CN118690065A - A cooking system and method based on recipes - Google Patents

Abstract

The present invention relates to the technical field of recipe recommendation, and in particular to a recipe-based cooking system and method, wherein the cooking system comprises a recipe recommendation subsystem and a cooking assistance subsystem; the recipe recommendation subsystem is used to: in response to a recipe recommendation request of a target user, obtain a target recipe matching a target recipe tag set from a recipe database, and recommend the target recipe to the target user; the cooking assistance subsystem is used to: in response to an operation instruction of the target user for the target recipe, identify the operation instruction to obtain instruction information, and input the instruction information into a binary classification model so that the binary classification model judges the type of the instruction information; when the instruction information belongs to inquiry information, input the instruction information into a recipe question-answering language model; when the instruction information belongs to correction information, input the instruction information into a recipe correction model; and provide the generated answer or the adjusted target recipe to the target user.

Description

A cooking system and method based on recipes

Technical Field

The present invention relates to the technical field of cooking, and in particular to a cooking system and method based on recipes.

Background Art

Recipe recommendation refers to intelligently recommending recipes to users based on their taste preferences, nutritional needs, food taboos, etc. The recipe recommendation system is an application system built based on recipe recommendation technology.

In the related art, a recipe recommendation system can select a matching recipe from a recipe database based on the user's instructions and recommend the recipe to the user. However, the reference data for such a system to select a recipe is only the user's instructions, which may result in the selected recipe not meeting the user's needs.

In addition, once a recipe is finalized, it is often impossible to modify or change it, and users cannot adjust the recipe in real time. Moreover, the recipe recommendation system cannot answer questions raised by users about the recipe, which affects the user's cooking process.

Summary of the invention

In response to the above technical problems, the present invention proposes a recipe-based cooking system and method, which aims to make the recipe more in line with the user's usage habits and preferences, and can answer inquiries about the recipe and modify the recipe according to the modification instructions proposed by the user, thereby increasing the interactivity between the cooking system and the user and ensuring the smoothness of the user's cooking process.

In a first aspect, the present application provides a recipe-based cooking system, the cooking system comprising a recipe recommendation subsystem and a cooking assistance subsystem;

The recipe recommendation subsystem is used to: respond to a recipe recommendation request of a target user, obtain recommendation information in the recipe recommendation request, and determine recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem according to the recommendation information; obtain historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem according to the user identifier, and input the recipe requirement information and the historical recommendation data into a pre-trained recipe label model so that the recipe label model generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation; obtain a target recipe matching the target recipe label set from a recipe database, and recommend the target recipe to the target user; wherein the recipe database records a plurality of recipes consisting of at least one dish;

The cooking assistance subsystem is used to: respond to the target user's operation instruction for the target recipe, identify the operation instruction to obtain instruction information, and input the instruction information into a pre-trained binary classification model so that the binary classification model determines the type of the instruction information; when the instruction information belongs to inquiry information, input the instruction information into a pre-trained recipe question and answer language model so that the recipe question and answer language model generates a response that matches the instruction information; when the instruction information belongs to correction information, input the instruction information into a pre-trained recipe correction model so that the recipe correction model determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information; and provide the generated response or the adjusted target recipe to the target user.

In some embodiments, the cooking system further comprises an audio device and a mobile device used by the target user;

In a case where the recipe recommendation request and/or the operation instruction is audio data of the target user, the recipe recommendation request and/or the operation instruction is acquired by the audio device;

In a case where the recipe recommendation request and/or the operation instruction is audio data of the target user, the recipe recommendation request and/or the operation instruction is sent by the target user through the mobile device.

In some embodiments, the cooking system further includes an audio device and a mobile device used by the target user:

The recommending the target recipe to the target user includes: providing the target recipe to the user in the form of voice broadcast; or sending the target recipe to the target user's mobile device in the form of graphics and text for display;

Providing the generated response or the adjusted target recipe to the target user includes: providing the generated response or the adjusted target recipe to the user in the form of voice broadcast; or sending the generated response or the adjusted target recipe to the user's mobile device in the form of graphics and text for display.

