Household catering heat and blood glucose monitoring method and system based on digital information technology and storage medium

Abstract

The present disclosure relates to the technical field of intelligent health monitoring, and particularly relates to a home catering calorie and blood glucose monitoring method and system based on digital information technology and a storage medium. The method comprises: acquiring a home catering parameter, the home catering parameter being used to indicate material characteristics used in a home catering preparation process; determining a calorie calculation result and / or a blood glucose measurement result of a prepared dish according to the home catering parameter through a preset target calculation model, the target calculation model being used for calorie calculation and / or blood glucose measurement of 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 a nutritional ingredient index affecting blood glucose level in the dish. Through calculation and monitoring of catering calorie and blood glucose influence, the method can provide relevant information of calorie and blood glucose to a user in real time, help the user to timely adjust diet and living habits, so as to achieve a better blood glucose control effect.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of intelligent health monitoring, in particular to a home catering calorie and blood glucose monitoring method and system based on digital information technology and a storage medium. BACKGROUND

[0002] With the continuous rise of global diabetes prevalence, personalized blood glucose monitoring has become a key link in diabetes management. Diabetes is a chronic metabolic disease characterized by abnormally high blood glucose levels.

[0003] It is particularly important to provide personalized blood glucose monitoring. As one of the important factors affecting blood glucose levels, diet has become a problem to be solved by accurately calculating and monitoring catering calories and delivering calorie and blood glucose information to users. Although related technologies have made some progress in blood glucose monitoring, they often rely on frequent blood sampling and device calibration, which not only brings inconvenience to the daily management of patients, but also fails to provide immediate monitoring of the intake of nutritional ingredients in diet. SUMMARY

[0004] Therefore, the present disclosure provides a home catering calorie and blood glucose monitoring method and system based on digital information technology and a storage medium.

[0005] According to an aspect of the present disclosure, a home catering calorie and blood glucose monitoring method based on digital information technology is provided, which comprises:

[0006] Obtaining home catering parameters, the home catering parameters being used to indicate the material characteristics used in the home catering production process;

[0007] According to the home catering parameters, determining the calorie calculation result and / or blood glucose measurement result of the finished dish through a preset target calculation model, the target calculation model being used to calculate the calorie and / or measure the blood glucose of 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 the nutritional ingredient index affecting the blood glucose level in the dish.

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

[0009] In another possible implementation, the home catering parameters further include processing procedure parameters and / or dish parameters, the processing procedure parameters being used to indicate the processing procedure steps of the dish in the home catering production process, and the dish parameters including the type and / or physical properties of the dish.

[0010] In another possible implementation, the obtaining the home catering parameter comprises at least one of the following:

[0011] obtaining an image of the raw material, and determining the raw material parameter from the image of the raw material by using a preset first identification model, the first identification model being configured to identify the raw material parameter in the image;

[0012] obtaining an image of the processed material, and determining the processed material parameter from the image of the processed material by using a preset second identification model, the second identification model being configured to identify the processed material parameter in the image.

[0013] In another possible implementation, the obtaining the home catering parameter further comprises at least one of the following:

[0014] obtaining a video of a processing stage in the home catering preparation process, and determining the processing flow parameter from the video of the processing stage by using a preset third identification model, the third identification model being configured to identify the processing flow parameter in the video;

[0015] obtaining an image of the dish, and determining the dish parameter from the image of the dish by using a preset fourth identification model, the fourth identification model being configured to identify the dish parameter in the image.

[0016] In another possible implementation, the determining the heat calculation result of the prepared dish from the home catering parameter by using a preset target calculation model comprises:

[0017] determining the heat calculation result of the dish from the home catering parameter and information in a database by using the target calculation model;

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

[0019] In another possible implementation, the target calculation model comprises a first calculation model, a second calculation model, a third calculation model and a fourth calculation model, the dish comprises one or more dishes, the heat calculation result of the dish comprises total heat of the dish and / or heat of a single dish, and the determining the heat calculation result of the dish from the home catering parameter and information in a database by using the target calculation model comprises:

[0020] According to the physical properties and unit heat of each of the n raw materials, a total 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 processed materials, a total heat of the m processed materials is determined by the second calculation model, wherein n and m are positive integers;

[0021] According to the total heat of the raw materials, the total heat of the processed materials and a cooking mode coefficient, a total heat of the dish is determined by the third calculation model, wherein the cooking mode coefficient is preset or determined according to a processing flow parameter;

[0022] According to the total heat of the dish, the physical properties of the dish and the physical properties of the single serving dish, a heat of the single serving dish is determined by the fourth calculation model.

