Data generation method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to a data generation method, which is applied to the field of digital medical treatment and comprises the following steps: acquiring user data of a user; preprocessing the user data to obtain corresponding target user data; extracting target key features from the target user data; based on a pre-trained recommendation model, screening out a target recipe template matched with the target key feature from a preset recipe template library; performing optimization processing on the target recipe template based on the allergic food information of the user to obtain a corresponding first recipe template; sorting the first recipe template according to dates to obtain a corresponding target recipe plan; and sending the target recipe plan to the user. The invention further provides a data generation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target recipe plan can be stored in a block chain. According to the invention, the processing efficiency and accuracy of recipe generation are effectively improved.

Description

Technical Field

[0001] This application relates to the technical fields of artificial intelligence development and digital medicine, and particularly relates to a data generation method, apparatus, computer device, and storage medium. Background Art

[0002] In the medical field, customizing recipes is a key link to ensure that patients receive appropriate nutritional support, especially in hospital canteens and nutritious meal services. However, in the stage of implementing customized recipes, manual configuration of recipes still faces many challenges, resulting in a cumbersome and inefficient process.

[0003] Customizing recipes requires comprehensive consideration of multiple factors such as the patient's age, height, weight, body type, activity intensity, medical condition, and the doctor's dietary instructions. The traditional method of customizing recipes is to determine the patient's nutritional needs through manual calculation or by manually using simple nutritional assessment tools. However, this method is not only time-consuming and laborious but also prone to errors. For example, the calculation method requires calculating the daily required energy and the intake of various nutrients through complex formulas based on the patient's specific information, while the food exchange portion method requires detailed classification and equivalent exchange of foods, and these processes all require manual operation, greatly increasing the workload and the possibility of errors. Summary of the Invention

[0004] The purpose of the embodiments of this application is to propose a data generation method, apparatus, computer device, and storage medium to solve the technical problems that the existing method of customizing recipes has low processing efficiency and is prone to errors.

[0005] To solve the above technical problems, the embodiments of this application provide a data generation method, which adopts the following technical solutions:

[0006] Obtain the user data of the user; wherein, the user data includes the personal information, health record data, goal setting, and taste preference of the user;

[0007] Preprocess the user data to obtain corresponding target user data;

[0008] Extract target key features from the target user data;

[0009] Based on a pre-trained recommendation model, screen out a target recipe template that matches the target key features from a preset recipe template library;

[0010] Optimize the target recipe template based on the user's food allergy information to obtain a corresponding first recipe template;

[0011] Sort the first recipe template by date to obtain a corresponding target recipe plan;

[0012] Send the target recipe plan to the user.

[0013] Further, the step of screening out a target recipe template matching the target key features from a preset recipe template library based on a pre-trained recommendation model specifically includes:

[0014] Call the recommendation model and call the recipe template library;

[0015] Traverse the recipe template library based on the recommendation model, and calculate the matching degree between the target key features and each recipe template included in the recipe template library;

[0016] Screen out the second recipe template with the highest matching degree from all the recipe templates;

[0017] Use the second recipe template as the target recipe template.

[0018] Further, the step of optimizing the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template specifically includes:

[0019] Obtain the allergic food information corresponding to the user;

[0020] Judge whether the target recipe template contains designated ingredients corresponding to the allergic food information;

[0021] If so, screen out the ingredient-related information corresponding to the designated ingredients from the target recipe template;

[0022] Perform deletion processing on the ingredient-related information in the target recipe template to obtain a third recipe template after deletion;

[0023] Use the third recipe template as the first recipe template.

[0024] Further, the step of sending the target recipe plan to the user specifically includes:

[0025] Perform nutritional verification on the target recipe plan based on a preset ingredient knowledge base;

[0026] If the target recipe plan passes the nutritional verification, obtain the category information corresponding to the target recipe template;

[0027] Obtain the measurement plan and suggestion information corresponding to the category information;

[0028] Perform information supplementation on the target recipe plan based on the measurement plan and the suggestion information to obtain a first target recipe plan after supplementation;

[0029] Send the first target recipe plan to the user.

[0030] Further, the step of performing nutritional verification on the target recipe plan based on a preset ingredient knowledge base specifically includes:

[0031] Perform calculation processing on the usage amount of each ingredient included in the target recipe plan based on the ingredient knowledge base to obtain the nutritional components of the target recipe plan;

[0032] Judge whether the nutritional components meet the requirements of preset nutritional thresholds;

[0033] If so, determine that the target recipe plan passes the nutritional verification, otherwise determine that the target recipe plan fails the nutritional verification.

[0034] Further, the step of preprocessing the user data to obtain corresponding target user data specifically includes:

[0035] Perform cleaning processing on the user data to obtain corresponding first user data;

[0036] Perform formatting processing on the first user data to obtain corresponding second user data;

[0037] Perform normalization processing on the second user data to obtain corresponding third user data;

[0038] Use the third user data as the target user data.

[0039] Further, after the step of sending the target recipe plan to the user, it further includes:

[0040] Judge whether feedback information corresponding to the target recipe plan returned by the user is received;

[0041] If so, perform adjustment processing on the target recipe plan based on the feedback information to obtain an adjusted second target recipe plan;

[0042] Perform storage processing on the second target recipe plan.