In some embodiments, the label includes at least one of the following: number of dishes, cooking method, cooking time, cooking cost, ingredient preference, taste preference, and nutritional value.

In some embodiments,

The invention also includes: obtaining environmental information corresponding to the recipe recommendation request, wherein the environmental information includes at least one of the following: festival information, social hot spots, weather information, and other user search hot words;

The step of inputting the recipe requirement information and the historical recommendation data into a pre-trained recipe label model comprises: inputting the environmental information, the recipe requirement information and the historical recommendation data into the recipe label model, so that the recipe label model generates a target recipe label set; wherein the labels include: popular ingredients, holiday-designated dishes, and season information.

In some embodiments, each recipe recorded in the recipe database corresponds to a recipe-associated tag set, and the recipe-associated information is generated by a set of dish information of at least one dish contained in the corresponding recipe; and obtaining a target recipe matching the target recipe tag set from the recipe database includes:

The association degree between the recipe-associated tag sets of the plurality of recipes and the target recipe tag set is calculated, and the recipe with the highest association degree is determined as the target recipe.

In some embodiments,

The recommending the target recipe to the target user includes: providing the target recipe to the target user in the form of voice broadcast;

The historical recommendation data includes a historical playback speed and a historical playback volume, and further includes: determining a target playback speed and a target playback volume corresponding to the target recipe according to the historical playback speed and the historical playback volume, and playing the target recipe according to the target playback speed and the target playback volume.

In a second aspect, the present application provides a recipe-based cooking method, which is applied to a recipe-based cooking system, wherein the cooking system includes a recipe recommendation subsystem and a cooking assistance subsystem; the method includes:

In response to a recipe recommendation request from a target user, obtaining recommendation information in the recipe recommendation request, and determining recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem according to the recommendation information;

Acquire historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem according to the user identifier, and input the recipe requirement information and the historical recommendation data into a pre-trained recipe label model, so that the recipe label model generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation;

Acquire a target recipe matching the target recipe tag set from a recipe database, and recommend the target recipe to the target user; wherein the recipe database records a plurality of recipes consisting of at least one dish;

In response to the target user's operation instruction for the target recipe, the operation instruction is identified to obtain instruction information, and the instruction information is input into a pre-trained binary classification model so that the binary classification model determines the type of the instruction information;

In the case where the instruction information belongs to inquiry information, the instruction information is input into a pre-trained recipe question-answering language model, so that the recipe question-answering language model generates a response matching the instruction information;

In the case where the instruction information belongs to correction type information, the instruction information is input into a pre-trained recipe correction model, so that the recipe correction model determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information;

The generated response or the adjusted target recipe is provided to the target user.

In a third aspect, an electronic device includes a processor and a memory;

The processor is connected to the memory;

The memory is used to store executable program code;

The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute a recipe-based cooking method as described in the second aspect.

In a fourth aspect, a computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements a recipe-based cooking method as described in the second aspect.

The beneficial technical effects of the present invention include at least: adopting a recipe-based cooking system and method, on the one hand, when selecting a recipe, the recipe recommendation subsystem not only considers the recipe requirement information obtained by the user's real-time recipe recommendation request, but also considers the user's historical recommendation data, so that the selected target recipe is more in line with the user's usage habits and preferences; on the other hand, the cooking assistance subsystem can distinguish the user's instruction information, can answer the distinguished inquiries, and can also modify the recipe according to the correction instructions proposed by the user, thereby increasing the interactivity between the cooking system and the user, and ensuring the smoothness of the user's cooking process.

Other features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further described below in conjunction with the accompanying drawings:

FIG. 1 is an architecture diagram of a recipe-based cooking system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of generating a recipe database according to an embodiment of the present invention.

FIG. 3 is a flow chart of a recipe-based cooking method according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

DETAILED DESCRIPTION

The technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the referred device or element must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as a limitation of the present invention.

An embodiment of the present application provides a recipe-based cooking system. Please refer to FIG. 1 . As shown in FIG. 1 , the recipe-based cooking system 10 includes a recipe recommendation subsystem 11 and a cooking assistance subsystem 12 .

