Wireless weighing record-based daily diet nutrition analysis method

Abstract

According to the daily diet nutrition analysis method based on the wireless weighing record, the intelligent nutrition scale can collect the food weight and conduct peeling calculation, and the net diet weight is accurately obtained. Through peeling calculation, weight interference of non-food parts such as containers can be eliminated, it is ensured that data uploaded to a cloud analysis background is the real food weight, and a foundation is laid for follow-up accurate nutrition analysis. The cloud analysis background can record the data in time and bind the data with the user ID, so that the diet data of each user can be independently stored and managed, and subsequent personalized nutrition analysis and health management for the user are facilitated. After the cloud analysis background records the data, a nutrition analysis notice or a food type selection input notice is sent to the user terminal in time. The timely notification mechanism can improve the participation degree and the response speed of the user, and ensures that the whole nutrition analysis process is efficiently carried out. The user can quickly log in the cloud analysis background to perform subsequent operation according to the notification, and waiting time is shortened.

Description

Technical Field

[0001] This invention relates to the field of dietary health analysis technology, and in particular to a method for daily dietary nutrition analysis based on wireless weighing records, as well as a device, electronic device, and computer-readable storage medium for daily dietary nutrition analysis based on wireless weighing records. Background Technology

[0002] Existing dietary nutrition analysis methods mainly rely on users manually inputting food types and weights, or on image recognition of food photos. These methods have the following problems:

[0003] Manual input is inefficient and error-prone: users need to manually input the type and weight of food, which is not only time-consuming, but may also lead to inaccurate analysis results due to input errors.

[0004] Insufficient image recognition accuracy: If relying solely on image recognition technology (such as traditional two-dimensional convolutional neural networks), recognition errors may occur due to image quality, shooting angle, or the complexity of food types.

[0005] Lack of real-time performance and automation: Existing methods typically cannot automate the entire process from data acquisition to analysis, requiring users to participate in multiple steps, which increases the user burden.

[0006] Limited data analysis capabilities: Existing solutions typically only provide nutritional analysis of a single meal, lacking the integration of historical data and personalized recommendation functions, thus failing to meet the needs of long-term health management. Summary of the Invention

[0007] To address the technical problems existing in the prior art, the present invention provides the following technical solution:

[0008] On the one hand, a method for daily dietary nutrition analysis based on wireless weighing records is provided. This method is implemented by an electronic device and includes:

[0009] S1. Using a smart nutrition scale, the weight of the food consumed in this meal is collected and tare is calculated to obtain the corresponding net weight of the meal, which is then uploaded to the cloud analysis backend.

[0010] S2. The cloud-based analysis backend records the net weight of the diet and binds it to the user ID, while simultaneously sending a nutrition analysis notification to the user's terminal.

[0011] S3. Respond to the notification through the user terminal, log in to the cloud analysis backend and input the types of food to be analyzed for nutrition.

[0012] S4. The cloud-based analysis backend (analysis engine) calculates the nutritional composition of each food type in this diet based on the net weight of the diet and the selected food types, using a nutritional database.

[0013] S5. The cloud-based analysis backend records the analysis results of this diet, generates a corresponding diet report, and sends it to the user terminal.

[0014] Preferably, the method further includes:

[0015] S101. Using a smart nutrition scale, collect images of the food consumed during this meal and upload them to the cloud-based analysis backend.

[0016] Preferably, the method further includes:

[0017] S201. The cloud-based analysis backend records the food images of this meal and binds them to the user ID. At the same time, it identifies the food types in the food images based on a 3D-CNN model and generates a corresponding food list. The food list of this meal is sent to the user terminal, and a food type selection input notification is sent to the user terminal.

[0018] Preferably, the method further includes:

[0019] S301. The user terminal responds to the notification, logs into the cloud analysis backend, selects the types of food to be analyzed for nutrition from the food list, and sends the selection results back to the cloud analysis backend.

[0020] Preferably, the nutrition database stores the unit nutrient composition of different food types: kcal / 100g; and includes at least the following nutrients:

[0021] carbohydrate;

[0022] protein;

[0023] or

[0024] Fat.

[0025] Preferably, the cloud-based analysis backend records the analysis results of this diet and generates a corresponding diet report, including:

[0026] Record the net weight of the food consumed, the nutritional composition of each food item, and the timestamp for this meal.

[0027] The data is aggregated daily, weekly, and monthly to show the types of food consumed and the total nutritional value, and the percentage of each nutrient consumed is calculated.

[0028] Based on statistical analysis, a corresponding dietary report is generated and sent to the user terminal.

[0029] Preferably, the method further includes:

[0030] The analysis results of each user's diet are stored, and the user's historical diet data is generated and saved on the cloud analysis backend.

[0031] Based on the RF model, the user's historical diet data is trained and learned to construct a recipe recommendation model, which is then deployed on the cloud-based analysis backend.

[0032] Based on the recipe recommendation model, the analysis results of this diet are analyzed to monitor and determine whether there is a nutritional deficiency:

[0033] If so, a food recommendation list for the corresponding nutritional gap is generated based on the recipe library, and the list is pushed to the user terminal;

[0034] If not, continue monitoring.