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

[0024] Obtaining personal information parameters, wherein the personal information parameters include physiological parameters and blood glucose information of a user;

[0025] According to the personal information parameters, an ideal index of a nutritional component required by the user is estimated;

[0026] The ideal index of the nutritional component and the blood glucose measurement result of the dish are compared and analyzed;

[0027] According to a result of the comparison and analysis, a feedback opinion is determined, wherein the feedback opinion is used to indicate a personalized dietary suggestion provided for the user.

[0028] According to another aspect of the present disclosure, a home catering heat and blood glucose monitoring device based on digital information technology is provided, and the device includes:

[0029] A data acquisition unit is configured to acquire home catering parameters, wherein the home catering parameters are used to indicate material characteristics used in a home catering process;

[0030] A digital algorithm unit is configured to determine a heat calculation result and / or a blood glucose measurement result of a dish made according to the home catering parameters by a preset target calculation model, wherein the target calculation model is used to calculate a heat of the dish and / or measure blood glucose of the dish, the heat calculation result is used to indicate the heat of the dish, and the blood glucose measurement result is used to indicate a nutritional component index affecting blood glucose level in the dish.

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

[0032] In another possible implementation, the home catering parameters further include processing procedure parameters and / or dish parameters, the processing procedure parameters are used to indicate processing procedure steps of the dish in the home catering production process, and the dish parameters include types and / or physical properties of the dish.

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

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

[0035] acquire an image of the processed material, and determine the processed material parameters by using a preset second recognition model according to the image of the processed material, the second recognition model being used to recognize the processed material parameters in the image.

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

[0037] acquire a video of a processing stage in the home catering production process, and determine the processing procedure parameters by using a preset third recognition model according to the video of the processing stage, the third recognition model being used to recognize the processing procedure parameters in the video;

[0038] acquire an image of the dish, and determine the dish parameters by using a preset fourth recognition model according to the image of the dish, the fourth recognition model being used to recognize the dish parameters in the image.

[0039] In another possible implementation, the digital algorithm unit is further configured to:

[0040] determine the calorie calculation result of the dish by using the target calculation model according to the home catering parameters and information in a database;

[0041] wherein the information in the database includes respective unit calories of a plurality of raw materials and respective unit calories of a plurality of processed materials, and the unit calorie is a calorie contained in a unit physical property.

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

[0043] According to the physical properties and unit calories of each of the n raw materials, a raw material total calorie of the n raw materials is determined by the first computing model, and according to the physical properties and unit calories of each of the m processed materials, a processed material total calorie of the m processed materials is determined by the second computing model, where n and m are positive integers;

[0044] According to the raw material total calorie, the processed material total calorie, and a cooking mode coefficient, a total calorie of the dishes is determined by the third computing model, where the cooking mode coefficient is preset or determined according to a processing flow parameter;

[0045] According to the total calorie of the dishes, the physical properties of the dishes, and the physical properties of the single serving of the dishes, a calorie of the single serving of the dishes is determined by the fourth computing model.

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

[0047] Obtain personal information parameters, where the personal information parameters include physiological parameters and blood glucose information of a user;

[0048] According to the personal information parameters, estimate an ideal index of a nutritional component required by the user;

[0049] Compare and analyze the ideal index of the nutritional component and the blood glucose measurement result of the dishes;

[0050] According to a result of the comparison and analysis, determine a feedback opinion, where the feedback opinion is used to indicate a personalized dietary suggestion provided for the user.