[0043] To solve the above technical problems, an embodiment of the present application further provides a data generation device, which adopts the following technical solutions:

[0044] An acquisition module, configured to acquire user data of a user; wherein, the user data includes personal information of the user, health record data, goal setting, and taste preferences;

[0045] A preprocessing module, configured to preprocess the user data to obtain corresponding target user data;

[0046] An extraction module, configured to extract target key features from the target user data;

[0047] A screening module, configured to screen out a target recipe template that matches the target key features from a preset recipe template library based on a pre-trained recommendation model;

[0048] An optimization module, configured to perform optimization processing on the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template;

[0049] A sorting module, configured to sort the first recipe template by date to obtain a corresponding target recipe plan;

[0050] A sending module, configured to send the target recipe plan to the user.

[0051] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

[0052] Obtain the user's user data; wherein, the user data includes the user's personal information, health record data, goal setting, and taste preference;

[0053] Preprocess the user data to obtain corresponding target user data;

[0054] Extract target key features from the target user data;

[0055] Based on a pre-trained recommendation model, screen out a target recipe template that matches the target key features from a preset recipe template library;

[0056] Perform optimization processing on the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template;

[0057] Sort the first recipe template by date to obtain a corresponding target recipe plan;

[0058] Send the target recipe plan to the user.

[0059] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

[0060] Obtain the user's user data; wherein, the user data includes the user's personal information, health record data, goal setting, and taste preference;

[0061] Preprocess the user data to obtain corresponding target user data;

[0062] Extract target key features from the target user data;

[0063] Based on a pre-trained recommendation model, screen out a target recipe template that matches the target key features from a preset recipe template library;

[0064] Optimize the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template;

[0065] Sort the first recipe template by date to obtain a corresponding target recipe plan;

[0066] Send the target recipe plan to the user.

[0067] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0068] The present application first obtains the user data of the user; wherein, the user data includes the personal information, health record data, goal setting, and taste preference of the user; then preprocesses the user data to obtain corresponding target user data; then extracts target key features from the target user data; subsequently, based on a pre-trained recommendation model, screens out a target recipe template that matches the target key features from a preset recipe template library; further optimizes the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template; sorts the first recipe template by date to obtain a corresponding target recipe plan; and finally sends the target recipe plan to the user. By preprocessing the obtained user data to obtain target user data, then extracting target key features from the target user data, and then based on a pre-trained recommendation model, screening out a target recipe template that matches the target key features from a preset recipe template library, and optimizing the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template, subsequently sorting the first recipe template by date to obtain a corresponding target recipe plan, and finally sending the target recipe plan to the user, thus, through the present application, based on the use of the recommendation model and the recipe template library, it is possible to automatically and accurately construct a target recipe plan that corresponds to the user's user data and meets the user's personalized needs, effectively improving the processing efficiency and accuracy of recipe generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] To more clearly illustrate the solutions in this application, the following provides a brief introduction to the drawings required for the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0070] Figure 1 is an exemplary system architecture diagram to which this application can be applied;

[0071] Figure 2 A flowchart of an embodiment of the data generation method according to this application;

[0072] Figure 3 is a schematic structural diagram of an embodiment of the data generation device according to this application;

[0073] Figure 4 is a schematic structural diagram of an embodiment of the computer device according to this application. Detailed implementation manners

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the description of the embodiments of this application in this specification are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0075] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0076] To enable those skilled in the technical field to better understand the solutions of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings.

[0077] Such as Figure 1As shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0078] Users can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0079] The terminal device 101 may be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 may also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop portable computer, a desktop computer, etc.

[0080] The server 103 may be a server providing various services, such as a background server supporting the pages displayed on the terminal device 101.

[0081] It should be noted that the data generation method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the data generation device is generally set in the server / terminal device.

[0082] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0083] are merely illustrative. According to the implementation requirements, there may be any number of terminal devices, networks, and servers. Figure 2 Continuing to refer to Figure 2 , a flowchart showing an embodiment of the data generation method according to the present application is shown. According to different requirements, the order of the steps in this flowchart may be changed, and some steps may be omitted. The data generation method provided by the embodiments of the present application can be applied to any scenario requiring data generation. Then, this data generation method can be applied to the products in these scenarios. For example, recipe generation in the field of digital medicine. The data generation method includes the following steps:

[0084] Step S201: Obtain the user data of the user; wherein, the user data includes the personal information, health record data, goal setting, and taste preference of the user.

[0085] In this embodiment, the electronic device (such as Figure 1 the server / terminal device shown) on which the data generation method runs can obtain the user data of the user through a wired connection method or a wireless connection method. It should be noted that the above wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods. The execution subject of this application can specifically be a data generation system, or simply referred to as a system. Among them, the process of obtaining the personal information of the user includes: designing a user-friendly interface to allow the user to input personal information such as age, gender, height, and weight. And storing the personal information of the user in the database through the API or manual input method.

[0086] The process of obtaining the health record data of the user includes: providing a detailed health questionnaire to ask about the user's medical history (such as high uric acid, cardiovascular diseases, etc.), allergic foods, etc. Allowing the user to upload existing health reports or medical records to more comprehensively understand the user's health status.

