second predicted index data represents the taste matching degree of the corresponding candidate recipe.
[0183] S1423: Determine the third predicted index data for each candidate recipe, where the third predicted index data represents the production matching degree of the corresponding candidate recipe.
[0184] S1424: Determine the predicted recommendation index data for each candidate recipe according to the first predicted index data of each candidate recipe, the second predicted index data of each candidate recipe, and the third predicted index data of each candidate recipe.
[0185] In the above embodiment, from the dimensions of nutritional matching, taste matching, and production matching, the predicted recommendation index data of at least one candidate recipe generated is predicted to obtain the first predicted index data, the second predicted index data, and the third predicted index data. The three can be weighted and summed to obtain the predicted recommendation index data of each candidate recipe finally, which is more comprehensive and overall considers the user's needs.
[0186] In a specific implementation manner, the recipe information of each candidate recipe and the dietary preference information can be input into the trained recipe recommendation prediction model to obtain the predicted recommendation index data of each candidate recipe.
[0187] S143: Screen at least one candidate recipe in the candidate recipe set according to the predicted recommendation index data of each candidate recipe and the dietary preference information to determine at least one target recipe.
[0188] In the above embodiment, according to at least one food combination, recipe generation processing is performed to obtain at least one candidate recipe, and the dietary preference information of the target object is used to screen at least one candidate recipe, so that the adaptability of at least one obtained target recipe to the target object can be improved, the satisfaction of the target object can be improved, and the intelligence of recipe recommendation can be improved.
[0189] S150: Display at least one target recipe to recommend recipes to the target object.
[0190] In the embodiments of the present application, at least one target recipe is displayed on the visual screen of the electrical appliance device or at least one target recipe is displayed in the terminal application of the target object to recommend recipes to the target object. Figure 4 It is a schematic diagram of a display interface provided by the embodiments of the present application. As Figure 4 shown, the generated target recipe and the nutritional indicators corresponding to the target recipe can be displayed in the terminal application, as well as personal information and an analysis report on personal health status. In addition, user evaluation feedback can be received to optimize the subsequent recipe recommendation results. This is only an example here.
[0191] Exemplarily, the display of the target recipe can be implemented through the user interaction module. The program content of the user interaction module is as follows:
[0192] def user_interaction():
[0193] ingredients_data, user_info = collect_data()
[0194] print("Welcome, %s!" % user_info['name'])
[0195] print("Your age is: %d" % user_info['age'])
[0196] print("Your goal is: %s" % user_info['goal'])
[0197] print("\nRecommended ingredient combination plan:")
[0198] for ingredient, data in ingredients_data.items():
[0199] print("- %s: Calories %d, Protein %.1f g, Fat %.1f g" % (ingredient, data['calories'], data['protein'], data['fat']))
[0200] # Main program
[0201] if __name__ == "__main__":
[0202] user_interaction()
[0203] As can be seen from the above embodiments, the technical solution provided by the present application performs ingredient analysis processing based on the inventory information of at least one ingredient included in the current ingredient inventory information to obtain the current ingredient analysis result, and the current ingredient analysis result represents the independent recommendation degree of each ingredient in the at least one ingredient and the combined recommendation degree between every two ingredients; combining the current ingredient analysis result for ingredient combination processing can obtain at least one ingredient combination, and each ingredient combination includes at least any one of the at least one ingredient; combining at least one ingredient combination and the dietary preference information of the target object for recipe generation processing can obtain at least one target recipe, and by displaying at least one target recipe on the electrical appliance device to recommend recipes to the target object. The technical solution provided by the present application is based on the current ingredient inventory information and the dietary preference information of the target object, and can more intelligently recommend rich, diverse, highly adaptable and immediately executable recipes to the target object, helping users better manage their diet, improve the quality of life and health level, and enhance user satisfaction.
