Household catering calorie and blood sugar monitoring method and system based on digital information technology and storage medium

Abstract

The invention relates to the technical field of intelligent health monitoring, in particular to a household catering calorie and blood sugar monitoring method and system based on a digital information technology and a storage medium. The method comprises the following steps: acquiring home catering parameters, wherein the home catering parameters are used for indicating characteristics of materials used in a home catering making process; according to the home catering parameters, a calorie calculation result and / or a blood glucose measurement result of the made dish are / is determined through a preset target calculation model, the target calculation model is used for performing calorie calculation and / or blood glucose measurement on the dish, and the calorie calculation result is used for indicating the calorie of the dish; the blood glucose measurement result is used for indicating the nutritional ingredient index influencing the blood glucose level in the dish. According to the embodiment of the invention, by calculating and monitoring catering calorie and blood sugar influences, the method can provide relevant information of calorie and blood sugar for the user in time, and help the user to adjust diet and living habits in time, so that a better blood sugar control effect is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent health monitoring technology, and in particular to a method, system and storage medium for monitoring calories and blood sugar in home dining based on digital information technology. Background Art

[0002] As the prevalence of diabetes continues to rise worldwide, personalized blood glucose monitoring has become a key component in diabetes management. Diabetes is a chronic metabolic disease characterized by abnormally elevated blood glucose levels.

[0003] It is particularly important to provide personalized blood sugar index monitoring. Diet is one of the important factors affecting blood sugar levels. It has become an urgent problem to accurately calculate and monitor the calories of meals and convey calorie and blood sugar information to users. Although related technologies have made certain progress in blood sugar monitoring, they often rely on frequent blood sampling and equipment calibration, which not only brings inconvenience to patients' daily management, but also fails to provide real-time monitoring of the intake of nutrients in the diet. Summary of the invention

[0004] In view of this, the present disclosure proposes a method, system and storage medium for monitoring calories and blood sugar in home dining based on digital information technology.

[0005] According to one aspect of the present disclosure, a method for monitoring calories and blood sugar in home dining based on digital information technology is provided, the method comprising:

[0006] Acquire home catering parameters, where the home catering parameters are used to indicate the characteristics of materials used in the home catering production process;

[0007] According to the home dining parameters, the calorie calculation result and / or the blood sugar measurement result of the prepared dish is determined by a preset target calculation model. The target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish. The calorie calculation result is used to indicate the calories of the dish, and the blood sugar measurement result is used to indicate the nutritional component index in the dish that affects the blood sugar level.

[0008] In a possible implementation, the home dining parameters include raw material parameters and processed material parameters, the raw material parameters include the type and physical properties of the raw materials, the processed material parameters include the type and physical properties of the processed materials, and the physical properties include mass or volume.

[0009] In another possible implementation, the home catering parameters also include processing flow parameters and / or dish parameters, wherein the processing flow parameters are used to indicate the processing flow steps of the dish during the home catering production process, and the dish parameters include the type and / or physical properties of the dish.

[0010] In another possible implementation, the obtaining of home dining parameters includes at least one of the following methods:

[0011] Acquire an image of the raw material, and determine the raw material parameters according to the image of the raw material by using a preset first recognition model, where the first recognition model is used to recognize the raw material parameters in the image;

[0012] An image of the processed material is acquired, and according to the image of the processed material, parameters of the processed material are determined by a preset second recognition model, where the second recognition model is used to recognize the parameters of the processed material in the image.

[0013] In another possible implementation, the obtaining of home dining parameters further includes at least one of the following methods:

[0014] Acquire a video of a processing stage in the home-cooked food preparation process, and determine the processing flow parameters by using a preset third recognition model based on the video of the processing stage, wherein the third recognition model is used to recognize the processing flow parameters in the video;

[0015] An image of the dish is acquired, and based on the image of the dish, the dish parameters are determined by a preset fourth recognition model, where the fourth recognition model is used to recognize the dish parameters in the image.

[0016] In another possible implementation, the calorie calculation result of the prepared dish is determined by a preset target calculation model according to the home dining parameter, including:

[0017] According to the home dining parameters and the information in the database, determining the calorie calculation result of the dish through the target calculation model;

[0018] The information in the database includes the unit heat of each of the various raw materials and the unit heat of each of the various processed materials, and the unit heat is the heat contained in the unit physical property.

[0019] In another possible implementation, the target calculation model includes a first calculation model, a second calculation model, a third calculation model, and a fourth calculation model, the dish includes one or more servings, the calorie calculation result of the dish includes the total calorie of the dish and / or the calorie of a single serving, and determining the calorie calculation result of the dish by the target calculation model according to the home dining parameter and the information in the database includes:

[0020] According to the physical properties and unit heat of each of the n raw materials used, the total raw material heat of the n raw materials is determined by the first calculation model, and according to the physical properties and unit heat of each of the m processing materials used, the total processing material heat of the m processing materials is determined by the second calculation model, wherein both n and m are positive integers;

[0021] Determining the total calories of the dish by the third calculation model according to the total calories of the raw materials, the total calories of the processed materials and the cooking method coefficient, wherein the cooking method coefficient is preset or determined according to processing flow parameters;

[0022] The calories of the single serving of dish are determined by the fourth calculation model based on the total calories of the dish, the physical properties of the dish and the physical properties of the single serving of dish.

[0023] In another possible implementation, the method further includes:

[0024] Acquiring personal information parameters, wherein the personal information parameters include physiological parameters and blood sugar information of the user;

[0025] According to the personal information parameters, estimating the ideal index of the nutritional components required by the user;

[0026] Comparative analysis is performed on the ideal index of the nutritional components and the blood sugar measurement result of the dish;

[0027] Based on the result of the comparative analysis, feedback is determined, and the feedback is used to instruct to provide personalized dietary advice to the user.

[0028] According to another aspect of the present disclosure, a home dining calorie and blood sugar monitoring device based on digital information technology is provided, the device comprising:

[0029] A data acquisition unit, used to obtain home catering parameters, wherein the home catering parameters are used to indicate the characteristics of materials used in the home catering production process;

[0030] A digital algorithm unit is used to determine the calorie calculation result and / or blood sugar measurement result of the prepared dish according to the home dining parameters through a preset target calculation model, the target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish, the calorie calculation result is used to indicate the calorie of the dish, and the blood sugar measurement result is used to indicate the nutrient component index in the dish that affects the blood sugar level.