[0087] The goal setting of the user can include setting hard indicators such as target calories, protein ratio, and fat ratio. Among them, according to the user's weight, height, age, and activity level, professional nutrition calculation tools or algorithms can be used to estimate the user's daily calorie requirement. And according to the user's nutritional needs and health goals (such as weight loss, muscle gain, etc.), set reasonable ratios of nutritional components such as protein and fat. In addition, the user is allowed to manually adjust these indicators to meet their personalized needs.

[0088] The process of obtaining the taste preference of the user includes: designing a taste preference questionnaire to ask about the user's preference levels for tastes such as spicy, sweet, and sour.

[0089] Step S202: Preprocess the user data to obtain the corresponding target user data.

[0090] In this embodiment, for the specific implementation process of preprocessing the user data to obtain the corresponding target user data, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0091] Step S203: Extract the target key features from the target user data.

[0092] In this embodiment, the target key features including the user's nutritional needs, taste preferences, allergy information, etc. can be obtained by extracting key features from the above-mentioned target user data.

[0093] Step S204: Based on the pre-trained recommendation model, screen out the target recipe template that matches the target key features from the preset recipe template library.

[0094] In this embodiment, for the specific implementation process of screening out the target recipe template that matches the target key features from the preset recipe template library based on the pre-trained recommendation model, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0095] Step S205: Optimize the target recipe template based on the user's allergic food information to obtain the corresponding first recipe template.

[0096] In this embodiment, for the specific implementation process of optimizing the target recipe template based on the user's allergic food information to obtain the corresponding first recipe template, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0097] Step S206: Sort the first recipe template by date to obtain the corresponding target recipe plan.

[0098] In this embodiment, the system can sort the generated first recipe template plan by date and generate a daily dining plan corresponding to the preset time period, that is, obtain the above-mentioned target recipe plan. Among them, the selection of the above time period can be set according to actual business needs, for example, it can be set to 14 days.

[0099] Step S207: Send the target recipe plan to the user.

[0100] In this embodiment, for the specific implementation process of sending the target recipe plan to the user, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0101] This application first obtains the user's user data; among them, the user data includes the user's personal information, health record data, goal setting, and taste preferences; then preprocesses the user data to obtain corresponding target user data; then extracts target key features from the target user data; subsequently, based on a pre-trained recommendation model, filters out a target recipe template that matches the target key features from a preset recipe template library; further optimizes the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template; sorts the first recipe template by date to obtain a corresponding target recipe plan; and finally sends the target recipe plan to the user. This application preprocesses the obtained user data to obtain target user data, then extracts target key features from the target user data, and then, based on a pre-trained recommendation model, filters out a target recipe template that matches the target key features from a preset recipe template library, optimizes the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template, subsequently sorts the first recipe template by date to obtain a corresponding target recipe plan, and finally sends the target recipe plan to the user. In this way, through this application, based on the use of the recommendation model and the recipe template library, it is possible to automatically and accurately construct a target recipe plan that corresponds to the user's user data and meets the user's personalized needs, effectively improving the processing efficiency and accuracy of recipe generation.

[0102] In some alternative implementation manners, step S204 includes the following steps:

[0103] Call the recommendation model and call the recipe template library.

[0104] In this embodiment, the above-mentioned recommendation model is a model obtained by training an initial model constructed by a content-based recommendation algorithm using historical data (user-recipe template interaction data). During the model training process, the model parameters are continuously adjusted to improve the accuracy and generalization ability of the recommendation model.

[0105] The above-mentioned recipe template library is a pre-constructed database containing various types of recipe templates, and each recipe template includes information such as nutritional components, ingredients, cooking methods, etc. Specifically, a series of standardized recipe templates can be generated based on big data and professional dietitian research and analysis. These recipe templates cover multiple fields such as high uric acid, cardiovascular diseases, obesity, PCOS special recipes, intestinal health special recipes, and three highs special recipes, etc. Each recipe template combines nutritional knowledge and user needs to ensure the scientificity and practicability of the recipes.

[0106] Exemplarily, taking the high uric acid recipe template as an example, it can be divided into nine high uric acid sub-templates according to risk, diagnosis, and calorie intake grading, including high-risk groups (1200 - 1400 kcal), high-risk groups (1400 - 1600 kcal), high-risk groups (1600 - 1800 kcal), confirmed high uric acid (1200 - 1400 kcal), confirmed high uric acid (1400 - 1600 kcal), confirmed high uric acid (1600 - 1800 kcal), acute gouty arthritis (1200 - 1400 kcal), acute gouty arthritis (1400 - 1600 kcal), and acute gouty arthritis (1600 - 1800 kcal).

[0107] Taking the recipe combination of high-risk group with 1200 - 1400 kcal as an example, the corresponding recipe template can specifically include:

[0108] 1. Lunch: Braised Beef Tendon with Carrots. Replaceable recipes are: Spicy Celery with Dried Tofu, Braised Eggplant with Potatoes, Lotus Root and Pork Ribs Soup, Tofu Soup, Peanut and Spinach Salad, Pickled Cabbage and Sliced Black Fish, Stir-Fried Purple Cabbage with Vinegar, Corn and Vegetable Soup, Scrambled Eggs with Loofah, Boiled Shrimp with Broccoli, Corn and Vegetable Soup.