The recipe recommendation subsystem 11 includes a recipe label model 111 and a recipe database 112 .

The recipe recommendation subsystem 11 is used to: respond to a recipe recommendation request from a target user, obtain recommendation information in the recipe recommendation request, and determine recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem 11 based on the recommendation information; obtain historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem 11 based on the user identifier, and input the recipe requirement information and the historical recommendation data into a pre-trained recipe label model 111, so that the recipe label model 111 generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation; obtain a target recipe matching the target recipe label set from a recipe database 112, and recommend the target recipe to the target user; wherein the recipe database 112 records a plurality of recipes consisting of at least one dish.

The recipe label model 111 is a pre-trained machine learning model for generating a recipe label set based on the recipe requirement information provided by the user and the user's historical recommendation data. The recipe requirement information is obtained by the recipe recommendation subsystem 11 based on the recommendation information in the recipe recommendation request obtained from the target user. For example, the recommendation information may be "I want to eat chopped pepper fish head today" or "I want to eat Sichuan cuisine for dinner." Based on the recommendation information of "I want to eat chopped pepper fish head today", the recipe requirement information can be determined as adding a dish named "chopped pepper fish head" to the recipe; based on the recommendation information of "I want to eat Sichuan cuisine for dinner", the dish requirement information can be determined as the dish labels in the recipe are all "Sichuan cuisine".

The recipe database 112 is a comprehensive and practical recipe database constructed by collecting and integrating dietary recipe information through multiple channels. As shown in FIG2 , the system collects public data from public data sources such as professional recipe websites, catering companies, nutritionists and diet experts, industry associations, questionnaires, etc. The system strictly cleans, integrates, classifies, normalizes and visualizes the collected data by considering factors such as the nutritional composition of ingredients, food incompatibility, cooking methods, and calorie balance to ensure the accuracy and availability of the data, so as to ensure that the generated recipes are both nutritious and healthy. Using a large language model (LLM), auxiliary tools and an existing recipe database, new basic recipes can be generated. On the basis of the basic recipes, professionals reproduce the cooking process, re-record and mark relevant data (audio and video recording information, floating range of material quantity, nutritional value, cooking methods, precautions, etc.) to continuously update and improve the recipe database. This process not only enriches the types of recipes, but also improves the quality and applicability of recipes.

Specifically, the historical recommendation data may include the number of times a recipe or dish is selected after being recommended, and the number of times it is rejected after being recommended. If the recipe or dish is selected many times, the probability of the recipe or dish being recommended subsequently will increase accordingly; if the recipe or dish is rejected many times, the recipe or dish will not be recommended subsequently.

The historical recommendation data may also include time attributes, for example, the historical data referenced by breakfast, lunch, and dinner are different. Each of the three meals refers to the recipe corresponding to the corresponding meal in history, such as: the historical data referenced by the breakfast recipe also only includes the recipes recommended for breakfast in history; the historical data referenced by the lunch recipe also only includes the recipes recommended for lunch in history. Or, the proportion of the historical data referenced by the breakfast recipe for the recipe of breakfast is greater than that of the other two meals.

For example, different historical recommendation data have different weights based on the time of recommendation. The historical recommendation data closer to the current moment has a higher weight than the historical recommendation data farther from the current moment. In other words, the weight of the historical recommendation data is inversely proportional to the time from the current moment.

The cooking assistance subsystem 12 is used to: respond to the operation instruction of the target user for the target recipe, identify the operation instruction to obtain instruction information, and input the instruction information into a pre-trained binary classification model so that the binary classification model 121 can judge the type of the instruction information; when the instruction information belongs to inquiry information, input the instruction information into a pre-trained recipe question and answer language model 122 so that the recipe question and answer language model 122 generates a response matching the instruction information; when the instruction information belongs to correction information, input the instruction information into a pre-trained recipe correction model 123 so that the recipe correction model 123 determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information; and provide the generated response or the adjusted target recipe to the target user.