[0035] On the other hand, a daily dietary nutrition analysis device based on wireless weighing records is provided. This device is used to implement the aforementioned daily dietary nutrition analysis method based on wireless weighing records. The device includes:

[0036] The smart nutrition scale is used to collect the weight of the food in this meal, tare it, calculate the corresponding net weight of the meal, and upload it to the cloud analysis backend.

[0037] The cloud-based analytics backend records the net weight of the diet and binds it to the user ID, while simultaneously sending a nutrition analysis notification to the user's terminal.

[0038] The user terminal is used to respond to notifications, log in to the cloud-based analysis backend, and input the types of food to be analyzed for nutrition.

[0039] The cloud-based analysis backend is also used to calculate the nutritional composition of each food type in the current diet based on the net weight of the diet and the selected food types, using a nutrition database; and to record the analysis results of the current diet, generate a corresponding diet report, and send it to the user terminal.

[0040] The smart nutrition scale and the user terminal are respectively connected to the cloud-based analysis backend.

[0041] On the other hand, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods described above for daily dietary nutrition analysis based on wireless weighing records.

[0042] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods for daily dietary nutrition analysis based on wireless weighing records.

[0043] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0044] 1. Intelligent analysis capabilities

[0045] The precise weighing of smart nutrition scales: Smart nutrition scales can collect the weight of food and perform tare calculations to accurately determine the net weight of the diet. Tare calculations eliminate the interference of weight from non-food parts such as containers, ensuring that the data uploaded to the cloud analysis backend represents the true weight of the food, laying the foundation for subsequent accurate nutritional analysis.

[0046] Image Acquisition and Intelligent Recognition: Food images are captured by a smart nutrition scale and uploaded to a cloud-based analysis platform. The cloud platform uses a 3D-CNN model to identify the food types in the images and generate a food list. The 3D-CNN model has powerful image recognition capabilities, able to analyze images from multiple angles and dimensions to accurately identify various foods.

[0047] 2. Highly efficient data transmission and recording

[0048] Rapid data upload and binding: Both food weight data and food image data can be quickly uploaded to the cloud analytics backend. The cloud analytics backend can record this data in a timely manner and bind it to the user ID. This binding method allows each user's dietary data to be stored and managed independently, facilitating subsequent personalized nutritional analysis and health management for that user.

[0049] Timely notifications and interactions: After recording data, the cloud-based analytics backend promptly sends nutritional analysis notifications or food type selection input notifications to the user's terminal. This timely notification mechanism improves user engagement and response speed, ensuring the entire nutritional analysis process is efficient. Users can quickly log in to the cloud-based analytics backend to perform subsequent operations based on the notifications, reducing waiting time.

[0050] 3. Nutritional analysis capabilities

[0051] Precise calculations based on a database: The cloud-based analytics engine calculates the nutritional composition of each food item in the meal based on the net weight and the types of food selected by the user, using a nutritional database. This database contains a wealth of food nutrition information, which the analytics engine can leverage for precise calculations. For example, for a serving of rice, it can accurately calculate the content of carbohydrates, protein, fat, and other nutrients based on the rice's weight and the nutritional information in the database.

[0052] Personalized Diet Report Generation: The cloud-based analytics backend records the results of this dietary analysis and generates a corresponding diet report, which is then sent to the user's device. The diet report is generated based on the user's specific dietary situation and is personalized. The report can list detailed information such as the nutritional components of various foods, the total nutrient intake of this diet, and a comparison with the user's nutritional needs, providing the user with comprehensive dietary guidance.

[0053] 4. Flexible user interaction experience

[0054] Diverse food selection options: Users can either manually input the types of food to be analyzed or select from a food list generated by the cloud-based analysis backend. This diverse selection method meets the needs of different users and improves user convenience. For example, users familiar with food names can input them directly, while users unsure of the food names or who wish to select quickly can choose from the list.

[0055] Convenient terminal response operation: Users can easily log in to the cloud-based analytics backend to perform operations via terminal response notifications. The entire process is simple and quick, requiring no complicated steps, lowering the user threshold and improving the user experience. Users can easily complete operations such as selecting food types and viewing dietary reports on terminal devices such as mobile phones and tablets. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of a daily dietary nutrition analysis method based on wireless weighing records provided by an embodiment of the present invention;

[0058] Figure 2 This is a block diagram of a daily dietary nutrition analysis device based on wireless weighing records provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of a mechanism for preferentially recommending foods based on nutritional gaps, provided by an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0062] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0063] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0064] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0065] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0066] The smart nutrition scale in this embodiment can be upgraded from an existing electronic scale. Its basic hardware system, such as power supply and controller, will not be described in detail here. The user terminal can be an APP or a mini-program, which can communicate with the cloud server (cloud analysis backend).

[0067] like Figure 2 As shown, this invention provides a daily dietary nutrition analysis device based on wireless weighing records. This device is used to implement a daily dietary nutrition analysis method based on wireless weighing records. The device includes a smart nutrition scale, a cloud-based analysis backend, and a user terminal. The smart nutrition scale and the user terminal are respectively communicatively connected to the cloud-based analysis backend. The functions and interactions of each component are as follows:

[0068] The smart nutrition scale consists of an MCU, Bluetooth, a processor, a camera, and a weighing sensor. When a user places food on the scale, it can peel, weigh, and photograph the food. After pre-processing by the processor, the data is uploaded to a cloud-based analysis platform via Bluetooth. The specific hardware components are not limited here; for example:

[0069] MCU (Microcontroller Unit): STMicroelectronics' STM32F103C8T6 is selected. It is based on the ARM Cortex-M3 core, has a 72MHz operating frequency, 64KB of flash memory and 20KB of SRAM. It features rich peripheral interfaces, such as multiple UART, SPI, and I2C interfaces, enabling easy communication with Bluetooth modules, sensors, etc.