[0109] 2. Low-fat pure milk. Replaceable categories are: Unsweetened Soy Milk, Unsweetened Yogurt.

[0110] 3. Mixed rice: Purple rice, multi-grain rice, purple sweet potato rice, corn rice, corn and multi-grain rice, buckwheat noodles, five-grain health rice, millet porridge, rice.

[0111] 4. Pitaya: Pomelo, orange, pear, strawberry, banana, cherry, watermelon, apple, cucumber, peach, tomato, cherry tomato, blueberry.

[0112] Traverse the recipe template library based on the recommended model, and calculate the matching degree between the target key features and each recipe template contained in the recipe template library.

[0113] In this embodiment, by using the above recommended model to traverse the recipe template library, calculate the matching degree of each recipe template contained in the recipe template library according to the above target key features. Among them, the matching degree can be calculated based on indicators such as the similarity and correlation between the target key features and the recipe template.

[0114] Select the second recipe template with the highest matching degree from all the recipe templates.

[0115] In this embodiment, the matching degrees of the generated recipe templates corresponding to the target key features can be numerically compared to select the second recipe template with the highest matching degree from all the recipe templates, so as to obtain the above target recipe template.

[0116] Use the second recipe template as the target recipe template.

[0117] In this application, the recommendation model is called, and the recipe template library is called; then, based on the recommendation model, the recipe template library is traversed to calculate the matching degree between the target key features and each recipe template included in the recipe template library; after that, the second recipe template with the highest matching degree is selected from all the recipe templates; subsequently, the second recipe template is used as the target recipe template. By traversing the recipe template library based on the use of the recommendation model, calculating the matching degree between the target key features and each recipe template included in the recipe template library, and then selecting the second recipe template with the highest matching degree from all the recipe templates as the final target recipe template, this application can accurately automatically match the most suitable target recipe template for the user from the recipe template library according to the target key features corresponding to the user, effectively improving the screening certainty of the target recipe template.

[0118] In some optional implementation manners of this embodiment, step S205 includes the following steps:

[0119] Obtain the allergic food information corresponding to the user.

[0120] In this embodiment, the allergic food information of the user can be obtained by extracting information from the above-mentioned health record data.

[0121] Determine whether the target recipe template contains the specified ingredients corresponding to the allergic food information.

[0122] In this embodiment, it can be determined whether the target recipe template contains the specified ingredients corresponding to the allergic food information by matching the above-mentioned allergic food information with the ingredients included in the above-mentioned target recipe template.

[0123] If so, screen out the ingredient-related information corresponding to the specified ingredients from the target recipe template.

[0124] In this embodiment, the above-mentioned ingredient-related information may include the specified ingredients and the cooking methods corresponding to the specified ingredients.

[0125] Perform a deletion process on the ingredient-related information in the target recipe template to obtain the third recipe template after deletion.

[0126] In this embodiment, the position information of the above-mentioned ingredient-related information in the target recipe template can be determined, and then the deletion process of the ingredient-related information in the target recipe template can be realized according to this position information, so as to obtain the third recipe template after deletion.

[0127] Use the third recipe template as the first recipe template.

[0128] This application obtains allergy food information corresponding to the user; then determines whether the target recipe template contains specified ingredients corresponding to the allergy food information; if so, screens out ingredient-related information corresponding to the specified ingredients from the target recipe template; then deletes the ingredient-related information in the target recipe template to obtain a third recipe template after deletion; subsequently, uses the third recipe template as the first recipe template. This application obtains allergy food information corresponding to the user, and when it detects that the target recipe template contains specified ingredients corresponding to the allergy food information, it will automatically screen out ingredient-related information corresponding to the specified ingredients from the target recipe template and further delete the ingredient-related information in the target recipe template, so as to achieve efficient and accurate optimization of the target recipe template, ensuring that the generated first recipe template does not contain ingredients that the user is allergic to, and improving the data accuracy of the generated first recipe template.

[0129] In some alternative implementation manners, step S207 includes the following steps:

[0130] Perform nutritional verification on the target recipe plan based on a preset ingredient knowledge base.

[0131] In this embodiment, for the specific implementation process of performing nutritional verification on the target recipe plan based on a preset ingredient knowledge base, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0132] If the target recipe plan passes the nutritional verification, obtain the category information corresponding to the target recipe template.

[0133] In this embodiment, the above category information may refer to the name information of the target recipe template. Exemplarily, if the target recipe template is a high-uric-acid recipe template, the category information corresponding to the high-uric-acid recipe template is high uric acid.

[0134] Obtain the measurement plan and suggestion information corresponding to the category information.