The binary classification model 121 is a model in machine learning that is used to classify input data into two mutually exclusive categories. This type of model is widely used in various scenarios, such as spam detection (classifying emails into spam and non-spam), sentiment analysis (classifying text into positive comments and negative comments), disease diagnosis (classifying disease states into diseased and non-disease), etc. In a binary classification model, there is usually a target variable, which is binary and can be 0 or 1, representing two different categories. The task of the model is to predict the value of the target variable by analyzing the input feature vector. During the training phase, the model learns the relationship between the input features and the target variable, thereby establishing a classification boundary so that new input data can be correctly classified during the testing phase. The binary classification model in this specification can be any one of logistic regression, support vector machine (SVM), decision tree, random forest, and neural network, and this specification does not limit this.

The recipe question-and-answer language model 122 is a large language model trained based on training data in the cooking field, and the recipe correction model 123 is a machine learning model built based on natural language processing technology and generative adversarial networks.

In this embodiment, on the one hand, when selecting a recipe, the recipe recommendation subsystem not only takes into account the recipe requirement information obtained by the user's real-time recipe recommendation request, but also takes into account the user's historical recommendation data, so that the selected target recipe is more in line with the user's usage habits and preferences; on the other hand, the cooking assistance subsystem can distinguish the user's instruction information, answer the distinguished inquiries, and modify the recipe according to the modification instructions proposed by the user, thereby increasing the interactivity between the cooking system and the user, and ensuring the smoothness of the user's cooking process.

In one embodiment, the cooking system also includes an audio device and a mobile device used by the target user; when the recipe recommendation request and/or the operation instruction is the audio data of the target user, the recipe recommendation request and/or the operation instruction is obtained by the audio device; when the recipe recommendation request and/or the operation instruction is the audio data of the target user, the recipe recommendation request and/or the operation instruction is sent by the target user through the mobile device.

In this embodiment, with the help of cutting-edge big models and NLP technology, the system successfully realizes the synchronous audio-visual interaction between mobile devices and audio devices, improving the user experience. With the strong support of the cooking system, it can accurately and intelligently recommend recipes that suit personal tastes, allowing users to easily master the cooking skills of various delicacies. The recipe correction system is dynamically adjusted according to the user's real-time feedback to ensure that every step of each dish can accurately meet the user's expectations, making the cooking process more refined and personalized. This system not only optimizes the cooking process, but also greatly improves the user's sense of participation and satisfaction in cooking activities through intelligent interaction.

In one embodiment, the cooking system also includes an audio device and a mobile device used by the target user: recommending the target recipe to the target user includes: providing the target recipe to the user in the form of a voice broadcast; or sending the target recipe to the target user's mobile device in the form of text and graphics for display; providing the generated response or the adjusted target recipe to the target user includes: providing the generated response or the adjusted target recipe to the user in the form of a voice broadcast; or sending the generated response or the adjusted target recipe to the user's mobile device in the form of text and graphics for display.

In this embodiment, with the help of cutting-edge big models and NLP technology, the system successfully realizes the synchronous audio-visual interaction between mobile devices and audio devices, improving the user experience. With the strong support of the cooking system, it can accurately and intelligently recommend recipes that suit personal tastes, allowing users to easily master the cooking skills of various delicacies. The recipe correction system is dynamically adjusted according to the user's real-time feedback to ensure that every step of each dish can accurately meet the user's expectations, making the cooking process more refined and personalized. This system not only optimizes the cooking process, but also greatly improves the user's sense of participation and satisfaction in cooking activities through intelligent interaction.

In one embodiment, the label includes at least one of the following: number of dishes, cooking method, cooking time, cooking cost, ingredient preference, taste preference, and nutritional value.

In one embodiment, it also includes: obtaining environmental information corresponding to the recipe recommendation request, the environmental information including at least one of the following: holiday information, social hot spots, weather information, and other user search hot words; the step of inputting the recipe requirement information and the historical recommendation data into a pre-trained recipe label model includes: inputting the environmental information, the recipe requirement information, and the historical recommendation data into the recipe label model, so that the recipe label model generates a target recipe label set; wherein the labels include: popular ingredients, holiday designated dishes, and season information.