[0070] Bluetooth: Select Nordic Semiconductor's nRF52832. It is a low-power Bluetooth chip with an integrated ARM Cortex-M4F processor, supporting the Bluetooth 5.0 protocol. It features excellent RF performance and low power consumption, allowing for extended operation in battery-powered devices.

[0071] Processor: Rockchip's RK3399. It is a high-performance 64-bit processor with two Cortex-A72 big cores and four Cortex-A53 little cores, using the big.LITTLE architecture. It integrates a high-performance GPU, providing strong graphics processing capabilities and supporting 4K video decoding and output.

[0072] Camera: Hikvision DS-2CD2T47G0-L. It features 2 megapixels, supports 1080P HD video recording, day / night switching, and wide dynamic range. Utilizing an advanced image sensor and ISP technology, it delivers clear images and high color fidelity.

[0073] Weighing sensor: The KELI D12 digital weighing sensor is selected. It features digital output, strong anti-interference capability, and high accuracy. It has a wide measuring range, allowing selection of a suitable range based on actual needs. Suitable for smart scales, smart kitchen scales, and other similar devices, it accurately measures the weight of objects and transmits the data to the MCU for processing.

[0074] The cloud-based analytics backend consists of a data receiving API, a nutrition database, data storage (caching), an analysis engine, and a recipe recommendation model. It records and analyzes food intake, and based on food analysis information from the user's terminal, it jointly analyzes the nutritional composition of each food type in the current meal. It can also analyze whether the current meal is lacking in any nutrients based on the user's historical food records; if so, it will proactively recommend corresponding foods and push them to the user. Specifically, the cloud-based analytics backend's main function is to comprehensively record and deeply analyze the user's food intake. The data receiving API receives food analysis information from the user's terminal; the nutrition database stores detailed nutritional data for various foods, providing a foundation for subsequent analysis; the data storage (caching) temporarily stores received data and intermediate results during the analysis process, ensuring data integrity and traceability; the analysis engine is the core component, combining the received food analysis information with data from the nutrition database to accurately analyze the nutritional composition of each food type in the current meal, and also determining whether there are any nutritional deficiencies based on the user's historical food records; the recipe recommendation model plays a role when the analysis engine determines nutritional deficiencies. Based on the missing nutrients, it will select suitable foods from the nutrition database and generate corresponding recipe recommendations. Finally, it will push these recommendations to the user's terminal through the system to provide the user with scientific and reasonable dietary advice.

[0075] Users log in through their user terminals to view data and execute recommendations.

[0076] The overall principle is based on a wireless (Bluetooth mode) electronic scale (nutrition scale) and an analysis backend. The wireless (Bluetooth mode) electronic scale can weigh and tare everyday food and upload the data to the backend system. The backend system can record this dietary record and automatically analyze the type of food selected by the user (the backend has a food database that records the calorie value of different types of food: g / kcal) and its nutritional content such as carbohydrates, protein, and fat. Each meal can be recorded and a dietary report can be generated. The system can also recommend corresponding nutritional diet lists based on the recipe plan and push the reports to the user's mobile app or mini-program.

[0077] The implementation principles and technical effects of this solution will be further described below in conjunction with the following analytical methods and steps.

[0078] This invention provides a method for daily dietary nutrition analysis based on wireless weighing records. This method can be implemented using an electronic device, which can be a terminal or a server. Figure 1 The flowchart shown illustrates a method for analyzing daily dietary nutrition based on wireless weighing records. This method's processing flow may include the following steps:

[0079] S1. The smart nutrition scale (composed of MCU, Bluetooth, processor, camera and weighing sensor) collects the weight of the food in this meal and calculates the tare weight, and uploads the corresponding net weight of the meal to the cloud analysis backend.

[0080] S2. The cloud-based analysis backend records the net weight of the diet and binds it to the user ID, while simultaneously sending a nutrition analysis notification to the user's terminal.

[0081] S3. Respond to the notification through the user terminal, log in to the cloud analysis backend and input the types of food to be analyzed for nutrition.

[0082] S4. The cloud-based analysis backend (analysis engine) calculates the nutritional composition of each food type in this diet based on the net weight of the diet and the selected food types, using a nutritional database.

[0083] S5. The cloud-based analysis backend records the analysis results of this diet, generates a corresponding diet report, and sends it to the user terminal.

[0084] The following section will describe each step in detail, based on the working principle of the aforementioned device.

[0085] S1: Food weight data collection and uploading

[0086] Turn on the smart nutrition scale and place the food for this meal on the scale. The weighing sensor inside the smart nutrition scale senses the weight of the food. The MCU performs preliminary processing on the collected weight data, removing the weight of non-food parts such as the weighing pan (tare calculation) to obtain the net weight of the meal. The processor integrates and encapsulates the net weight data and transmits it to a paired device (such as a mobile phone) via Bluetooth. The device then uploads the data to the cloud-based analysis backend.