[0135] In this embodiment, for different categories of recipe templates, measurement plans and recommended information corresponding to the recipe templates of the category are pre-written and stored. Exemplarily, if the target recipe template is a high-uric-acid recipe template, the measurement plan corresponding to the high-uric-acid recipe template includes: 1. Blood uric acid measurement: By default, it is measured once a week; 2. Body weight measurement: By default, it is measured once a day (if the user's BMI is normal, the weight-related plan is not displayed). The recommended information corresponding to the high-uric-acid recipe template includes: 1. Regular detection: According to personal circumstances, regularly self-monitor the uric acid level. For those with stable uric acid control, it is once a week. For those with unqualified control or repeated attacks, the frequency is appropriately increased; 2. Preparation before measurement: Keep an empty stomach before measurement, avoid consuming high-purine foods and beverages that affect uric acid excretion, and at the same time reduce strenuous exercise; 3. Lifestyle adjustment: If the measurement results show abnormal uric acid levels, pay attention to diet control, reduce the intake of high-purine foods, drink more water, maintain appropriate exercise, and use medications as prescribed by a doctor if necessary.

[0136] Based on the measurement plan and the recommended information, information supplementation is performed on the target recipe plan to obtain the supplemented first target recipe plan.

[0137] In this embodiment, the first target recipe plan after supplementation can be obtained by filling the above measurement plan and recommended information into the corresponding positions in the above target recipe plan, such as the later position of the recipe plan.

[0138] Send the first target recipe plan to the user.

[0139] In this embodiment, the above first target recipe plan can be sent to the user by using push methods such as email sending, SMS sending, and system message sending.

[0140] This application performs nutritional verification on the target recipe plan based on a preset ingredient knowledge base; if the target recipe plan passes the nutritional verification, it obtains the category information corresponding to the target recipe template; then it obtains the measurement plan and recommended information corresponding to the category information; afterwards, it supplements the target recipe plan with information based on the measurement plan and the recommended information to obtain the first target recipe plan after supplementation; subsequently, it sends the first target recipe plan to the user. After sorting the first recipe template by date to obtain the corresponding target recipe plan, this application will further perform nutritional verification on the target recipe plan based on the preset ingredient knowledge base to ensure that the target recipe plan meets the set nutritional indicators. When it detects that the target recipe plan passes the nutritional verification, it will obtain the measurement plan and recommended information corresponding to the category information of the target recipe template, and supplement the target recipe plan with information based on the obtained measurement plan and recommended information to obtain the first target recipe plan after supplementation, improving the content richness of the generated first target recipe plan, and then sending the first target recipe plan to the user, which is beneficial to the intelligence of sending the recipe plan, thereby improving the user experience.

[0141] In some alternative implementation manners of this embodiment, the performing nutritional verification on the target recipe plan based on the preset ingredient knowledge base includes the following steps:

[0142] Perform calculation processing on the usage amount of each ingredient included in the target recipe plan based on the ingredient knowledge base to obtain the nutritional components of the target recipe plan.

[0143] In this embodiment, the above-mentioned ingredient knowledge base is a pre-constructed database containing the nutritional components of various ingredients, and these components include calories, protein, fat, carbohydrates, vitamins, minerals, etc. The data in the ingredient knowledge base can be sourced from reliable nutritional materials or third-party data providers. Among them, the nutritional components of the entire target recipe plan can be calculated according to the usage amount of each ingredient in the target recipe plan and the information in the ingredient knowledge base. Specifically, it can be achieved by multiplying the usage amount of each ingredient by its nutritional components, and then adding up the nutritional components of all ingredients to obtain the nutritional components of the target recipe plan.

[0144] Judge whether the nutritional components meet the preset nutritional threshold requirements.

[0145] In this embodiment, a reasonable nutritional threshold requirement can be preset for each nutritional component according to the user's input and nutritional standards.

[0146] If so, determine that the target recipe plan passes the nutritional verification; otherwise, determine that the target recipe plan fails the nutritional verification.

[0147] In this embodiment, by comparing the nutritional components of the calculated target recipe plan with the above nutritional threshold requirements, if it is detected that the nutritional components of the target recipe plan meet the nutritional threshold requirements, it is determined that the target recipe plan passes the nutritional verification. If the nutritional components of the target recipe plan do not meet the nutritional threshold requirements, it is determined that the target recipe plan fails the nutritional verification.

[0148] This application calculates and processes the usage amount of each ingredient included in the target recipe plan based on the ingredient knowledge base to obtain the nutritional components of the target recipe plan; subsequently, it determines whether the nutritional components meet the preset nutritional threshold requirements; if so, it determines that the target recipe plan passes the nutritional verification, otherwise it determines that the target recipe plan fails the nutritional verification. This application calculates and processes the usage amount of each ingredient included in the target recipe plan based on the use of the ingredient knowledge base to obtain the nutritional components of the target recipe plan, and then by comparing the obtained nutritional components with the preset nutritional threshold requirements, it can efficiently and accurately complete the nutritional verification process of the target recipe plan, effectively ensuring the accuracy of the obtained nutritional verification results corresponding to the target recipe plan.

[0149] In some alternative implementation manners, step S202 includes the following steps:

[0150] Clean the user data to obtain corresponding first user data.

[0151] In this embodiment, the above cleaning process may include missing value processing, outlier processing, and duplicate value processing. Specifically, for missing values, methods such as deletion, filling (such as using the mean, median, mode, etc.), or interpolation (such as linear interpolation, polynomial interpolation, etc.) can be used for processing. For outliers, methods such as deletion, replacement (such as using the average of adjacent values), or smoothing (such as using the moving average method) can be used for processing. For duplicate records, one can choose to delete or keep one (such as keeping the earliest or latest one).