In this embodiment, the system uses large models and NLP technology to accurately identify user needs and achieve personalized recipe recommendations. It is smarter and more efficient than traditional search methods, has high real-time and dynamic capabilities, and combines multiple information for dynamic updates to ensure that recommended recipes are always synchronized with user needs and the environment.

In one embodiment, each recipe recorded in the recipe database corresponds to a recipe associated tag set, and the recipe associated information is generated by a set of dish information of at least one dish contained in the corresponding recipe; obtaining a target recipe matching the target recipe tag set from the recipe database includes: calculating the degree of association between the recipe associated tag sets of the multiple recipes and the target recipe tag set, and determining the recipe with the highest degree of association as the target recipe.

For example, if the user uses the recipe recommendation system during the Qingming Festival, and the customer's recipe requirement information is voice input "I want to eat spicy food, preferably Sichuan cuisine", and the customer has recently selected recipes with the dish "Maoxuewang". Based on the above historical recommendation data and recipe requirement information, a target recipe tag set with "Qingming, spicy, Sichuan cuisine, Maoxuewang" can be generated. The recipe-related tag sets of each recipe in the recipe database are traversed, and the degree of association between each recipe-related tag set and the target recipe tag set is calculated to determine the target recipe with the highest degree of association. The target recipe is provided to the user.

In one embodiment, recommending the target recipe to the target user includes: providing the target recipe to the target user in the form of voice broadcast; the historical recommendation data includes a historical playback speed and a historical playback volume, and also includes: determining a target playback speed and a target playback volume corresponding to the target recipe according to the historical playback speed and the historical playback volume, and playing the target recipe according to the target playback speed and the target playback volume.

The present application also proposes a recipe-based cooking method, see FIG3 , the method is applied to a recipe-based cooking system, the cooking system includes a recipe recommendation subsystem and a cooking assistance subsystem; and at least includes the following steps:

Step 302: In response to a recipe recommendation request from a target user, obtain recommendation information in the recipe recommendation request, and determine recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem according to the recommendation information;

Step 304: acquiring historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem according to the user identifier, and inputting the recipe requirement information and the historical recommendation data into a pre-trained recipe label model, so that the recipe label model generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation;

Step 306: Acquire a target recipe matching the target recipe tag set from a recipe database, and recommend the target recipe to the target user; wherein the recipe database records a plurality of recipes consisting of at least one dish;

Step 308: In response to the target user's operation instruction for the target recipe, the operation instruction is identified to obtain instruction information, and the instruction information is input into a pre-trained binary classification model so that the binary classification model determines the type of the instruction information;

Step 310: if the instruction information is a question-type information, input the instruction information into a pre-trained recipe question-answering language model, so that the recipe question-answering language model generates a response matching the instruction information;

Step 312: if the instruction information belongs to correction information, input the instruction information into a pre-trained recipe correction model, so that the recipe correction model determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information;

Step 314: Provide the generated response or the adjusted target recipe to the target user.

In this embodiment, on the one hand, when selecting a recipe, the recipe recommendation subsystem not only takes into account the recipe requirement information obtained by the user's real-time recipe recommendation request, but also takes into account the user's historical recommendation data, so that the selected target recipe is more in line with the user's usage habits and preferences; on the other hand, the cooking assistance subsystem can distinguish the user's instruction information, answer the distinguished inquiries, and modify the recipe according to the modification instructions proposed by the user, thereby increasing the interactivity between the cooking system and the user, and ensuring the smoothness of the user's cooking process.

Optionally, the above recipe-based cooking method may also include the following embodiments:

In one embodiment, the cooking system also includes an audio device and a mobile device used by the target user; when the recipe recommendation request and/or the operation instruction is the audio data of the target user, the recipe recommendation request and/or the operation instruction is obtained by the audio device; when the recipe recommendation request and/or the operation instruction is the audio data of the target user, the recipe recommendation request and/or the operation instruction is sent by the target user through the mobile device.

In one embodiment, the cooking system also includes an audio device and a mobile device used by the target user: recommending the target recipe to the target user includes: providing the target recipe to the user in the form of a voice broadcast; or sending the target recipe to the target user's mobile device in the form of text and graphics for display; providing the generated response or the adjusted target recipe to the target user includes: providing the generated response or the adjusted target recipe to the user in the form of a voice broadcast; or sending the generated response or the adjusted target recipe to the user's mobile device in the form of text and graphics for display.