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

[0052] An image acquisition apparatus configured to acquire images in a home catering production process;

[0053] The processing device is configured to determine, according to the collected image, a home catering parameter, the home catering parameter being used to indicate a material characteristic used in the home catering process; determine, according to the home catering parameter, a calorie calculation result and / or a blood glucose measurement result of a finished dish through a preset target calculation model, the target calculation model being used for calorie calculation and / or blood glucose measurement of the dish, the calorie calculation result being used to indicate a calorie of the dish, and the blood glucose measurement result being used to indicate a nutritional component index affecting blood glucose level in the dish; and determine a feedback opinion according to the calorie calculation result and / or the blood glucose measurement result.

[0054] The feedback device is configured to output the feedback opinion, the feedback opinion being used to provide personalized dietary suggestions for a user.

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

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

[0057] The home catering calorie and blood glucose monitoring method based on digital information technology provided by the embodiments of the present disclosure can obtain a home catering parameter, the home catering parameter being used to indicate a material characteristic used in a home catering process, determine a calorie calculation result and / or a blood glucose measurement result of a finished dish according to the home catering parameter through a preset target calculation model, the target calculation model being used for calorie calculation and / or blood glucose measurement of the dish, the calorie calculation result being used to indicate a calorie of the dish, and the blood glucose measurement result being used to indicate a nutritional component index affecting blood glucose level in the dish; that is, the home catering parameter in the home catering process is obtained, and the preset target calculation model is combined to calculate and monitor catering calorie and blood glucose influence. The method can provide relevant information of calorie and blood glucose to a user in real time, help the user adjust diet and living habits in time, and achieve better blood glucose control effect.

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

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

[0060] Figure 1 Fig. 1 shows a structural schematic diagram of a home catering calorie and blood glucose monitoring system based on digital information technology according to an example embodiment of the present disclosure.

[0061] Figure 2 Fig. 2 shows a flow chart of a home catering calorie and blood glucose monitoring method based on digital information technology according to an example embodiment of the present disclosure.

[0062] Figure 3 Fig. 3 shows a flow chart of a home catering calorie and blood glucose monitoring method based on digital information technology according to another example embodiment of the present disclosure.

[0063] Figure 4 Fig. 4 shows a principle schematic diagram of a home catering calorie and blood glucose monitoring method based on digital information technology according to an example embodiment of the present disclosure.

[0064] Figure 5 Fig. 5 is a block diagram of an apparatus according to an example embodiment. DETAILED DESCRIPTION

[0065] Various example embodiments, features and aspects of the present disclosure will be explained below in detail with reference to the accompanying drawings. The same reference numerals in different drawings denote the same or similar elements. Although various aspects of embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

[0066] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0067] In addition, for the purpose of convenience and brevity, detailed descriptions of well-known methods, apparatuses, circuits, and devices are omitted so as not to obscure the concepts of the present disclosure. In some instances, well-known methods, apparatuses, circuits, and devices are described in detail to facilitate the understanding of the present disclosure.

[0068] First, the application scenarios related to the embodiments of the present disclosure are introduced. Please refer to Figure 1 Fig. 1 shows a structural schematic diagram of a home catering calorie and blood glucose monitoring system based on digital information technology according to an example embodiment of the present disclosure.

[0069] The system can include three main devices: an image acquisition device 11, a processing device 12, and a feedback device 13. The three devices are introduced as follows.

[0070] Image acquisition device 11: used to acquire images of the home cooking process, providing visual data for subsequent parameter identification and analysis.

[0071] Processing device 12: as the core of the system, used to determine various parameters related to home cooking, i.e. home cooking parameters, based on the images acquired by the image acquisition device 11. Home cooking parameters can include: 1. Raw material parameters: referring to the basic parameters of food materials, which can include the type and physical properties of raw materials, and the physical properties can include mass or volume. 2. Processing material parameters: referring to the parameters of processing materials such as seasonings and oils added during cooking, which can include the type and physical properties of processing materials, and the physical properties can also include mass or volume. 3. Dish parameters: referring to the parameters of the final dish, which can 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 cooking parameters, calculates the calories and possible effects on blood sugar of the dish through large databases and target calculation models. The processing device 12 can include: 1. Large database: storing information such as unit calories of various raw materials and processing materials. 2. Target calculation model: based on the home cooking parameters and information in the database, calculating the calorie calculation results and / or blood sugar measurement results of the dish.