[0087] S2: Data logging and notification sending

[0088] After receiving the net weight data, the cloud-based analytics backend binds it to the user ID and stores it in the database. Simultaneously, the cloud-based analytics backend generates a nutrition analysis notification and sends it to the user's terminal (e.g., a mobile app) via a network (e.g., HTTP protocol).

[0089] S3: User Response and Food Type Input

[0090] Operation: After receiving the notification, the user opens the relevant application on their terminal, responds to the notification, and logs into the cloud-based analysis backend. In the logged-in interface, the user manually enters the types of food to be analyzed nutritionally. Encryption technology can be used to ensure login security.

[0091] S4: Nutritional Calculation

[0092] After receiving the net weight of the meal and the food types entered by the user, the analysis engine in the cloud-based analytics backend retrieves the nutritional composition data for the corresponding food types from the nutrition database. Based on the net weight of the meal and the data in the nutrition database, it calculates the nutritional composition of each food type in this meal according to certain calculation rules (such as the nutrient content per gram of food multiplied by the net weight of the meal).

[0093] S5: Recording and Distributing Analysis Results and Reports

[0094] The cloud-based analytics backend records the nutritional composition calculations for this meal in its database and generates a corresponding dietary report (such as a PDF or HTML report containing information on food types, nutrient content, and recommended intake ranges) based on the recorded data. The dietary report is then distributed to the user's terminal via the network. Encrypted transmission can be used during the distribution process.

[0095] The weighing sensor in this smart nutrition scale is based on the principle of pressure sensing. When food is placed on the weighing pan, the pan deforms under pressure, and the weighing sensor converts this deformation into an electrical signal. The MCU performs analog-to-digital conversion and preliminary processing on the electrical signal, removing noise and interference. The processor is responsible for data integration and transmission control, and the Bluetooth module enables wireless communication with external devices. The camera is based on the principle of optical imaging. Through the lens, it focuses the light from the food onto the image sensor, which converts the light signal into an electrical signal, which is then processed to obtain an image of the food.

[0096] The cloud-based analytics backend primarily consists of servers and an analytics engine. The servers are responsible for receiving and storing data from the smart nutrition scale and user terminals, and managing user account information and the database. The analytics engine processes dietary net weight and food type information based on algorithms, extracts data from the nutrition database, and performs calculations.

[0097] User terminals (such as mobile apps) are primarily responsible for interacting with users, receiving notifications and data from the cloud analytics backend, and providing functions such as user login, food type input, and selection. Data is transmitted between the user and the cloud analytics backend via the network to ensure smooth information flow between the user and the cloud.

[0098] Therefore, based on the above method and steps, the following technical advantages can be achieved:

[0099] The smart nutrition scale's high-precision weighing sensor and tare calculation function can accurately obtain the net weight of food. Users only need to use the smart nutrition scale to complete the acquisition of food weight and images, without the need for additional equipment.

[0100] The cloud-based analytics platform automatically processes data and generates dietary reports. Users can easily access the analysis results via a mobile app, without any complicated operations. Dietary data is linked to user IDs, allowing the cloud-based analytics platform to create personalized dietary profiles for each user and provide more accurate nutritional advice based on their historical dietary data.

[0101] The data collected by the smart nutrition scale can be uploaded to the cloud analysis backend in real time, and users can receive timely nutrition analysis notifications and diet reports, making it easier to adjust their diet accordingly.

[0102] Preferably, the method further includes:

[0103] S101. Collect food images of this meal using the smart nutrition scale (with its camera) and upload them to the cloud-based analysis backend;

[0104] S201. The cloud-based analysis backend records the food images of this meal and binds them to the user ID. At the same time, it identifies the food types in the food images based on a 3D-CNN model and generates a corresponding food list. The food list of this meal is sent to the user terminal, and a food type selection input notification is sent to the user terminal.

[0105] S301. The user terminal responds to the notification, logs into the cloud analysis backend, selects the types of food to be analyzed for nutrition from the food list, and sends the selection results back to the cloud analysis backend.

[0106] While food is placed on the smart nutrition scale, the scale's camera captures images of the food. The processor compresses and converts the captured images (e.g., to JPEG format), transmits the image data via Bluetooth to a paired device, and then the device uploads the image data to a cloud-based analysis backend. Upon receiving the food image, the cloud analysis backend binds it to the user's ID and stores it in a database. Simultaneously, a 3D-CNN model processes the food image, identifies the food types in the image, and generates a corresponding food list. The food list for this meal is sent to the user's terminal, along with a notification to select the food type. The 3D-CNN model is a deep learning model that learns from a large number of food images to identify food types. It consists of multiple convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract image features, pooling layers reduce the dimensionality of feature maps, and fully connected layers classify the features. The 3D-CNN model is trained on a large amount of food image data to improve recognition accuracy. After receiving the notification and food list, the user opens the relevant application on their terminal, responds to the notification, and logs into the cloud analysis backend. Select the food categories to be analyzed from the food list, and the results will be sent to the cloud-based analysis backend. For user convenience and faster operation, interactive methods such as checkboxes can be used.