In one embodiment, the label includes at least one of the following: number of dishes, cooking method, cooking time, cooking cost, ingredient preference, taste preference, and nutritional value.

In one embodiment, it also includes: obtaining environmental information corresponding to the recipe recommendation request, the environmental information including at least one of the following: holiday information, social hot spots, weather information, and other user search hot words; the step of inputting the recipe requirement information and the historical recommendation data into a pre-trained recipe label model includes: inputting the environmental information, the recipe requirement information, and the historical recommendation data into the recipe label model, so that the recipe label model generates a target recipe label set; wherein the labels include: popular ingredients, holiday designated dishes, and season information.

In one embodiment, each recipe recorded in the recipe database corresponds to a recipe associated tag set, and the recipe associated information is generated by a set of dish information of at least one dish contained in the corresponding recipe; obtaining a target recipe matching the target recipe tag set from the recipe database includes: calculating the degree of association between the recipe associated tag sets of the multiple recipes and the target recipe tag set, and determining the recipe with the highest degree of association as the target recipe.

In one embodiment, recommending the target recipe to the target user includes: providing the target recipe to the target user in the form of voice broadcast; the historical recommendation data includes a historical playback speed and a historical playback volume, and also includes: determining a target playback speed and a target playback volume corresponding to the target recipe according to the historical playback speed and the historical playback volume, and playing the target recipe according to the target playback speed and the target playback volume.

It can be understood that the technical concept of the recipe-based cooking method provided in this embodiment is similar to the technical concept of the aforementioned recipe-based cooking system, and this embodiment will not be repeated here.

Please refer to FIG. 4 , which is a schematic diagram of the structure of an electronic device provided in yet another embodiment of the present specification.

As shown in FIG. 4 , the electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

The communication bus 402 may be used to realize the connection and communication among the above-mentioned components.

The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

The network interface 404 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts within the entire electronic device 400, and executes various functions and processes data of the electronic device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 401 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

Among them, the memory 405 may include RAM or ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be at least one storage device located away from the aforementioned processor 401. The memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor 401 may be used to call the application stored in the memory 405 and execute the method in one or more of the above-mentioned embodiments.

Another embodiment of the present specification provides a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes one or more steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and predicted or used as independent downstream tasks, they can be stored in the computer-readable storage medium.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

The above description is only the preferred embodiment disclosed in this application and the description of the technical principle used. Those skilled in the art should understand that the scope of protection involved in this disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but also should cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this disclosure (but not limited to) to form a technical solution.

In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

Claims (10)