[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 can include: 1. Personal index monitoring: based on the user's health data and eating habits, monitoring and analyzing the user's dietary calories and blood sugar effects. 2. Suggestion feedback: based on the calorie calculation results and / or blood sugar measurement results, providing personalized dietary suggestions and feedback for the user, helping the user better manage blood sugar and calorie intake.

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

[0075] The whole system is a closed-loop system, from data collection to processing, and then to feedback, forming a complete monitoring and management process. According to the specific application scenarios and user needs of the system, the three devices can be appropriately expanded or reduced to meet specific functional requirements. For example, the function of determining the home dining parameters according to the collected images can also be integrated in the image collection device 11, and the image analysis and parameter identification can be performed at the same time when the image is collected, thereby reducing the delay of data transmission and processing. For example, the function of determining the feedback according to the heat calculation result and the blood glucose measurement result can also be integrated in the feedback device 13, so that the feedback can be 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 better blood sugar control.

[0076] It should be noted that the implementation of the system hardware design can include the following several ways: 1, integrated hardware design: the above three devices are integrated in one computing device, such as a smart phone or a dedicated kitchen device, the image collection device 11 is the camera in the computing device, the processing device 12 is the processor in the computing device, and the feedback device 13 is the display screen in the computing device. 2, modular hardware design: the system is composed 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 collection device 11 in the kitchen, and the processing device 12 and the feedback device 13 on a remote server. 4, combination of cloud and edge computing: edge devices (such as terminal devices) process real-time data, while cloud servers process non-real-time, large-scale data analysis. 5, multiple devices working together: different devices work together, such as a camera as an image collection 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 of the system, cost budget, user scenarios and performance requirements, and the present embodiment does not limit them.

[0077] In the following, several exemplary embodiments of the home dining heat and blood glucose monitoring method based on digital information technology provided by the present embodiment are introduced.

[0078] Please refer to Figure 2 , which shows the flowchart of the home dining heat and blood glucose monitoring method based on digital information technology provided by one exemplary embodiment of the present disclosure. The present embodiment uses the method in the system described above. The method includes the following steps. Figure 1

[0079] Step 201, obtaining home dining parameters, the home dining parameters are used to indicate the material characteristics used in the home dining preparation process. ​

[0080] The home catering parameter refers to the detailed characteristics of various materials and processes used in the preparation of catering in a home environment, which is used to indicate the material characteristics used in the home catering preparation process.

[0081] The home catering parameter includes raw material parameters and processed material parameters. The raw material refers to the basic material used in the home catering preparation process. The raw material parameters include the type (such as meat, vegetables, grains, etc.) and physical properties (such as mass, volume, etc.) of the raw material. These parameters can be obtained through image recognition technology and classified and identified through a pre-trained neural network.

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

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

[0084] In terms of implementation of the home catering parameter acquisition step, the cooking process can be monitored in real time through a camera, combined with image recognition and video analysis technology, to automatically record and analyze the home catering parameters in real time during the cooking process. It should be noted that the acquisition method of the home catering parameter can refer to the related description in the following embodiments, which will not be introduced here.

[0085] Step 202, according to the home catering parameter, the heat calculation result and / or the blood sugar measurement result of the finished dish are determined through a pre-set target calculation model. The target calculation model is used for heat calculation and / or blood sugar measurement of the dish. The heat calculation result is used to indicate the heat of the dish, and the blood sugar measurement result is used to indicate the nutritional component index affecting the blood sugar level in the dish.