[0107] 3D-CNN models improve the accuracy of food type recognition by enhancing the identification of food images and reducing errors from manual input by users. The nutrition database can be continuously updated and expanded to include more food types and more detailed nutritional information. 3D-CNN models can further improve the accuracy and range of food type recognition through continuous training and optimization.

[0108] Based on the above solution, this embodiment provides the following implementation scenario:

[0109] User Xiao Zhang used a smart nutrition scale to weigh and image his food for breakfast. He placed a bowl of oatmeal, a boiled egg, and a glass of milk on the scale. The steps were as follows:

[0110] 1. Data Acquisition Phase

[0111] The smart nutrition scale's weighing sensor collects the total weight of the oatmeal, boiled egg, and milk. The MCU then performs tare calculations to obtain the net weight of the food. Simultaneously, the camera captures images of the food. The net weight data and image data are transmitted to Xiao Zhang's mobile phone via Bluetooth.

[0112] 2. Cloud processing stage

[0113] The cloud-based analytics backend records the net weight of the food and images of the food, and binds them to Xiao Zhang's user ID.

[0114] The 3D-CNN model identifies food images and generates a list of foods including oatmeal, boiled eggs, and milk.

[0115] The cloud analytics backend sends a notification and a food list to Xiao Zhang's phone, prompting him to select the food type.

[0116] 3. User Interaction Stage

[0117] After receiving the notification on his phone, Xiao Zhang opened the app and logged into the cloud analytics backend.

[0118] Xiao Zhang selected oatmeal porridge, boiled eggs, and milk from the food list as the food types to be analyzed, and then sent the selection results to the cloud analysis backend.

[0119] 4. Nutritional Analysis Stage

[0120] The cloud-based analytics engine retrieves nutritional data for oatmeal, boiled eggs, and milk from a nutrition database based on the net weight of the meal and the types of food chosen, calculating the nutritional composition of each item for this breakfast. The cloud-based analytics then generates a dietary report containing the nutritional information and sends it to Xiao Zhang's mobile phone.

[0121] Preferably, the nutrition database stores the unit nutrient composition of different food types: kcal / 100g; and includes at least the following nutrients:

[0122] carbohydrate;

[0123] protein;

[0124] or

[0125] Fat.

[0126] The nutrition database employs a relational database architecture, which is suitable for storing structured data and facilitates efficient querying, insertion, updating, and deletion of food nutrition information. The database primarily consists of a core food nutrition information table, and may also include index tables to improve query efficiency.

[0127] Food nutrition information sheets mainly record the following information:

[0128] Food Category Column: This column uniquely identifies different foods and is stored as text, such as "apple," "beef," and "spinach." This column is the key identifier for each record, allowing users to perform precise searches based on food names.

[0129] kcal / 100g column: This column stores the calories per 100g of each food item. It uses a floating-point number to ensure accurate recording of calorie values. For example, the calories of an apple may be recorded as 53.0 kcal / 100g.

[0130] Carbohydrate column: This column records the carbohydrate content per 100 grams of food, also stored as a floating-point number. For example, 100 grams of apples contain 13.81 grams of carbohydrates.

[0131] Protein column: Used to store the protein content per 100 grams of food. The data type is floating point, such as beef containing 20.2 grams of protein per 100 grams.

[0132] Fat column: Records the fat content per 100 grams of food, using a floating-point number, for example, olive oil contains 99.9 grams of fat per 100 grams.

[0133] To improve query efficiency, an index is created for the food category column. The purpose of an index is to speed up the data retrieval process. When a user queries by food name, the database can directly locate the corresponding record through the index without traversing the entire food nutrition information table, thus greatly reducing query time.

[0134] Data entry

[0135] Data collection: Professional nutritionists or researchers use scientific experiments and analytical methods to determine the nutrient content of different foods and obtain specific values ​​for kcal, carbohydrates, protein, and fat per 100 grams of food.

[0136] Data processing: The collected data is processed in a standardized format to ensure its accuracy and consistency.

[0137] Data Insertion: Using the insert statements provided by the database management system, insert the prepared data row by row into the food nutrition information table. During the insertion process, the database will automatically check the data format to ensure that the data conforms to the table structure definition.

[0138] Data Query

[0139] Users initiate query requests: Users can enter the name of the food they want to query through applications or database management tools.

[0140] Database query processing: After receiving a query request, the database first searches the index table for an index entry that matches the food name entered by the user. If a matching index entry is found, the database directly locates the corresponding record in the food nutrition information table based on the address provided by the index entry.

[0141] Returning query results: The database extracts the content information of kcal / 100g, carbohydrates, protein, and fat from the queried records and returns it to the user.

[0142] Using the delete statement provided by the database management system, locate the record to be deleted based on the food name, and then remove that record from the food nutrition information table. After the deletion operation is complete, the database will automatically release the storage space occupied by the record.

[0143] Preferably, the cloud-based analysis backend records the analysis results of this diet and generates a corresponding diet report, including:

[0144] Record the net weight of the food consumed, the nutritional composition of each food item, and the timestamp for this meal.

[0145] The data is aggregated daily, weekly, and monthly to show the types of food consumed and the total nutritional value, and the percentage of each nutrient consumed is calculated.

[0146] Based on statistical analysis, a corresponding dietary report is generated and sent to the user terminal.