[0152] Format the first user data to obtain corresponding second user data.

[0153] In this embodiment, the above formatting process includes: converting the fields in the data into data types suitable for subsequent model processing. Specifically, converting the date field of string type into date type, converting the rating field of numeric type into floating point type, etc.

[0154] Normalize the second user data to obtain corresponding third user data.

[0155] In this embodiment, the above normalization process includes: for numerical data (such as nutritional components like calories, protein, fat, etc.), normalization methods such as min-max normalization, Z-score standardization, etc. can be used to convert it into a value between 0 and 1. The normalized data can eliminate the dimensional differences between different features and improve the accuracy and efficiency of the algorithm.

[0156] Use the third user data as the target user data.

[0157] In this application, the user data is cleaned to obtain the corresponding first user data; then the first user data is formatted to obtain the corresponding second user data; after that, the second user data is normalized to obtain the corresponding third user data; subsequently, the third user data is used as the target user data. By cleaning, formatting, and normalizing the user data, this application can achieve comprehensive preprocessing of the user data, ensuring the quality, consistency, and processability of the generated target user data, which is beneficial for providing a solid foundation for subsequent model processing.

[0158] In some optional implementation manners of this embodiment, after step S207, the above electronic device may further perform the following steps:

[0159] Determine whether feedback information corresponding to the target recipe plan returned by the user is received.

[0160] In this embodiment, after sending the target recipe plan to the user, feedback opinions from the user on the target recipe plan can be further collected, including evaluations in aspects such as taste, satiety, and health effects.

[0161] If so, perform adjustment processing on the target recipe plan based on the feedback information to obtain an adjusted second target recipe plan.

[0162] In this embodiment, the above target recipe plan can be optimized and adjusted according to the user's feedback information to meet the user's personal needs, thereby improving user satisfaction and loyalty.

[0163] Perform storage processing on the second target recipe plan.

[0164] In this embodiment, the storage method for the second target recipe plan is not specifically limited and can be determined according to actual business requirements. For example, any one of local database storage, disk storage, cloud server storage, and blockchain storage can be used.

[0165] This application determines whether feedback information corresponding to the target recipe plan is received from the user; if so, the target recipe plan is adjusted based on the feedback information to obtain an adjusted second target recipe plan; subsequently, the second target recipe plan is stored. After receiving the feedback information corresponding to the target recipe plan from the user, this application also intelligently adjusts the target recipe plan based on the feedback information, thereby ensuring that the obtained adjusted second target recipe plan can meet the personalized needs of the user, which is conducive to providing high-quality recipe adjustment services for the user and improving the user experience. In addition, the second target recipe plan will be automatically stored, thus ensuring the data security of the second target recipe plan.

[0166] In some alternative implementation manners, the user information obtained is based on the consent of the user and complies with the relevant laws and regulations.

[0167] In addition, the non-company software tools or components appearing in the embodiments of this application are only introduced by way of example and do not represent actual use.

[0168] In addition, this application can improve the accuracy and efficiency of recipe customization. By optimizing the operation process of operators and introducing an automated configuration function, recipe templates that meet the personalized needs of users can be recommended to users more quickly and accurately. Secondly, make better use of user historical data. By deeply analyzing and integrating the user's health record data, the eating habits and changes in health needs of users can be better understood, so as to provide more personalized recipe recommendations. This helps to improve user satisfaction and loyalty. In addition, through the automated configuration function of the background system, the workload of manually adjusting the ingredient ratio and nutritional combination can also be reduced, improving work efficiency. Generally speaking, the improved solution will improve the quality and efficiency of recipe customization and promote the development of the recipe customization industry.

[0169] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0170] It should be emphasized that to further ensure the privacy and security of the above target recipe plan, the above target recipe plan can also be stored in a node of a blockchain.

[0171] The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms. Blockchain, in essence, is a decentralized database, a series of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity of the information (anti-counterfeiting) and generate the next block. The blockchain can include the blockchain underlying platform, the platform product service layer, and the application service layer, etc.

[0172] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0173] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0174] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disc, a Read-Only Memory (ROM), or a Random Access Memory (RAM), etc.

[0175] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps do not necessarily execute in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily execute at the same moment, but can execute at different moments. Their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0176] For further referenceFigure 3 , as an implementation of the method shown above Figure 2 , this application provides an embodiment of a data generation device. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0177] As Figure 3 shown, the data generation device 300 described in this embodiment includes: an acquisition module 301, a preprocessing module 302, an extraction module 303, a screening module 304, an optimization module 305, a sorting module 306, and a sending module 307. Among them:

[0178] The acquisition module 301 is used to acquire the user's data; among them, the user data includes the user's personal information, health record data, goal setting, and taste preference;

[0179] The preprocessing module 302 is used to preprocess the user data to obtain corresponding target user data;

[0180] The extraction module 303 is used to extract target key features from the target user data;

[0181] The screening module 304 is used to screen out target recipe templates that match the target key features from a preset recipe template library based on a pre-trained recommendation model;

[0182] The optimization module 305 is used to optimize the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template;

[0183] The sorting module 306 is used to sort the first recipe template by date to obtain a corresponding target recipe plan;

[0184] The sending module 307 is used to send the target recipe plan to the user.