[0031] In a possible implementation, the home dining parameters include raw material parameters and processed material parameters, the raw material parameters include the type and physical properties of the raw materials, the processed material parameters include the type and physical properties of the processed materials, and the physical properties include mass or volume.

[0032] In another possible implementation, the home catering parameters also include processing flow parameters and / or dish parameters, wherein the processing flow parameters are used to indicate the processing flow steps of the dish during the home catering production process, and the dish parameters include the type and / or physical properties of the dish.

[0033] In another possible implementation, the data acquisition unit is further configured to execute at least one of the following methods:

[0034] Acquire an image of the raw material, and determine the raw material parameters according to the image of the raw material by using a preset first recognition model, where the first recognition model is used to recognize the raw material parameters in the image;

[0035] An image of the processed material is acquired, and according to the image of the processed material, parameters of the processed material are determined by a preset second recognition model, where the second recognition model is used to recognize the parameters of the processed material in the image.

[0036] In another possible implementation, the data acquisition unit is further configured to execute at least one of the following methods:

[0037] Acquire a video of a processing stage in the home-cooked food preparation process, and determine the processing flow parameters by using a preset third recognition model based on the video of the processing stage, wherein the third recognition model is used to recognize the processing flow parameters in the video;

[0038] An image of the dish is acquired, and based on the image of the dish, the dish parameters are determined by a preset fourth recognition model, where the fourth recognition model is used to recognize the dish parameters in the image.

[0039] In another possible implementation, the digital arithmetic unit is further used for:

[0040] Determining the calorie calculation result of the dish through the target calculation model according to the home dining parameters and the information in the database;

[0041] The information in the database includes the unit heat of each of the various raw materials and the unit heat of each of the various processed materials, and the unit heat is the heat contained in the unit physical property.

[0042] In another possible implementation, the target calculation model includes a first calculation model, a second calculation model, a third calculation model, and a fourth calculation model, the dish includes one or more servings, the calorie calculation result of the dish includes the total calorie of the dish and / or the calorie of a single serving, and the digital algorithm unit is further used to:

[0043] According to the physical properties and unit heat of each of the n raw materials used, the total raw material heat of the n raw materials is determined by the first calculation model, and according to the physical properties and unit heat of each of the m processing materials used, the total processing material heat of the m processing materials is determined by the second calculation model, wherein both n and m are positive integers;

[0044] Determining the total calories of the dish by the third calculation model according to the total calories of the raw materials, the total calories of the processed materials and the cooking method coefficient, wherein the cooking method coefficient is preset or determined according to processing flow parameters;

[0045] The calories of the single serving of dish are determined by the fourth calculation model based on the total calories of the dish, the physical properties of the dish and the physical properties of the single serving of dish.

[0046] In another possible implementation, the device further includes: an information feedback unit, configured to:

[0047] Acquiring personal information parameters, wherein the personal information parameters include physiological parameters and blood sugar information of the user;

[0048] According to the personal information parameters, estimating the ideal index of the nutritional components required by the user;

[0049] Comparative analysis is performed on the ideal index of the nutritional components and the blood sugar measurement result of the dish;

[0050] Based on the result of the comparative analysis, feedback is determined, and the feedback is used to instruct to provide personalized dietary advice to the user.

[0051] According to another aspect of the present disclosure, a home dining calorie and blood sugar monitoring system based on digital information technology is provided, the system comprising:

[0052] An image acquisition device, used to acquire images during the home catering production process;

[0053] A processing device, for determining home catering parameters according to the collected images, the home catering parameters being used to indicate the characteristics of materials used in the home catering production process; determining a calorie calculation result and / or a blood glucose measurement result of a prepared dish according to the home catering parameters through a preset target calculation model, the target calculation model being used to perform calorie calculation and / or blood glucose measurement on the dish, the calorie calculation result being used to indicate the calorie of the dish, and the blood glucose measurement result being used to indicate an index of nutrient components in the dish that affect blood glucose levels; and determining feedback according to the calorie calculation result and / or the blood glucose measurement result;

[0054] The feedback device is used to output the feedback, and the feedback is used to indicate providing personalized dietary advice to the user.

[0055] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0056] According to another aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of a computing device, the processor in the computing device executes the above method.

[0057] The disclosed embodiments provide a method for monitoring calories and blood sugar in home catering based on digital information technology, by acquiring home catering parameters, which are used to indicate the material characteristics used in the home catering production process, and according to the home catering parameters, the calorie calculation result and / or blood sugar measurement result of the prepared dish are determined by a preset target calculation model, the target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish, the calorie calculation result is used to indicate the calories of the dish, and the blood sugar measurement result is used to indicate the nutrient component index in the dish that affects the blood sugar level; that is, by acquiring the home catering parameters in the home catering production process, combined with the preset target calculation model, to calculate and monitor the calorie and blood sugar effects of the catering, the method can instantly provide users with relevant information on calories and blood sugar, and help users adjust their diet and living habits in time to achieve better blood sugar control effects.

[0058] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0060] Figure 1 A schematic structural diagram of a home dining calorie and blood sugar monitoring system based on digital information technology provided by an exemplary embodiment of the present disclosure is shown.

[0061] Figure 2 A flow chart of a method for monitoring calories and blood sugar in home dining based on digital information technology provided by an exemplary embodiment of the present disclosure is shown.

[0062] Figure 3 A flow chart of a method for monitoring calories and blood sugar in home dining based on digital information technology provided by another exemplary embodiment of the present disclosure is shown.

[0063] Figure 4 A schematic diagram of the principles of a method for monitoring calories and blood sugar in home dining based on digital information technology provided by an exemplary embodiment of the present disclosure is shown.

[0064] Figure 5 It is a block diagram of a device according to an exemplary embodiment. DETAILED DESCRIPTION

[0065] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0066] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0067] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0068] First, the application scenarios involved in the embodiments of the present disclosure are introduced. Figure 1 , which shows a structural schematic diagram of a home dining calorie and blood sugar monitoring system based on digital information technology provided by an exemplary embodiment of the present disclosure.

[0069] The system may include three main devices: an image acquisition device 11, a processing device 12 and a feedback device 13. These three devices are introduced below respectively.