[0147] 1. Data Recording

[0148] Food Net Weight Recording: The cloud-based analytics backend connects to the user's interactive device (such as a mobile phone with a relevant food recording app installed). When recording food intake, users can manually input the net weight or use image recognition technology to automatically identify the food's net weight. This is because accurate food net weight is the basis for subsequent nutritional component calculations, as different weights of the same food contain different amounts of nutrients. For example, 100 grams of apples and 200 grams of apples will have significantly different contents of nutrients such as vitamin C and sugar.

[0149] Nutritional Information Recording: The system has a built-in rich nutritional database. After a user records the type of food, the system extracts information on various nutrients, such as the content of protein, fat, carbohydrates, vitamins, and minerals, from the database based on the food's name and net weight. Simultaneously, it records the timestamp of the food intake, which helps in subsequent data aggregation according to different time dimensions.

[0150] 2. Data aggregation and statistics

[0151] The system aggregates food types and total nutritional value by time: Based on recorded timestamps, it categorizes and summarizes the food consumed by the user according to daily, weekly, and monthly time dimensions. For each day, the system counts all types of food consumed that day and sums up the nutritional content of each food to obtain the total nutritional value for that day. Similarly, a similar aggregation operation is performed for a week and a month. For example, when calculating the total nutritional value for a week, the system adds up the intake of nutrients such as protein, fat, and carbohydrates each day of that week.

[0152] Calculating the percentage of nutrient intake: After obtaining the total nutritional value over different time periods, the system calculates the percentage of each nutrient intake based on the daily reference values ​​for human nutrition. For example, an adult male needs approximately 65 grams of protein per day. If a user consumes 30 grams of protein on a particular day, then the percentage of protein intake for that day would be 30 ÷ 65 × 100%. In this way, users can intuitively understand whether their intake of various nutrients at different times is reasonable.

[0153] 3. Report generation and distribution

[0154] Report Generation: Based on the preceding data statistical analysis, the system will generate a dietary report according to a preset template. The report will include information such as the types of food consumed by the user over different time periods, the total nutritional value, and the intake percentage of each nutrient. Additionally, it may provide dietary suggestions based on the analysis results, such as how to adjust the diet if a certain nutrient is consumed in excess or deficiency.

[0155] Report Distribution: The generated dietary report is distributed to user terminals via network transmission. Users can view the report on their mobile phones, computers, and other devices to understand their dietary situation.

[0156] Implementation across different time dimensions

[0157] Weekly Dimension: At the end of the week, the cloud-based analytics platform will aggregate the daily dietary data to generate a weekly dietary report. The report will show the types of food the user consumed during the week, the total weekly nutritional value, the percentage of each nutrient intake, and a comparison with daily nutritional reference values. For example, if the user's vitamin C intake was consistently insufficient throughout the week, the report will remind them to eat more fruits and vegetables rich in vitamin C.

[0158] Monthly dimension: Similarly, at the end of the month, a monthly diet report will be generated to give users a more comprehensive understanding of their diet during the month.

[0159] Therefore, the cloud-based analytics backend has a rich and accurate database of food nutrients, capable of precisely calculating the content of various nutrients based on the types and weights of food recorded by the user. Simultaneously, through timestamp recording, it can accurately aggregate and statistically analyze data according to different time dimensions, providing users with detailed and accurate dietary information.

[0160] Based on the statistical analysis of users' dietary data, the system can provide personalized dietary advice. Different users may have different physical conditions and nutritional needs. The system can use information such as age, gender, height, and weight, combined with dietary data, to provide dietary adjustment suggestions that are more tailored to the user's actual situation, helping users improve their dietary structure and maintain healthy eating habits.

[0161] Users can record their dietary information anytime, anywhere using mobile devices. The cloud-based analytics platform processes this data in real time and generates reports that are promptly sent to the user's device. Users can quickly understand their dietary habits without the need for complex manual calculations and statistics, making it convenient and efficient. The cloud-based analytics platform employs advanced security technology to encrypt and store user dietary data during transmission, ensuring data security and privacy. Only the user can view their dietary report on their own device using their account, preventing data leaks.

[0162] Preferably, the method further includes:

[0163] The analysis results of each user's diet are stored, and the user's historical diet data is generated and saved on the cloud analysis backend.

[0164] Based on the RF model, the user's historical diet data is trained and learned to construct a recipe recommendation model, which is then deployed on the cloud-based analysis backend.

[0165] Based on the recipe recommendation model, the analysis results of this diet are analyzed to monitor and determine whether there is a nutritional deficiency:

[0166] If so, a food recommendation list for the corresponding nutritional gap is generated based on the recipe library, and the list is pushed to the user terminal;

[0167] If not, continue monitoring.

[0168] The model training steps are as follows:

[0169] 1. Data Collection and Preprocessing: Collect data on each user's diet, which may include information such as food type, intake, and nutritional composition. Clean the collected data to remove errors, duplicates, or incomplete data; perform normalization to unify data from different ranges to the same scale to improve model training performance.

[0170] 2. Divide the data into training and validation sets: Divide the processed user historical diet data into a certain proportion (e.g., 80% training set, 20% validation set). The training set is used for model parameter learning, and the validation set is used to evaluate the model's performance on unseen data to prevent overfitting.