[0204] An embodiment of the present application further provides a recipe recommendation device 500, as Figure 5 shown, the device 200 may include:
[0205] An acquisition module 510, configured to acquire the current ingredient inventory information and acquire the dietary preference information of the target object; the current ingredient inventory information includes the inventory information of at least one ingredient;
[0206] An ingredient matching and analysis module 520, configured to perform ingredient analysis processing according to the inventory information of the at least one ingredient to obtain the current ingredient analysis result, and the current ingredient analysis result represents the independent recommendation degree of each ingredient in the at least one ingredient and the combined recommendation degree between every two ingredients;
[0207] An ingredient combination module 530, configured to perform ingredient combination processing according to the current ingredient analysis result to obtain at least one ingredient combination, and each ingredient combination includes at least any one of the at least one ingredient;
[0208] A recipe generation module 540, configured to perform recipe generation processing according to the at least one ingredient combination and the dietary preference information to obtain at least one target recipe;
[0209] A display module 550, configured to display the at least one target recipe to recommend recipes to the target object.
[0210] In an embodiment of the present application, the current ingredient analysis result includes a first analysis result and a second analysis result, and the ingredient matching and analysis module 520 includes:
[0211] An attribute information determination unit, configured to determine the nutritional attribute information, the growth attribute information, and the cultural attribute information of each of the at least one ingredient according to the reserve information of the at least one ingredient;
[0212] A recommended index determination unit, configured to determine at least one independent recommended index of each of the ingredients and at least one combined recommended index between any two of the ingredients according to the nutritional attribute information, the growth attribute information, and the cultural attribute information of each of the ingredients;
[0213] A first analysis unit, configured to determine the first analysis result according to at least one independent recommended index of each of the ingredients, where the first analysis result represents the independent recommendation degree of each of the ingredients;
[0214] A second analysis unit, configured to determine the second analysis result according to at least one combined recommended index between any two of the ingredients, where the second analysis result represents the combined recommendation degree between any two of the ingredients.
[0215] In an embodiment of the present application, the at least one independent recommended index includes a first independent recommended index, the at least one combined recommended index includes a first combined recommended index, and the recommended index determination unit includes:
[0216] A first recommended index determination subunit, configured to perform component analysis according to the nutritional attribute information of each of the ingredients to obtain the first independent recommended index of each of the ingredients and the first combined recommended index between any two of the ingredients.
[0217] In an embodiment of the present application, the at least one independent recommended index includes a second independent recommended index, the at least one combined recommended index includes a second combined recommended index, and the recommended index determination unit includes:
[0218] A second recommended index determination subunit, configured to perform spatio-temporal analysis according to the growth attribute information of each of the ingredients to obtain the second independent recommended index of each of the ingredients and the second combined recommended index between any two of the ingredients.
[0219] In an embodiment of the present application, the at least one independent recommended index includes a third independent recommended index, the at least one combined recommended index includes a third combined recommended index, and the recommended index determination unit includes:
[0220] A third recommended index determination subunit, configured to perform sensitivity analysis based on the cultural attribute information of each ingredient, to obtain a third independent recommended index for each ingredient and a third combined recommended index between every two of the ingredients.
[0221] In an embodiment of the present application, the ingredient combination module 530 includes:
[0222] A combination unit, configured to combine the at least one ingredient to obtain at least one candidate ingredient combination;
[0223] A joint recommendation degree determination unit, configured to determine the joint recommendation degree of each candidate ingredient combination in the at least one candidate ingredient combination according to the current ingredient analysis result; each candidate ingredient combination includes at least one ingredient;
[0224] A combination screening unit, configured to screen the at least one candidate ingredient combination according to the joint recommendation degree of each candidate ingredient combination, to obtain the at least one ingredient combination.
[0225] In an embodiment of the present application, the recipe generation module 540 includes:
[0226] A recipe generation unit, configured to perform recipe generation processing according to the at least one ingredient combination, to obtain a candidate recipe set, the candidate recipe set includes at least one candidate recipe;
[0227] A predicted recommended index data determination unit, configured to determine the predicted recommended index data of each candidate recipe in the at least one candidate recipe;
[0228] A recipe screening unit, configured to screen at least one candidate recipe in the candidate recipe set according to the predicted recommended index data of each candidate recipe and the diet preference information, to determine at least one target recipe.