[0070] Image acquisition device 11: used to collect images during the home catering production process to provide visual data for subsequent parameter identification and analysis.

[0071] Processing device 12: As the core of the system, it is used to determine various parameters related to home catering, namely home catering parameters, based on the images captured by the image acquisition device 11. Home catering parameters may include: 1. Raw material parameters: refers to the basic parameters of the ingredients, which may include the type and physical properties of the raw materials, and the physical properties may include mass or volume. 2. Processing material parameters: refers to the parameters of processing materials such as seasonings and oils added during the cooking process, which may include the type and physical properties of the processing materials, and the physical properties may include mass or volume. 3. Dish parameters: refers to the parameters of the final dish, which may include the type and / or physical properties of the dish. 4. Processing flow parameters: used to indicate the processing flow steps of the dish.

[0072] The processing device 12 further processes the home dining parameters, and calculates the calories of the dishes and the possible impact on blood sugar through a large database and a target calculation model. The processing device 12 may include: 1. A large database: storing information such as unit calories of various raw materials and processed materials. 2. A target calculation model: calculating the calorie calculation results and / or blood sugar measurement results of the dishes based on the home dining parameters and the information in the database.

[0073] The processing device 12 is also used to: determine feedback based on the calorie calculation results and / or blood sugar measurement results. The processing device 12 may include: 1. Personality indicator monitoring: monitoring and analyzing the user's dietary calories and blood sugar impact based on the user's health data and eating habits. 2. Suggestion feedback: based on the calorie calculation results and / or blood sugar measurement results, providing users with personalized dietary suggestions and feedback to help users better manage blood sugar and calorie intake.

[0074] Feedback device 13: used to output the feedback determined by processing device 12, and the feedback is used to indicate providing personalized dietary advice to the user. That is, feedback device 13 is responsible for presenting the feedback in a user-friendly manner to ensure that the user can receive clear and accurate personalized dietary advice. Feedback device 13 may include user interface design, notification system or other interactive methods to facilitate user access to information.

[0075] The entire system is a closed-loop system, from data acquisition to processing, and then to feedback, forming a complete monitoring and management process. According to the specific application scenarios of the system and user needs, these three devices can be expanded or reduced accordingly to meet specific functional requirements. For example, the function of determining home dining parameters based on the collected images can also be replaced and integrated into the image acquisition device 11. While collecting images, image analysis and parameter identification can be performed instantly, thereby reducing data transmission and processing delays. For example, the function of determining feedback based on calorie calculation results and blood sugar measurement results can also be replaced and integrated into the feedback device 13, which can make the feedback more direct and personalized. Such a system can help users better understand the impact of their diet on blood sugar, and adjust their eating habits accordingly to achieve healthier blood sugar control.

[0076] It should be noted that the implementation methods of the system hardware design may include the following: 1. Integrated hardware design: The above three devices are integrated into a computing device, such as a smart phone or a dedicated kitchen device, the image acquisition device 11 is a camera in the computing device, the processing device 12 is a processor in the computing device, and the feedback device 13 is a display screen in the computing device. 2. Modular hardware design: The system consists of multiple independent modules, each module is responsible for one or more units, and can be combined or upgraded as needed. 3. Distributed hardware design: The hardware is distributed in different physical locations, such as the image acquisition device 11 in the kitchen, while the processing device 12 and the feedback device 13 are on a remote server. 4. Combination of cloud and edge computing: The edge device (such as a terminal device) processes real-time data, while the cloud server processes non-real-time, large-scale data analysis. 5. Multi-device collaboration: Different devices work together, such as a camera as an image acquisition device 11, a server as a processing device 12, and a smart phone as a feedback device 13. These hardware design implementation methods can be selected and combined according to the specific needs, cost budget, user scenarios and performance requirements of the system, and the embodiments of the present disclosure are not limited to this.

[0077] Below, several exemplary embodiments are used to introduce the home dining calorie and blood sugar monitoring method based on digital information technology provided by the embodiments of the present disclosure.

[0078] Please refer to Figure 2 , which shows a flowchart of a method for monitoring calories and blood sugar in home dining based on digital information technology provided by an exemplary embodiment of the present disclosure. This embodiment uses the method for Figure 1 The method comprises the following steps.

[0079] Step 201, obtaining home catering parameters, where the home catering parameters are used to indicate the characteristics of materials used in the home catering production process.

[0080] Home catering parameters refer to the detailed characteristics of various materials and processes used in preparing catering in a home environment, and are used to indicate the characteristics of materials used in the home catering preparation process.

[0081] Home catering parameters include raw material parameters and processing material parameters. Raw materials refer to the basic materials used in the production process of home catering. Raw material parameters include the type of raw materials (such as meat, vegetables, grains, etc.) and physical properties (such as quality, volume, etc.). These parameters can be obtained through image recognition technology and classified and identified through pre-trained neural networks.

[0082] Processing materials refer to auxiliary materials added during the home catering production process, such as seasonings, oils, etc., which are used to enhance flavor, improve texture or complete specific processing steps. Processing material parameters also include the type and physical properties of processing materials (such as mass, volume, etc.), which can also be identified and quantified through image recognition technology combined with pre-trained neural networks.

[0083] In some embodiments, the home catering parameters also include processing flow parameters and / or dish parameters. The processing flow parameters are used to indicate the processing flow steps of dishes in the home catering production process, such as cutting, frying, boiling, baking, etc., which can be obtained through video stream recognition, mainly using frame generation technology and convolutional neural network for action recognition. Dish parameters include the type and / or physical properties (such as quality, volume, etc.) of the finished dish, which can be obtained by capturing video frames at the last turning point of the processing flow and using similar raw material recognition methods to identify and analyze the finished dish.

[0084] In the implementation of the step of acquiring home dining parameters, the cooking process can be monitored in real time by a camera, and the home dining parameters in the cooking process can be automatically recorded and analyzed in real time by combining image recognition and video analysis technology. It should be noted that the method of acquiring home dining parameters can refer to the relevant description in the following embodiment, which will not be introduced here.

[0085] Step 202, based on the home dining parameters, the calorie calculation result and / or blood sugar measurement result of the prepared dish is determined through a preset target calculation model. The target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish. The calorie calculation result is used to indicate the calories of the dish, and the blood sugar measurement result is used to indicate the nutritional component index in the dish that affects the blood sugar level.