1. A recipe-based cooking system, characterized in that the cooking system includes a recipe recommendation subsystem and a cooking assistance subsystem; The recipe recommendation subsystem is used to: in response to a recipe recommendation request of a target user, obtain recommendation information in the recipe recommendation request, and determine recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem according to the recommendation information; obtain historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem according to the user identifier, and input the recipe requirement information and the historical recommendation data into a pre-trained recipe label model so that the recipe label model generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation; obtain a target recipe matching the target recipe label set from a recipe database, and recommend the target recipe to the target user; wherein the recipe database records a plurality of recipes consisting of at least one dish; The cooking assistance subsystem is used to: respond to the target user's operation instruction for the target recipe, identify the operation instruction to obtain instruction information, and input the instruction information into a pre-trained binary classification model so that the binary classification model determines the type of the instruction information; when the instruction information belongs to inquiry information, input the instruction information into a pre-trained recipe question and answer language model so that the recipe question and answer language model generates a response that matches the instruction information; when the instruction information belongs to correction information, input the instruction information into a pre-trained recipe correction model so that the recipe correction model determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information; and provide the generated response or the adjusted target recipe to the target user. 2. A recipe-based cooking system according to claim 1, characterized in that the cooking system further comprises an audio device and a mobile device used by the target user; In a case where the recipe recommendation request and/or the operation instruction is audio data of the target user, the recipe recommendation request and/or the operation instruction is acquired by the audio device; In a case where the recipe recommendation request and/or the operation instruction is audio data of the target user, the recipe recommendation request and/or the operation instruction is sent by the target user through the mobile device. 3. The recipe-based cooking system according to claim 1, wherein the cooking system further comprises an audio device and a mobile device used by the target user: The recommending the target recipe to the target user includes: providing the target recipe to the user in the form of voice broadcast; or sending the target recipe to the target user's mobile device in the form of graphics and text for display; Providing the generated response or the adjusted target recipe to the target user includes: providing the generated response or the adjusted target recipe to the user in the form of voice broadcast; or sending the generated response or the adjusted target recipe to the user's mobile device in the form of graphics and text for display. 4. A recipe-based cooking system as described in claim 1, characterized in that the label includes at least one of the following: number of dishes, cooking method, cooking time, cooking cost, ingredient preference, taste preference, and nutritional value. 5. A recipe-based cooking system as claimed in claim 1, characterized in that: The invention also includes: obtaining environmental information corresponding to the recipe recommendation request, wherein the environmental information includes at least one of the following: festival information, social hot spots, weather information, and other user search hot words; The step of inputting the recipe requirement information and the historical recommendation data into a pre-trained recipe label model comprises: inputting the environmental information, the recipe requirement information and the historical recommendation data into the recipe label model, so that the recipe label model generates a target recipe label set; wherein the labels include: popular ingredients, holiday-designated dishes, and season information. 6. A recipe-based cooking system according to claim 1, characterized in that each recipe recorded in the recipe database corresponds to a recipe-related tag set, and the recipe-related information is generated by a set of dish information of at least one dish contained in the corresponding recipe; and obtaining a target recipe matching the target recipe tag set from the recipe database comprises: The association degree between the recipe-associated tag sets of the plurality of recipes and the target recipe tag set is calculated, and the recipe with the highest association degree is determined as the target recipe. 7. A recipe-based cooking system as claimed in claim 1, characterized in that: The recommending the target recipe to the target user includes: providing the target recipe to the target user in the form of voice broadcast; The historical recommendation data includes a historical playback speed and a historical playback volume, and further includes: determining a target playback speed and a target playback volume corresponding to the target recipe according to the historical playback speed and the historical playback volume, and playing the target recipe according to the target playback speed and the target playback volume. 8. A recipe-based cooking method, characterized in that it is applied to a recipe-based cooking system, wherein the cooking system includes a recipe recommendation subsystem and a cooking assistance subsystem; the method comprises: In response to a recipe recommendation request from a target user, obtaining recommendation information in the recipe recommendation request, and determining recipe requirement information and a user identifier of the target user in the recipe recommendation subsystem according to the recommendation information; Acquire historical recommendation data corresponding to the target user stored in the recipe recommendation subsystem according to the user identifier, and input the recipe requirement information and the historical recommendation data into a pre-trained recipe label model, so that the recipe label model generates a target recipe label set, wherein the target recipe label set includes a plurality of labels for recipe generation; Acquire a target recipe matching the target recipe tag set from a recipe database, and recommend the target recipe to the target user; wherein the recipe database records a plurality of recipes consisting of at least one dish; In response to the target user's operation instruction for the target recipe, the operation instruction is identified to obtain instruction information, and the instruction information is input into a pre-trained binary classification model so that the binary classification model determines the type of the instruction information; In the case where the instruction information belongs to inquiry information, the instruction information is input into a pre-trained recipe question-answering language model, so that the recipe question-answering language model generates a response matching the instruction information; In the case where the instruction information belongs to correction type information, the instruction information is input into a pre-trained recipe correction model, so that the recipe correction model determines the step to be corrected in the target recipe targeted by the instruction information, and adjusts the step to be corrected according to the correction information contained in the instruction information; The generated response or the adjusted target recipe is provided to the target user. 9. An electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the recipe-based cooking method as claimed in claim 8. 10. A computer-readable storage medium, characterized in that a computer program is stored thereon, and when the computer program is executed by a processor, the recipe-based cooking method according to claim 8 is implemented.