[0171] 3. Model Initialization: Initialize the RF (Random Forest) model by setting its hyperparameters, such as the number of trees, maximum depth, and minimum number of splits. These hyperparameters affect the model's performance and complexity and need to be adjusted according to the specific circumstances.

[0172] 4. Model Training: The RF model is trained using the training set. The model learns the features and patterns in the user's historical dietary data, constructing a decision tree forest. During training, the model continuously adjusts the parameters of the decision trees to minimize prediction error.

[0173] 5. Model Evaluation and Tuning: Evaluate the trained model using a validation set and calculate evaluation metrics (such as accuracy, recall, F1 score, etc.). Based on the evaluation results, adjust the model's hyperparameters and repeat the training and evaluation process until the model performance reaches a satisfactory level.

[0174] 6. Model Saving and Deployment: Save the trained recipe recommendation model to the cloud analysis backend and deploy it for subsequent recipe recommendations.

[0175] Suppose we have dietary data from 1000 users, each with 100 dietary records. After cleaning and normalizing this data, it is divided into training and validation sets at a ratio of 80% to 20%. When initializing the RF model, the number of trees is set to 100, the maximum depth to 10, and the minimum number of splits to 5. The model is trained using the training set, and after multiple iterations, the model achieves an accuracy of 85%. The trained model is then saved to a cloud-based analytics platform and deployed.

[0176] Because the Random Forest (RF) model integrates multiple decision trees, it can effectively handle high-dimensional data and complex nonlinear relationships, exhibiting strong generalization ability and achieving good performance on diverse datasets. During training, the RF model employs random sampling and feature selection, reducing model variance and mitigating the risk of overfitting. Decision trees are highly interpretable models; each decision tree in the RF model can provide explanatory information, aiding in understanding the model's decision-making process.

[0177] The training process of RF models can be parallelized, making full use of the computing resources of multi-core processors and improving training speed.

[0178] 7. Model Validation Steps

[0179] Data preparation: Prepare a set of validation data (validation set) that was not used in model training to ensure the quality and integrity of the data.

[0180] Model loading: Load the trained recipe recommendation model from the cloud analytics backend.

[0181] Prediction and Evaluation: Use validation data to make predictions for the model, compare the prediction results with the true labels, and calculate evaluation metrics (such as accuracy, recall, F1 score, etc.).

[0182] Results Analysis: Analyze the evaluation results to check for overfitting or underfitting issues in the model. If the model's performance on the validation set is significantly lower than that on the training set, overfitting may be present; if the model's performance is poor on both the validation and training sets, underfitting may be present.

[0183] Model tuning: Based on the results analysis, adjust the model's hyperparameters or increase training data, and retrain and validate until the model performance reaches a satisfactory level.

[0184] The trained recipe recommendation model was validated using the previously allocated 20% validation data. After loading the model, predictions were made on the validation data, yielding an accuracy of 82%, a recall of 80%, and an F1 score of 81%. Analysis of the evaluation results showed that the model's performance generally met the requirements, but there was still room for improvement. It is advisable to try increasing the training data or adjusting the model's hyperparameters, and then retraining and validating.

[0185] The validation process assesses a model's performance on unseen data, ensuring good generalization ability and reliability. It helps identify overfitting or underfitting issues, allowing for timely adjustments and optimizations to improve performance. Validation results provide a basis for hyperparameter tuning and feature engineering, guiding further model optimization.

[0186] 8. Model Application Steps

[0187] Data Acquisition: Acquire the analysis results of the user's current diet and send them to the cloud-based analysis backend;

[0188] Model prediction: The cloud-based analytics backend loads the recipe recommendation model to predict the results of the user's current diet and determine whether there is a nutritional deficiency.

[0189] Nutritional Deficiency Assessment: Based on the model's predictions, determine if the user has a nutritional deficiency. If the predictions indicate a nutritional deficiency, proceed to the next step; otherwise, continue monitoring the user's diet.

[0190] Food recommendation list generation: If a nutritional gap exists, a priority recommendation list of foods corresponding to the nutritional gap is generated based on the recipe library; the recipe library contains nutritional information and cooking methods for various foods, which can be filtered and sorted according to the user's nutritional needs and taste preferences;

[0191] Recommendation list push: The generated food priority recommendation list is pushed to the user's terminal for the user's reference;

[0192] Continuous monitoring: Continue to monitor the user's diet, repeat the above steps, and provide the user with ongoing nutritional advice and recipe recommendations.

[0193] For example, a user records their dietary information for a particular day, including bread, milk, and eggs for breakfast, and rice, chicken, and vegetables for lunch. After the cloud-based analytics team receives this information, they use a recipe recommendation model to predict whether the user is deficient in Vitamin C. Based on the recipe database, a priority list of Vitamin C-rich foods, such as oranges, strawberries, and kiwis, is generated and pushed to the user's mobile device. The user can then choose suitable foods from the recommendations.

[0194] Therefore, the model can provide personalized recipe recommendations based on the user's historical dietary data and the analysis results of the current diet, meeting the user's nutritional needs and taste preferences. By continuously monitoring the user's dietary situation, the model can promptly identify nutritional deficiencies and provide corresponding food recommendations to help the user maintain healthy eating habits. Pushing the food recommendation list to the user's device makes it convenient for the user to obtain nutritional advice and recipe information, improving the user experience.