[0086] In some embodiments, the system inputs the home dining parameters into a preset target calculation model in real time, or inputs the home dining parameters after the dish is prepared, and the system finally outputs the calorie calculation results and / or blood glucose measurement results of the prepared dish. The target calculation model is a preset mathematical model used to calculate the calorie calculation results and / or blood glucose measurement results of the dish based on the home dining parameters. This model can be used to analyze raw material parameters and processing material parameters, and can be further analyzed in combination with other parameters (such as processing flow parameters and / or dish parameters) to determine the calories of the final dish and / or the impact of the dish on blood glucose levels. The target calculation model can be trained based on the home dining parameter samples, and the calorie calculation result labels and / or blood glucose measurement result labels corresponding to the samples, based on the training method in the prior art.

[0087] It should be noted that the application process of the target computing model can refer to the relevant description in the following embodiments and will not be introduced here.

[0088] The calorie calculation result refers to the quantitative value of the energy contained in the dish obtained by the preset target calculation model, usually expressed in kilocalories (kcal) or joules (J). This result helps users understand the energy content of the dish, which is very important for controlling weight and maintaining a healthy diet. In some embodiments, the dish includes one or more servings, and the calorie calculation result includes the calories of the dish obtained by the target calculation model, which can be the total calories of the dish and / or the calories of a single serving of the dish.

[0089] In some embodiments, the blood sugar measurement result includes a nutrient index in the dish that affects blood sugar levels, which is usually related to the glycemic index (GI) of the dish, and is used to indicate the potential impact of the dish on blood sugar levels. The nutrient index refers to the intake of various nutrients (such as protein, fat and carbohydrates) ingested through diet. Understanding the nutrient index is very important for maintaining health and preventing nutrition-related diseases.

[0090] In some embodiments, the system can feed back the calorie calculation results and / or blood sugar measurement results to the user. The system can also provide personalized dietary suggestions to the user through algorithm analysis based on the calorie calculation results and / or blood sugar measurement results of the dishes (and can also be combined with the user's health data and eating habits), such as adjusting the proportion of ingredients, cooking methods or food choices to better manage blood sugar and calorie intake.

[0091] In summary, the embodiments of the present disclosure provide a method for monitoring calories and blood sugar in home catering based on digital information technology. Home catering parameters are obtained, which are used to indicate the material characteristics used in the home catering production process. According to the home catering parameters, the calorie calculation results and / or blood sugar measurement results of the prepared dishes are determined by a preset target calculation model. The target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dishes. The calorie calculation results are used to indicate the calories of the dishes, and the blood sugar measurement results are used to indicate the nutritional component indicators in the dishes that affect the blood sugar level. That is, by real-time calculation and monitoring of catering calories and blood sugar effects, the method can instantly provide users with calorie and blood sugar related information to help users adjust their diet and lifestyle habits in a timely manner to achieve better blood sugar control effects.

[0092] In some embodiments, the system uses advanced image acquisition and video recording technology combined with neural network algorithms to automatically collect relevant material parameters in the home catering production process. In other words, the system can use the camera to achieve image acquisition and video recording, and use pre-trained neural networks for classification and recognition to obtain the specific parameters of raw materials, processing materials, processing procedures and finished dishes. Calorie and blood sugar monitoring is performed based on the collected relevant material parameters in the home catering production process. Please refer to Figure 3 , which shows a flowchart of a method for monitoring calories and blood sugar in home dining based on digital information technology provided by another exemplary embodiment of the present disclosure. This embodiment uses the method for Figure 1 The method comprises the following steps.

[0093] Step 301, acquiring an image of a raw material, and determining raw material parameters based on the image of the raw material through a preset first recognition model, where the first recognition model is used to recognize the raw material parameters in the image.

[0094] In the raw material preparation stage of the home catering production process, an image of the raw materials is captured by real-time or timed photography, and the image includes the raw materials. The image of the raw materials is input into the first recognition model, and the raw material parameters are output, and the raw material parameters include the type and physical properties of the raw materials, and the physical properties include mass or volume.

[0095] The first recognition model is a preset neural network model for identifying raw material parameters from the image of the raw material. The first recognition model can be trained based on the image samples of the raw materials and the raw material parameter labels corresponding to the image samples based on the training method in the prior art. Schematically, the first recognition model is a convolutional neural network (CNN) model. This is not limited in the embodiments of the present disclosure.

[0096] In some embodiments, the process of collecting images of raw materials and determining their parameters includes but is not limited to the following steps: 1. Image acquisition and recognition: Use a high-definition camera to capture images of raw materials, which will be used as input information. Classification and recognition are performed through a pre-trained first recognition model, and matching is performed with a preset database to determine the type of raw materials. 2. Three-dimensional information acquisition: Use a binocular three-dimensional imaging algorithm, such as the NeRF algorithm, or a monocular three-dimensional reconstruction algorithm based on monocular image depth estimation, such as the Metric3D algorithm, to obtain three-dimensional information of raw materials. These algorithms can output three-dimensional information such as voxels, point clouds, and grids of raw materials. 3. Smoothing and volume calculation: The three-dimensional information of raw materials is smoothed by interpolation to improve the continuity and accuracy of the data. Sum or integrate in space to calculate the mass or volume of the raw materials, thereby obtaining the output of the type and physical properties (including mass or volume) of the raw materials.

[0097] Step 302, acquiring an image of the processed material, and determining the processing material parameters according to the image of the processed material through a preset second recognition model, where the second recognition model is used to recognize the processing material parameters in the image.

[0098] In the process of preparing or processing materials in the home catering production process, an image of the processed materials is captured by real-time or timed photography, and the image includes the processed materials. The image of the processed materials is input into the second recognition model, and the processed material parameters are output, and the processed material parameters include the type and physical properties of the processed materials, and the physical properties include mass or volume.

[0099] The second recognition model is a preset neural network model for identifying the processing material parameters from the image of the processing material. The second recognition model can be trained based on the image samples of the processing material and the processing material parameter labels corresponding to the image samples based on the training method in the prior art. Schematically, the second recognition model is a CNN model. The embodiments of the present disclosure are not limited to this.

[0100] It should be noted that the process of collecting images of processed materials and determining their parameters can be analogously referred to the above-mentioned image recognition process of raw materials, and will not be repeated here.