[0229] In an embodiment of the present application, the predicted recommended index data determination unit includes:
[0230] A first prediction subunit, configured to determine the first prediction index data of each candidate recipe, the first prediction index data characterizing the nutritional matching degree of the corresponding candidate recipe;
[0231] A second prediction subunit, configured to determine the second prediction index data of each candidate recipe, the second prediction index data characterizing the taste matching degree of the corresponding candidate recipe;
[0232] A third prediction subunit, configured to determine the third prediction index data of each candidate recipe, the third prediction index data characterizing the production matching degree of the corresponding candidate recipe;
[0233] A fusion subunit, configured to determine the predicted recommendation index data of each candidate recipe according to the first predicted index data of each candidate recipe, the second predicted index data of each candidate recipe, and the third predicted index data of each candidate recipe.
[0234] It should be noted that, when the device provided in the above embodiment realizes its functions, only the division of the above function modules is used for illustration. In actual applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the method embodiment belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be elaborated here.
[0235] An embodiment of the present application provides a computer device, which includes a processor and a memory. At least one instruction or at least one segment of program is stored in the memory, and the at least one instruction or the at least one segment of program is loaded and executed by the processor to implement a recipe recommendation method as provided in the above method embodiment.
[0236] Figure 6 The hardware structure diagram of a device for implementing a recipe recommendation method provided in an embodiment of the present application is shown. The device may participate in forming or include the device or system provided in the embodiment of the present application. As Figure 6 shown, the device 10 may include one or more (shown as 1002a, 1002b,..., 1002n in the figure) processors 1002 (the processor 1002 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 6 the structure shown is only schematic and does not limit the structure of the above electronic device. For example, the device 10 may further include more or fewer components than Figure 6 shown, or have a different configuration from Figure 6 shown.
[0237] It should be noted that one or more of the above-mentioned processors 1002 and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of other elements in the device 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit serves as a processor control (for example, the selection of a variable resistor terminal path connected to an interface).
[0238] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage devices corresponding to the methods described in the embodiments of the present application. The processor 1002 executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, that is, to implement the above-mentioned oil fume control method. The memory 1004 can include high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 1004 can further include a memory remotely set relative to the processor 1002, and these remote memories can be connected to the device 10 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.
[0239] The transmission device 1006 is used to receive or send data via a network. Specific examples of the above-mentioned network can include the wireless network provided by the communication provider of the device 10. In one instance, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 1006 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.
[0240] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the device 10 (or mobile device).
[0241] The embodiments of the present application also provide a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one segment of program related to implementing an oil fume control method in the method embodiments. The at least one instruction or the at least one segment of program is loaded and executed by the processor to implement the oil fume control method provided in the above-mentioned method embodiments.
[0242] Optionally, in this embodiment, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium may include, but is not limited to: various media that can store program codes such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), external hard drives, magnetic disks, or optical discs.
[0243] The embodiment of the present invention also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and these computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads these computer instructions from the computer-readable storage medium, and the processor executes these computer instructions, so that the computer device executes a recipe recommendation method provided in the above various optional implementation manners.
[0244] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims may be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.
[0245] Each embodiment in this application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.
[0246] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk, or an optical disc, etc.
[0247] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims
1. A recipe recommendation method, characterized in that, The method includes: Obtaining current food ingredient stock information and obtaining the dietary preference information of a target object; the current food ingredient stock information includes the stock information of at least one food ingredient; Performing food ingredient analysis processing based on the stock information of the at least one food ingredient to obtain a current food ingredient analysis result, where the current food ingredient analysis result represents the independent recommendation degree of each food ingredient in the at least one food ingredient and the combined recommendation degree between every two of the food ingredients; Performing food ingredient combination processing based on the current food ingredient analysis result to obtain at least one food ingredient combination, and each food ingredient combination includes at least any one of the at least one food ingredient; Performing recipe generation processing based on the at least one food ingredient combination and the dietary preference information to obtain at least one target recipe; Displaying the at least one target recipe to recommend recipes to the target object.
2. The method according to claim 1, wherein The current food ingredient analysis result includes a first analysis result and a second analysis result. Performing food ingredient analysis processing based on the stock information of the at least one food ingredient to obtain the current food ingredient analysis result includes: Determining the nutritional attribute information, growth attribute information, and cultural attribute information of each food ingredient in the at least one food ingredient according to the stock information of the at least one food ingredient; Determining at least one independent recommendation index for each food ingredient and at least one combined recommendation index between every two of the food ingredients according to the nutritional attribute information, growth attribute information, and cultural attribute information of each food ingredient; Determining the first analysis result according to at least one independent recommendation index of each food ingredient, where the first analysis result represents the independent recommendation degree of each food ingredient; Determining the second analysis result according to at least one combined recommendation index between every two of the food ingredients, where the second analysis result represents the combined recommendation degree between every two of the food ingredients.