[0195] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 4 As shown, optionally, electronic device 410 may include a first processor 2001.

[0196] Optionally, the electronic device 410 may also include a memory 2002 and a transceiver 2003.

[0197] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0198] The following is combined Figure 4 A detailed description of each component of electronic device 410 is provided below:

[0199] The first processor 2001 is the control center of the electronic device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0200] Optionally, the first processor 2001 can perform various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0201] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0202] In a specific implementation, as one example, the electronic device 410 may also include multiple processors, for example... Figure 4 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0203] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0204] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be connected via the interface circuit of the electronic device 410. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0205] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0206] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0207] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected via the interface circuit of the electronic device 410. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0208] It should be noted that, Figure 4 The structure of the electronic device 410 shown does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0209] Furthermore, the technical effects of the electronic device 410 can be referred to the technical effects of the daily dietary nutrition analysis method based on wireless weighing records described in the above method embodiments, and will not be repeated here.

[0210] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0211] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0212] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. 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 from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0213] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0214] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0215] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0216] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0217] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0218] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0219] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0220] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0221] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0222] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A daily dietary nutrition analysis method based on wireless weighing records, characterized by, The method comprises: S1, through the intelligent nutrition scale, the food weight of this diet is collected and the skin is calculated to obtain the corresponding diet net weight uploaded to the cloud analysis background; S2, the cloud analysis background records the diet net weight and binds with the user ID, and sends a nutrition analysis notification to the user terminal at the same time; S3, through the user terminal responding to the notification, logging in to the cloud analysis background and inputting the food type to be analyzed for nutrition; S4, the cloud analysis background calculates the nutritional ingredients of each food type of this diet based on the nutrition database according to the diet net weight and the selected food type of this diet; S5, the cloud analysis background records the analysis results of this diet, generates the corresponding diet report and sends it to the user terminal.

2. The daily diet nutrition analysis method based on wireless weighing record according to claim 1, characterized in that, The method further comprises: S101, through the intelligent nutrition scale, the food image of this diet is collected and uploaded to the cloud analysis background.

3. The daily diet nutrition analysis method based on wireless weighing record according to claim 2, characterized in that, The method further comprises: S201, the cloud analysis background records the food image of this diet and binds with the user ID, and at the same time, based on the 3D-CNN model, identifies the food type in the food image and generates the corresponding food list, sends the food list of this diet to the user terminal, and sends the food type selection input notification to the user terminal at the same time.

4. The daily diet nutrition analysis method based on wireless weighing record according to claim 3, characterized in that, The method further comprises: S301, through the user terminal responding to the notification, logging in to the cloud analysis background and selecting the food type to be analyzed for nutrition from the food list, and feeding back the selection results to the cloud analysis background.

5. The daily diet nutrition analysis method based on wireless weighing record according to claim 1, wherein, The nutrition database stores the unit nutritional ingredients of different food types: kilocalories / 100g; and at least contains the following nutritional ingredients: Carbohydrates; Protein; Or Fat.

6. The daily diet nutrition analysis method based on wireless weighing record according to claim 5, wherein, The cloud analysis background records the analysis results of this diet, generates the corresponding diet report, which includes: Record the diet net weight, nutritional ingredients of each food type and time stamp of this diet; According to the day / week / month aggregation of the user's intake food type and nutritional total value, the intake proportion of each nutritional ingredient is counted; Based on statistical analysis, the corresponding diet report is generated and sent to the user terminal.

7. The daily diet nutrition analysis method based on wireless weighing record according to claim 6, characterized in that, The method further comprises: Data storage of the analysis results of each diet of the user, generation of the user's historical diet data and saving on the cloud analysis background; Based on the RF model, the user's historical diet data is trained and learned to build a recipe recommendation model and deploy it on the cloud analysis background; Based on the recipe recommendation model, the analysis results of this diet are analyzed to monitor and judge whether there is a nutritional gap: If yes, generate a food priority recommendation list for the corresponding nutritional gap based on the recipe library, and push the list to the user terminal; If not, continue to monitor.

8. A wireless weighing record based daily dietary nutrition analysis device for implementing the wireless weighing record based daily dietary nutrition analysis method according to any one of claims 1-7, characterized in that, The device comprises: Intelligent nutrition scale for collecting food weight of this diet and skin calculation to obtain corresponding diet net weight uploaded to the cloud analysis background; Cloud analysis background for recording the diet net weight and binding with the user ID, and sending a nutrition analysis notification to the user terminal at the same time; User terminal for responding to the notification, logging in to the cloud analysis background and inputting the food type to be analyzed for nutrition; The cloud analysis background is further configured to calculate the nutritional components of each food category of the diet based on a nutrition database according to the net weight of the diet and the selected food category of the diet, record the analysis result of the diet, generate a corresponding diet report, and send the diet report to the user terminal. The intelligent nutrition scale and the user terminal are respectively in communication connection with the cloud analysis background.

9. An electronic device, comprising: The electronic device comprises: a processor; a memory having computer readable instructions stored thereon, the computer readable instructions being executed by the processor to implement the method of any one of claims 1 to 7.

10. A computer readable storage medium, characterized in that, The computer readable storage medium has program code stored therein, and the program code can be called and executed by the processor to implement the method of any one of claims 1 to 7.

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