[0101] Step 303, obtaining a video of the processing stage during the home catering production process, and determining the processing flow parameters based on the video of the processing stage through a preset third recognition model, where the third recognition model is used to recognize the processing flow parameters in the video.

[0102] During the processing stage of the home catering production process, a video of the processing stage is collected through real-time or timed photography, and the video records the dynamic changes of the entire processing stage. The video of the processing stage is input into the third recognition model, and the processing flow parameters are output. The processing flow parameters are used to indicate one or more processing flow steps (such as steaming, stir-frying) of the dish during the home catering production process. The processing flow parameters can also indicate the duration of each processing flow step.

[0103] The third recognition model is a preset neural network model, which is used to identify the processing parameters from the video of the processing stage. The third recognition model can be trained based on the video samples of the processing stage and the processing parameter labels corresponding to the video samples, based on the training method in the prior art. Schematically, the third recognition model is a CNN model, for example, the third recognition model is a faster region convolutional neural network ("Faster Region-based Convolutional Neural Network, Faster R-CNN) model. The embodiments of the present disclosure are not limited to this.

[0104] In some embodiments, the process of collecting videos of the processing stage and determining the processing flow parameters includes but is not limited to the following steps: 1. Video stream collection: First, obtain the video of the processing stage in the home catering production process. 2. Frame generation technology: Frame generation technology is used to convert the continuous video stream into a series of static image sequences, which allows each frame of the image to be analyzed in detail. 3. Application of neural network: The cooking action corresponding to each frame of the image is located and identified through the third recognition model. This model has been specially trained to recognize different cooking actions, such as stir-frying, boiling, etc. 4. Action recognition and time marking: The cooking action corresponding to each frame of the image is identified through the third recognition model, and the start and end time of these actions are recorded to provide accurate time marking for each processing flow step. 5. Turning time detection: Further analyze the action recognition results, detect the turning time points between different processing flow steps, and obtain detailed information on the processing flow steps and their duration.

[0105] Step 304, obtaining an image of the dish, and determining dish parameters based on the image of the dish through a preset fourth recognition model, where the fourth recognition model is used to recognize the dish parameters in the image.

[0106] When the home dining is completed, especially at the final turning point of the processing flow, the image of the dish can be captured by real-time or timed photography technology. This process can use a camera to extract key frames from the captured video stream, and the image of the dish includes the finished dish. The image includes the finished dish. The image of the dish is input into the fourth recognition model, and the dish parameters are output, which include the type and / or physical properties of the dish, and the physical properties include mass or volume.

[0107] The fourth recognition model is a preset neural network model, which is used to identify dish parameters from the image of the dish. The fourth recognition model can be trained based on the image samples of the dish and the dish labels corresponding to the image samples based on the training method in the prior art. Schematically, the fourth recognition model is a CNN model. The embodiments of the present disclosure are not limited to this.

[0108] It should be noted that the process of collecting images of dishes and determining dish parameters can be analogously referred to the above-mentioned image recognition process of raw materials, and will not be repeated here.

[0109] Step 305, according to the raw material parameters, processing material parameters, processing flow parameters and dish parameters, the calorie calculation result and blood sugar measurement result of the prepared dish are determined through a preset target calculation model.

[0110] The system inputs raw material parameters, processing material parameters, processing flow parameters and dish parameters into the target calculation model, and outputs the calorie calculation results and blood sugar measurement results of the finished dish.

[0111] Among them, raw material parameters and processing material parameters are required input data, while processing process parameters and dish parameters are optional input data to meet different calculation needs and accuracy requirements.

[0112] In some embodiments, based on home dining parameters, relevant data is retrieved through a large database, and the calorie calculation results and blood sugar measurement results of the dishes are determined in combination with a built-in algorithm.

[0113] Taking calorie calculation as an example, the system can determine the calorie calculation results of dishes through the target calculation model based on home dining parameters and information in the database; the information in the database includes the unit calorie of each of the various raw materials and the unit calorie of each of the various processed materials. The unit calorie is the calorie contained in the unit physical property. That is, the unit calorie is the calorie contained in the unit mass or unit volume.

[0114] In some embodiments, the target calculation model includes a first calculation model, a second calculation model, a third calculation model and a fourth calculation model. The dish includes one or more servings. The calorie calculation result of the dish includes the total calorie of the dish and / or the calorie of a single serving of the dish. The calorie calculation result of the dish is determined through the target calculation model according to the home dining parameters and the information in the database, including: determining the total raw material calorie of the n raw materials through the first calculation model according to the physical properties and unit calorie of each of the n raw materials used, and determining the total processed material calorie of the m processed materials through the second calculation model according to the physical properties and unit calorie of each of the m processed materials used, where n and m are both positive integers; determining the total calorie of the dish through the third calculation model according to the total calorie of the raw materials, the total calorie of the processed materials and the cooking method coefficient, where the cooking method coefficient is preset or determined according to the processing flow parameters; determining the calorie of a single serving of the dish through the fourth calculation model according to the total calorie of the dish, the physical properties of the dish and the physical properties of a single serving of the dish.

[0115] The first calculation model is used to determine the total calories of the n raw materials used. This model is based on the physical properties and unit calories of each raw material, and obtains the total calories of the raw materials through mathematical calculation. The first calculation model can be preset or pre-trained. The first calculation model can be trained based on the raw material parameter samples and the total calories labels of the raw materials corresponding to the samples based on the training method in the prior art.

[0116] The second calculation model is used to determine the total heat of the processed materials of the m types of processed materials used. Similar to the first calculation model, this model also obtains the total heat of the processed materials through mathematical calculation based on the physical properties and unit heat of the processed materials. The second calculation model can be preset or pre-trained. The second calculation model can be trained based on the processing material parameter samples and the total heat labels of the processed materials corresponding to the samples based on the training method in the prior art.

[0117] The third calculation model combines the total calories of raw materials, the total calories of processed materials and the cooking method coefficient to determine the total calories of the dish. This model is based on the total calories of raw materials, the total calories of processed materials and the cooking method coefficient, and obtains the total calories of the dish through mathematical calculation. The third calculation model can be preset or pre-trained. The third calculation model can be trained based on the total calories of raw materials, the total calories of processed materials and the cooking method coefficient samples, and the total calories labels of the dishes corresponding to the samples based on the training method in the prior art.