[0185] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated here.

[0186] In some alternative implementation manners of this embodiment, the screening module 304 includes:

[0187] A calling sub-module, used to call the recommendation model and the recipe template library;

[0188] A calculation sub-module, used to traverse the recipe template library based on the recommendation model and calculate the matching degree between the target key features and each recipe template included in the recipe template library;

[0189] The first screening sub-module is used to screen out the second recipe template with the highest matching degree from all the recipe templates;

[0190] The first determination sub-module is used to use the second recipe template as the target recipe template.

[0191] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0192] In some alternative implementation manners of this embodiment, the optimization module 305 includes:

[0193] The first acquisition sub-module is used to acquire the allergic food information corresponding to the user;

[0194] The judgment sub-module is used to judge whether the target recipe template contains the specified ingredients corresponding to the allergic food information;

[0195] The second screening sub-module is used to, if so, screen out the ingredient-related information corresponding to the specified ingredients from the target recipe template;

[0196] The deletion sub-module is used to perform deletion processing on the ingredient-related information in the target recipe template to obtain the third recipe template after deletion;

[0197] The second determination sub-module is used to use the third recipe template as the first recipe template.

[0198] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0199] In some alternative implementation manners of this embodiment, the sending module 307 includes:

[0200] The verification sub-module is used to perform nutritional verification on the target recipe plan based on a preset ingredient knowledge base;

[0201] The second acquisition sub-module is used to, if the target recipe plan passes the nutritional verification, acquire the category information corresponding to the target recipe template;

[0202] The third acquisition sub-module is used to acquire the measurement plan and the recommended information corresponding to the category information;

[0203] The supplement sub-module is used to supplement the information of the target recipe plan based on the measurement plan and the recommended information to obtain the first target recipe plan after supplementation;

[0204] A sending sub-module, configured to send the first target recipe plan to the user.

[0205] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0206] In some alternative implementation manners of this embodiment, the verification sub-module includes:

[0207] A calculation unit, configured to perform calculation processing on the dosage of each ingredient included in the target recipe plan based on the ingredient knowledge base to obtain the nutritional components of the target recipe plan;

[0208] A judgment unit, configured to judge whether the nutritional components meet the preset nutritional threshold requirements;

[0209] A determination unit, configured to, if so, determine that the target recipe plan passes the nutritional verification, otherwise determine that the target recipe plan fails the nutritional verification.

[0210] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0211] In some alternative implementation manners of this embodiment, the preprocessing module 302 includes:

[0212] A first processing sub-module, configured to clean the user data to obtain corresponding first user data;

[0213] A second processing sub-module, configured to format the first user data to obtain corresponding second user data;

[0214] A third processing sub-module, configured to normalize the second user data to obtain corresponding third user data;

[0215] A third determination sub-module, configured to use the third user data as the target user data.

[0216] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0217] In some alternative implementation manners of this embodiment, the data generation device further includes:

[0218] A judgment module, configured to judge whether feedback information corresponding to the target recipe plan returned by the user is received;

[0219] An adjustment module, configured to, if so, perform an adjustment process on the target recipe plan based on the feedback information to obtain an adjusted second target recipe plan;

[0220] A storage module, configured to perform a storage process on the second target recipe plan.

[0221] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data generation method in the foregoing embodiment, and will not be elaborated herein.

[0222] To solve the above technical problems, an embodiment of the present application further provides a computer device. Specifically, please refer to Figure 4 , Figure 4 which is a basic structural block diagram of the computer device in this embodiment.

[0223] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0224] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, or the like.

[0225] The memory 41 at least includes one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc. equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and the external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the data generation method. In addition, the memory 41 may also be used to temporarily store various data that have been output or will be output.

[0226] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the data generation method.

[0227] The network interface 43 may include a wireless network interface or a wired network interface, and the network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0228] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0229] In the embodiments of the present application, the present application first obtains the user data of the user; wherein, the user data includes the personal information, health record data, goal setting, and taste preference of the user; then preprocesses the user data to obtain corresponding target user data; then extracts target key features from the target user data; subsequently, based on a pre-trained recommendation model, filters out a target recipe template that matches the target key features from a preset recipe template library; further optimizes the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template; sorts the first recipe template by date to obtain a corresponding target recipe plan; and finally sends the target recipe plan to the user. By preprocessing the obtained user data to obtain target user data, then extracting target key features from the target user data, and then based on a pre-trained recommendation model, filtering out a target recipe template that matches the target key features from a preset recipe template library, and optimizing the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template, subsequently sorting the first recipe template by date to obtain a corresponding target recipe plan, and finally sending the target recipe plan to the user, in this way, through the present application, based on the use of the recommendation model and the recipe template library, it is possible to automatically and accurately construct a target recipe plan that corresponds to the user's user data and meets the user's personalized needs, effectively improving the processing efficiency and accuracy of recipe generation.

[0230] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium, the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to execute the steps of the data generation method as described above.