3. The method according to claim 2, wherein The at least one independent recommendation index includes a first independent recommendation index, and the at least one combined recommendation index includes a first combined recommendation index. The first independent recommendation index and the first combined recommendation index are determined in the following manner: Performing component analysis according to the nutritional attribute information of each food ingredient to obtain the first independent recommendation index of each food ingredient and the first combined recommendation index between every two of the food ingredients.
4. The method according to claim 2, characterized in that The at least one independent recommendation index includes a second independent recommendation index, and the at least one combined recommendation index includes a second combined recommendation index. The second independent recommendation index and the second combined recommendation index are determined in the following manner: Performing spatio-temporal analysis according to the growth attribute information of each food ingredient to obtain the second independent recommendation index of each food ingredient and the second combined recommendation index between every two of the food ingredients.
5. The method according to claim 2, wherein The at least one independent recommendation index includes a third independent recommendation index, and the at least one combined recommendation index includes a third combined recommendation index. The third independent recommendation index and the third combined recommendation index are determined in the following manner: Based on the cultural attribute information of each ingredient, perform sensitivity analysis to obtain the third independent recommendation index of each ingredient and the third combined recommendation index between every two of the ingredients.
6. The method according to claim 1, wherein Based on the current ingredient analysis result, perform ingredient combination processing to obtain at least one ingredient combination, including: Combine the at least one ingredient to obtain at least one candidate ingredient combination; Based on the current ingredient analysis result, determine the combined recommendation degree of each candidate ingredient combination in the at least one candidate ingredient combination; each candidate ingredient combination includes at least one ingredient; Based on the combined recommendation degree of each candidate ingredient combination, screen the at least one candidate ingredient combination to obtain the at least one ingredient combination.
7. The method according to claim 1, characterized in that, Based on the at least one ingredient combination and the diet preference information, perform recipe generation processing to obtain at least one target recipe, including: Based on the at least one ingredient combination, perform recipe generation processing to obtain a candidate recipe set, the candidate recipe set includes at least one candidate recipe; Determine the predicted recommendation index data of each candidate recipe in the at least one candidate recipe; Based on the predicted recommendation index data of each candidate recipe and the diet preference information, screen at least one candidate recipe in the candidate recipe set to determine at least one target recipe.
8. The method according to claim 7, wherein The determination of the predicted recommendation index data of each candidate recipe in the at least one candidate recipe includes: Determine the first predicted index data of each candidate recipe, and the first predicted index data represents the nutritional matching degree of the corresponding candidate recipe; Determine the second predicted index data of each candidate recipe, and the second predicted index data represents the taste matching degree of the corresponding candidate recipe; Determine the third predicted index data of each candidate recipe, and the third predicted index data represents the production matching degree of the corresponding candidate recipe; Based on the first predicted index data of each candidate recipe, the second predicted index data of each candidate recipe, and the third predicted index data of each candidate recipe, determine the predicted recommendation index data of each candidate recipe.
9. A recipe recommendation device, characterized in that, The device includes: An acquisition module, configured to acquire current ingredient reserve information and acquire the diet preference information of a target object; the current ingredient reserve information includes reserve information of at least one ingredient; An ingredient matching analysis module, configured to perform ingredient analysis processing based on the reserve information of the at least one ingredient to obtain a current ingredient analysis result, and the current ingredient analysis result represents the independent recommendation degree of each ingredient in the at least one ingredient and the combined recommendation degree between every two of the ingredients; An ingredient combination module, configured to perform ingredient combination processing based on the current ingredient analysis result to obtain at least one ingredient combination, and each ingredient combination includes at least any one of the at least one ingredient; A recipe generation module, configured to perform recipe generation processing based on the at least one ingredient combination and the diet preference information to obtain at least one target recipe; A display module for displaying the at least one target recipe to recommend recipes to the target object.
10. A computer device, characterized in that, The computer device includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory. The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the recipe recommendation method according to any one of claims 1 to 8.