[0118] The fourth calculation model determines the calories of a single serving of food based on the total calories of the food, the physical properties of the food, and the physical properties of a single serving of food. This model calculates the calories of a single serving of food through mathematical calculation based on the total calories of the food, the physical properties of the food, and the physical properties of a single serving of food. The fourth calculation model can be preset or pre-trained. The fourth calculation model can be trained based on the total calorie samples of the food, the physical property samples of the food, the physical property samples of a single serving of food, and the calorie labels of a single serving of food based on the training method in the prior art.

[0119] In an illustrative example, the heat calculation process includes but is not limited to the following steps:

[0120] 1. Raw material identification and data acquisition: Use lens scanning technology to automatically identify the type of raw materials through classification algorithms. Retrieve the unit heat of each raw material from the database. The unit heat is the heat contained in unit mass or unit volume, recorded as r1, r2…r n , where r1 is the unit heat of the first raw material, r2 is the unit heat of the second raw material, and so on. n is the unit heat of the nth raw material.

[0121] 2. Acquisition of 3D information of raw materials: Use a monocular or binocular camera to shoot the raw materials used. Use advanced binocular 3D imaging algorithms, such as the NeRF algorithm, or 3D reconstruction algorithms based on monocular image depth estimation, such as the Metric3D algorithm, to obtain 3D information of the raw materials, including voxels, point clouds, and grids.

[0122] 3. 3D information processing: Smooth the 3D information through interpolation to improve the accuracy of the data. Sum or integrate the volume of the raw materials in space to obtain the mass or volume of the raw materials. The physical properties (i.e. mass or volume) of the raw materials collected are recorded as w1, w2…w n , where w1 is the physical property of the first raw material, w2 is the physical property of the second raw material, and so on. n is the physical property of the nth raw material.

[0123] 4. Calculation of total heat of raw materials: The total heat Q of n raw materials is determined by the first calculation model. The calculation formula can be expressed as:

[0124]

[0125] Among them, r i is the unit heat of the i-th raw material, w i is the physical property of the i-th raw material, i is a positive integer, and the value range of i is 1 to n. iis the heat content per unit volume of the i-th raw material, then w i is the volume of the i-th raw material; if r i is the heat content per unit mass of the i-th raw material, then w i is the mass of the i-th raw material.

[0126] 5. Calculation of total heat of processed materials: Determine the total heat Q of processed materials of m types through the second calculation model ’ , and its calculation formula can be expressed as:

[0127]

[0128] Among them, R j is the unit heat of the jth processed material, s j is the physical property of the jth processing material, j is a positive integer, and the value range of j is 1 to m. j is the heat content per unit volume of the jth processed material, then s j is the volume of the jth processed material; if R j is the heat content per unit mass of the jth processed material, then s j is the mass of the jth processed material.

[0129] 6. Calculation of total calories of the dish after preparation: Determine the total calories Q of the dish through the third calculation model * , and its calculation formula can be expressed as:

[0130] Q * ≈A*(Q+Q ’ )

[0131] Wherein, A is a cooking method coefficient, and the cooking method coefficient may be preset or determined according to a processing flow parameter. In some embodiments, the system may preset a cooking coefficient table, and the cooking coefficient table includes processing flow parameters and their corresponding cooking method coefficients. For example, when the processing flow step indicated by the processing flow parameter is frying, the corresponding cooking method coefficient is 1.4, when the processing flow step indicated by the processing flow parameter is first boiling and then frying, the corresponding cooking method coefficient is 1.3, when the processing flow step indicated by the processing flow parameter is first frying and then boiling, the corresponding cooking method coefficient is 1.2, and when the processing flow step indicated by the processing flow parameter is boiling, stir-frying and frying in sequence, the corresponding cooking method coefficient is 1.8.

[0132] 7. Calorie calculation of a single dish: After the preparation is completed, measure the total mass M1 of the entire dish and the mass M2 of a single dish. The calorie Q1 of a single dish is determined by the fourth calculation model, and the calculation formula can be as follows:

[0133]

[0134] Through the above process, the calorie calculation result of the dish can be accurately calculated. The calorie calculation result of the dish includes the total calories of the dish and / or the calories of a single serving of the dish, providing a scientific basis for healthy eating.

[0135] The blood sugar measurement results include nutritional component indicators in dishes that affect blood sugar levels. The nutritional component indicators that affect blood sugar levels may include one or more of protein intake, fat intake, carbohydrate intake, salt intake, sugar intake, and glycemic index.

[0136] In some embodiments, the system determines the blood sugar measurement results of the prepared dishes through a preset target calculation model according to the home dining parameters, which may include: determining the blood sugar measurement results through the target calculation model according to the home dining parameters and the information in the database; wherein the information in the database includes the unit nutrient component index of each of the various raw materials and the unit nutrient component index of each of the various processed materials, and the unit nutrient component index is the nutrient component index contained in the unit physical property. The relevant details can be analogously referred to the process of determining the calorie calculation results of the dishes according to the home dining parameters, and will not be repeated here.

[0137] Step 306: output feedback based on the calorie calculation result of the dish and the blood sugar measurement result. The feedback is used to indicate providing personalized dietary advice to the user.

[0138] In some embodiments, the user's personal information parameters are first collected, and the personal information parameters include the user's physiological parameters and blood sugar information. The user can input this information in advance, such as height, weight, age, and recent blood sugar records. Based on these personal information parameters, the system will estimate the ideal indicators of the nutrients required by the user, such as the recommended intake of protein, fat, and carbohydrates. Taking protein as an example, the recommended intake for different groups of people is different: the average adult is recommended to consume 0.8 grams of protein / kg of body weight per day. Athletes or very active people are recommended to consume 1.2 to 2.0 grams of protein / kg of body weight. The elderly may need more protein, preferably 1.0 to 1.2 grams / kg of body weight / day, to prevent muscle loss.

[0139] The system monitors blood sugar measurement results in real time based on the dishes made by users at home, that is, it monitors the nutritional indicators in the dishes that affect blood sugar levels (such as protein, fat and carbohydrate intake) in real time, and combines smart devices to monitor blood sugar for a period of time after the meal. In this way, the system can provide users with personalized monitoring and feedback based on calorie calculation results and blood sugar measurement results.