[0231] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0232] In the embodiments of the present application, the present application first obtains the user data of the user; wherein, the user data includes the personal information, health record data, goal setting, and taste preference of the user; then preprocesses the user data to obtain corresponding target user data; then extracts target key features from the target user data; subsequently, based on a pre-trained recommendation model, filters out a target recipe template that matches the target key features from a preset recipe template library; further optimizes the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template; sorts the first recipe template by date to obtain a corresponding target recipe plan; and finally sends the target recipe plan to the user. By preprocessing the obtained user data to obtain target user data, then extracting target key features from the target user data, and then based on a pre-trained recommendation model, filtering out a target recipe template that matches the target key features from a preset recipe template library, and optimizing the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template, subsequently sorting the first recipe template by date to obtain a corresponding target recipe plan, and finally sending the target recipe plan to the user, thus, through the present application, based on the use of the recommendation model and the recipe template library, it is possible to automatically and accurately construct a target recipe plan that meets the personalized needs of the user corresponding to the user data of the user, effectively improving the processing efficiency and accuracy of recipe generation.

[0233] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0234] Obviously, the embodiments described above are only a part of the embodiments of this application, rather than all the embodiments. The preferred embodiments of this application are given in the accompanying drawings, but they do not limit the patent scope of this application. This application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of this application more thorough and comprehensive. Although this application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structures made by using the content of this application's specification and drawings, directly or indirectly applied in other related technical fields, are equally within the scope of patent protection of this application.

Claims

1. A data generation method, characterized in that: The steps include: Obtaining user data of the user; wherein the user data includes the user's personal information, health record data, goal setting, and taste preferences; Preprocessing the user data to obtain corresponding target user data; Extracting target key features from the target user data; Based on the pre-trained recommendation model, a target recipe template matching the target key feature is selected from a preset recipe template library; Optimizing the target recipe template based on the user's food allergy information to obtain a corresponding first recipe template; Sort the first recipe templates by date to obtain corresponding target recipe plans; The target recipe plan is sent to the user.

2. The data generation method according to claim 1, characterized in that: The step of selecting a target recipe template matching the target key feature from a preset recipe template library based on the pre-trained recommendation model specifically includes: Calling the recommendation model and calling the recipe template library; Traversing the recipe template library based on the recommendation model, and calculating the matching degree between the target key feature and each recipe template included in the recipe template library; Filter out a second recipe template with the highest matching degree from all the recipe templates; The second recipe template is used as the target recipe template.

3. The data generation method according to claim 1, characterized in that: The step of optimizing the target recipe template based on the user's allergic food information to obtain a corresponding first recipe template specifically includes: Acquire food allergy information corresponding to the user; Determining whether the target recipe template contains designated ingredients corresponding to the allergic food information; If so, filtering out the ingredient-related information corresponding to the designated ingredient from the target recipe template; Deleting the ingredient related information in the target recipe template to obtain a deleted third recipe template; The third recipe template is used as the first recipe template.

4. The data generation method according to claim 1, characterized in that: The step of sending the target recipe plan to the user specifically includes: Performing nutrition verification on the target recipe plan based on a preset food material knowledge base; If the target recipe plan passes the nutrition verification, obtaining category information corresponding to the target recipe template; Obtaining measurement solutions and suggestion information corresponding to the category information; Supplementing the target recipe plan with information based on the measurement scheme and the suggestion information to obtain a supplemented first target recipe plan; The first target recipe plan is sent to the user.

5. The data generation method according to claim 4, characterized in that: The step of performing nutrition verification on the target recipe plan based on the preset food material knowledge base specifically includes: Calculating the amount of each ingredient included in the target recipe plan based on the ingredient knowledge base to obtain the nutritional components of the target recipe plan; Determining whether the nutritional components meet preset nutritional threshold requirements; If so, it is determined that the target recipe plan has passed the nutrition verification, otherwise it is determined that the target recipe plan has not passed the nutrition verification.

6. The data generation method according to claim 1, characterized in that: The step of preprocessing the user data to obtain corresponding target user data specifically includes: Cleaning the user data to obtain corresponding first user data; Formatting the first user data to obtain corresponding second user data; Normalizing the second user data to obtain corresponding third user data; The third user data is used as the target user data.

7. The data generation method according to claim 1, characterized in that: After the step of sending the target recipe plan to the user, the method further includes: Determining whether feedback information corresponding to the target recipe plan returned by the user is received; If yes, adjusting the target recipe plan based on the feedback information to obtain an adjusted second target recipe plan; The second target recipe plan is stored.

8. A data generating device, characterized in that: include: An acquisition module, used to acquire user data of a user; wherein the user data includes the user's personal information, health record data, goal setting and taste preference; A preprocessing module, used to preprocess the user data to obtain corresponding target user data; An extraction module, used to extract target key features from the target user data; A screening module, used for screening out a target recipe template matching the target key feature from a preset recipe template library based on a pre-trained recommendation model; an optimization module, configured to optimize the target recipe template based on the user's food allergy information to obtain a corresponding first recipe template; A sorting module, used to sort the first recipe templates by date to obtain corresponding target recipe plans; A sending module is used to send the target recipe plan to the user.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the data generating method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the data generation method according to any one of claims 1 to 7 are implemented.

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