[0140] Eventually, the system will output detailed feedback based on a comparative analysis of the ideal indicators of nutrients required by the user for a day and the measured indicators of nutrients. The feedback is used to indicate personalized dietary recommendations for the user. The feedback not only includes healthy dietary recommendations for the day, but also evaluates the health of the food consumed by the user (such as whether it is high in oil and calories, trace element content, etc.). All this information can be compiled into a daily dietary feedback form to provide users with intuitive dietary guidance and suggestions to help them make healthier dietary choices.

[0141] In an illustrative example, Figure 4As shown, the processing device of the home catering calorie and blood sugar monitoring system based on digital information technology includes a data acquisition unit 41, a digital algorithm unit 42 and an information feedback unit 43. Combined with Internet data, various raw materials, unit calories of processed materials, unit nutritional content indicators and other related parameters are collected in advance and a large database system call is established. The built-in algorithm is pre-input and written into the digital algorithm unit 42. When the home user makes dishes under the camera in the data acquisition unit 41, the system will classify and identify the types of raw materials through image recognition technology and pre-trained convolutional neural network models, and use binocular three-dimensional imaging algorithms, such as NeRF algorithms, or monocular three-dimensional reconstruction algorithms based on monocular image depth estimation, such as Metric3D algorithms, etc., to obtain three-dimensional information of raw materials, including but not limited to outputs such as voxels, point clouds and grids. The output is smoothed by interpolation, summed or integrated in space, and the mass or volume of the raw materials is obtained, thereby calculating the total raw material calories of the raw materials. Similar to the calculation process of the total raw material calories, the total processed material calories of the processed materials are calculated. During the user's production process, the video of the processing stage is recorded by the camera in the data acquisition unit, and the processing flow parameters in the dish processing are determined according to the video of the processing stage. After the production is completed, the image of the dish is obtained, and the dish parameters are calculated according to the image of the dish, that is, the total calories of the raw materials, the total calories of the processed materials, the processing flow parameters and the dish parameters are obtained through the data acquisition unit 41, and the calorie calculation results and blood sugar measurement results of the dishes finally produced are estimated through the target calculation model of the digital algorithm unit 42 and the information in the database. In this way, the total calories of the dish, the calories of a single dish and the nutritional index, etc. can be obtained. The information feedback unit 43 performs personalized index detection and gives relevant dietary suggestions, including: estimating the ideal index of the nutrients required by the user according to the user's physiological parameters and blood sugar information. According to the dishes made by the user at home, the nutritional index is monitored in real time, and the blood sugar is monitored for a period of time after the meal in combination with the smart device. And compare the ideal index of the nutritional index with the monitored nutritional index during the day, provide health feedback for the day, and evaluate the health of the food consumed on the day (whether it is high in oil and high in heat, trace element content), etc., and organize it into a daily diet feedback form for feedback to the user.

[0142] In summary, the disclosed embodiment uses information digitization to monitor the home diet process, obtains the calorie calculation results and blood sugar measurement results of the prepared dishes in real time, performs personalized index detection on the user in combination with the user's own situation, and gives relevant diet suggestions, so as to achieve accurate prediction of the user's blood sugar and achieve the effect of healthy and reasonable diet.

[0143] The following is an apparatus embodiment of the present disclosure. For parts not described in detail in the apparatus embodiment, reference may be made to the technical details disclosed in the above method embodiment.

[0144] The present disclosure also provides a home dining calorie and blood sugar monitoring device based on digital information technology, which can realize all or part of the system through software, hardware, or a combination of the two. The device includes:

[0145] A data acquisition unit, used to obtain home catering parameters, which are used to indicate the characteristics of materials used in the home catering production process;

[0146] The digital algorithm unit is used to determine the calorie calculation result and / or blood sugar measurement result of the prepared dish according to the home dining parameters through a preset target calculation model. The target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish. The calorie calculation result is used to indicate the calorie of the dish, and the blood sugar measurement result is used to indicate the nutritional component index in the dish that affects the blood sugar level.

[0147] In a possible implementation, the home catering parameters include raw material parameters and processed material parameters. The raw material parameters include the type and physical properties of the raw materials. The processed material parameters include the type and physical properties of the processed materials. The physical properties include mass or volume.

[0148] In another possible implementation, the home catering parameters also include processing flow parameters and / or dish parameters, where the processing flow parameters are used to indicate the processing flow steps of the dish during the home catering production process, and the dish parameters include the type and / or physical properties of the dish.

[0149] In another possible implementation, the data acquisition unit is further configured to execute at least one of the following methods:

[0150] Acquire an image of the raw material, and determine the raw material parameters according to the image of the raw material by using a preset first recognition model, where the first recognition model is used to recognize the raw material parameters in the image;

[0151] An image of the processed material is acquired, and according to the image of the processed material, parameters of the processed material are determined by a preset second recognition model, where the second recognition model is used to recognize the parameters of the processed material in the image.

[0152] In another possible implementation, the data acquisition unit is further configured to execute at least one of the following methods:

[0153] Obtain a video of a processing stage in a home catering production process, and determine processing flow parameters through a preset third recognition model based on the video of the processing stage, where the third recognition model is used to recognize the processing flow parameters in the video;

[0154] An image of the dish is obtained, and based on the image of the dish, dish parameters are determined by a preset fourth recognition model, where the fourth recognition model is used to recognize the dish parameters in the image.

[0155] In another possible implementation, the digital arithmetic unit is further configured to:

[0156] According to the home dining parameters and the information in the database, the calorie calculation results of the dishes are determined through the target calculation model;

[0157] The information in the database includes the unit heat of each of the various raw materials and the unit heat of each of the various processed materials, where the unit heat is the heat contained in a unit physical property.

[0158] In another possible implementation, the target calculation model includes a first calculation model, a second calculation model, a third calculation model, and a fourth calculation model, the dish includes one or more portions, the calorie calculation result of the dish includes the total calorie of the dish and / or the calorie of a single portion of the dish, and the digital algorithm unit is further used to:

[0159] According to the physical properties and unit heat of the n raw materials used, the total raw material heat of the n raw materials is determined by a first calculation model, and according to the physical properties and unit heat of the m processing materials used, the total processing material heat of the m processing materials is determined by a second calculation model, where n and m are both positive integers;

[0160] Determining the total calories of the dish by a third calculation model according to the total calories of the raw materials, the total calories of the processed materials and the cooking method coefficient, wherein the cooking method coefficient is preset or determined according to the processing flow parameters;

[0161] The calories of a single serving of dish are determined by a fourth calculation model based on the total calories of the dish, the physical properties of the dish, and the physical properties of a single serving of dish.

[0162] In another possible implementation, the device further includes: an information feedback unit, configured to:

[0163] Obtaining personal information parameters, including the user's physiological parameters and blood sugar information;

[0164] Estimate the ideal index of nutrients required by the user based on personal information parameters;

[0165] Compare and analyze the ideal indicators of nutritional content and the blood sugar measurement results of the dishes;

[0166] Based on the results of the comparative analysis, feedback is determined, and the feedback is used to indicate personalized dietary recommendations for the user.

[0167] It should be noted that the device provided in the above embodiment only uses the division of the above-mentioned functional modules as an example to implement its functions. In practical applications, the above-mentioned functions can be assigned to different functional modules according to actual needs, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0168] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0169] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0170] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0171] The disclosed embodiment also proposes a home dining calorie and blood sugar monitoring system based on digital information technology, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0172] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0173] Figure 5 1 is a block diagram of a device 1900 according to an exemplary embodiment. For example, the device 1900 is used to perform a home dining calorie and blood sugar monitoring method based on digital information technology, and the device 1900 can be provided as a server or a terminal device. Figure 5 , the apparatus 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0174] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output interface 1958 (I / O interface). The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2000. TM , MacOS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0175] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the device 1900 to perform the above method.

[0176] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0177] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0178] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0179] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0180] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0181] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0182] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0183] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0184] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring calories and blood sugar in home dining based on digital information technology, characterized in that: The method comprises: Acquire home catering parameters, where the home catering parameters are used to indicate the characteristics of materials used in the home catering production process; According to the home dining parameters, the calorie calculation result and / or the blood sugar measurement result of the prepared dish is determined by a preset target calculation model. The target calculation model is used to perform calorie calculation and / or blood sugar measurement on the dish. The calorie calculation result is used to indicate the calories of the dish, and the blood sugar measurement result is used to indicate the nutritional component index in the dish that affects the blood sugar level.

2. The method according to claim 1, characterized in that The home dining parameters include raw material parameters and processed material parameters. The raw material parameters include the type and physical properties of the raw materials. The processed material parameters include the type and physical properties of the processed materials. The physical properties include mass or volume.

3. The method according to claim 2, characterized in that The home catering parameters also include processing flow parameters and / or dish parameters, wherein the processing flow parameters are used to indicate the processing flow steps of the dish during the home catering production process, and the dish parameters include the type and / or physical properties of the dish.

4. The method according to claim 2, characterized in that: The method of obtaining home dining parameters includes at least one of the following methods: Acquire an image of the raw material, and determine the raw material parameters according to the image of the raw material by using a preset first recognition model, where the first recognition model is used to recognize the raw material parameters in the image; An image of the processed material is acquired, and according to the image of the processed material, parameters of the processed material are determined by a preset second recognition model, where the second recognition model is used to recognize the parameters of the processed material in the image.

5. The method according to claim 3, characterized in that: The method of obtaining home dining parameters also includes at least one of the following methods: Acquire a video of a processing stage in the home-cooked food preparation process, and determine the processing flow parameters by using a preset third recognition model based on the video of the processing stage, wherein the third recognition model is used to recognize the processing flow parameters in the video; An image of the dish is acquired, and based on the image of the dish, the dish parameters are determined by a preset fourth recognition model, where the fourth recognition model is used to recognize the dish parameters in the image.

6. The method according to any one of claims 2 to 5, characterized in that The step of determining the calorie calculation result of the prepared dish according to the home dining parameters by using a preset target calculation model includes: According to the home dining parameters and the information in the database, determining the calorie calculation result of the dish through the target calculation model; The information in the database includes the unit heat of each of the various raw materials and the unit heat of each of the various processed materials, and the unit heat is the heat contained in the unit physical property.

7. The method according to claim 6, characterized in that The target calculation model includes a first calculation model, a second calculation model, a third calculation model and a fourth calculation model. The dish includes one or more portions. The calorie calculation result of the dish includes the total calorie of the dish and / or the calorie of a single portion of the dish. The calorie calculation result of the dish is determined by the target calculation model according to the home dining parameter and the information in the database, including: According to the physical properties and unit heat of each of the n raw materials used, the total raw material heat of the n raw materials is determined by the first calculation model, and according to the physical properties and unit heat of each of the m processing materials used, the total processing material heat of the m processing materials is determined by the second calculation model, wherein both n and m are positive integers; Determining the total calories of the dish by the third calculation model according to the total calories of the raw materials, the total calories of the processed materials and the cooking method coefficient, wherein the cooking method coefficient is preset or determined according to processing flow parameters; The calories of the single serving of dish are determined by the fourth calculation model based on the total calories of the dish, the physical properties of the dish and the physical properties of the single serving of dish.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Acquiring personal information parameters, wherein the personal information parameters include physiological parameters and blood sugar information of the user; According to the personal information parameters, estimating the ideal index of the nutritional components required by the user; Comparative analysis is performed on the ideal index of the nutritional components and the blood sugar measurement result of the dish; Based on the result of the comparative analysis, feedback is determined, and the feedback is used to instruct to provide personalized dietary advice to the user.

9. A home dining calorie and blood sugar monitoring system based on digital information technology, characterized in that: The system comprises: An image acquisition device, used to acquire images during the home catering production process; A processing device, for determining home catering parameters according to the collected images, the home catering parameters being used to indicate the characteristics of materials used in the home catering production process; determining a calorie calculation result and / or a blood glucose measurement result of a prepared dish according to the home catering parameters through a preset target calculation model, the target calculation model being used to perform calorie calculation and / or blood glucose measurement on the dish, the calorie calculation result being used to indicate the calorie of the dish, and the blood glucose measurement result being used to indicate an index of nutrient components in the dish that affect blood glucose levels; and determining feedback according to the calorie calculation result and / or the blood glucose measurement result; The feedback device is used to output the feedback, and the feedback is used to indicate providing personalized dietary advice to the user.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images