[0086] In some embodiments, the system inputs the home catering parameters into a preset target calculation model in real time, or inputs the home catering parameters after the dish is prepared, and finally outputs the heat calculation result and / or the blood glucose measurement result of the prepared dish. The target calculation model is a preset mathematical model for calculating the heat calculation result and / or the blood glucose measurement result of the dish according to the home catering parameters. This model can be used to analyze the raw material parameters and the processed material parameters, and can further combine other parameters (such as processing process parameters and / or dish parameters) to determine the heat of the final dish and / or the influence of the dish on blood glucose level. The target calculation model can be trained based on the existing technology based on the training method based on the home catering parameter samples, and the heat calculation result labels and / or the blood glucose measurement result labels corresponding to the samples.

[0087] It should be noted that the application process of the target calculation model can refer to the related description in the following embodiments, which will not be introduced here.

[0088] The heat 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 weight control and maintaining a healthy diet. In some embodiments, the dish includes one or more servings, and the heat calculation result includes the dish heat obtained by the target calculation model, which can be the total heat of the dish and / or the heat of a single serving of the dish.

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

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

[0091] In summary, the embodiment of the present disclosure provides a home catering heat and blood glucose monitoring method based on digital information technology. By obtaining a home catering parameter, the home catering parameter is used to indicate the material characteristics used in the home catering preparation process. According to the home catering parameter, the heat calculation result and / or the blood glucose measurement result of the prepared dish are determined through a preset target calculation model. The target calculation model is used for heat calculation and / or blood glucose measurement of the dish. The heat calculation result is used to indicate the heat of the dish, and the blood glucose measurement result is used to indicate the nutritional ingredient index affecting the blood glucose level in the dish. That is, by real-time calculation and monitoring of catering heat and blood glucose, the method can provide relevant information of heat and blood glucose to the user in time, help the user to adjust the diet and living habits in time, and achieve better blood glucose control effect.

[0092] In some embodiments, the system automatically collects the relevant material parameters in the home catering preparation process through advanced image acquisition and video recording technology combined with neural network algorithm. That is, the system can realize image acquisition and video recording through the camera, and use the pre-trained neural network for classification and identification to obtain the specific parameters of the raw materials, processed materials, processing procedures and finished dishes. The heat and blood glucose are monitored according to the collected relevant material parameters in the home catering preparation process. Please refer to Figure 3 , which shows a flowchart of a home catering heat and blood glucose monitoring method based on digital information technology provided by another example embodiment of the present disclosure. The embodiment uses the method for Figure 1 The system described is used as an example. The method includes the following steps.

[0093] Step 301, acquiring an image of raw materials, and determining raw material parameters according to the image of raw materials through a preset first identification model. The first identification model is used to identify the raw material parameters in the image.

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

[0095] The first identification model is a preset neural network model used to identify the raw material parameters from the image of the raw material. The first identification model can be trained based on the training method in the prior art based on the image sample of the raw material and the raw material parameter label corresponding to the image sample. Illustratively, the first identification model is a convolutional neural network (CNN) model. The embodiment of the present disclosure is not limited thereto.

[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 collection and identification: high-definition cameras are used to capture images of raw materials, which will serve as input information. Classification and identification are performed by a pre-trained first identification model, which is matched with a pre-set database to determine the type of raw materials. 2. Three-dimensional information acquisition: three-dimensional information of raw materials is obtained using binocular three-dimensional imaging algorithms such as NeRF algorithm, or monocular three-dimensional reconstruction algorithms based on monocular image depth estimation such as Metric3D algorithm. These algorithms can output three-dimensional information such as voxels, point clouds, and meshes of raw materials. 3. Smoothing processing and volume calculation: the three-dimensional information of raw materials is smoothed by interpolation method to improve the continuity and accuracy of the data. Summation or integration is performed 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, obtaining an image of the processed material, determining the processed material parameters according to the image of the processed material, the second identification model is used to identify the processed material parameters in the image.

[0098] In the processing material preparation stage or processing stage of the home catering production process, the image of the processed material is captured by real-time or timing photography, and the image includes the processed material. The image of the processed material is input into the second identification model, and the output of the processed material parameters is obtained, including the type and physical properties of the processed material, and the physical properties include mass or volume.

[0099] The second identification model is a pre-set neural network model used to identify the processed material parameters from the image of the processed material. The second identification model can be trained based on the existing technology based on the training method of the image sample of the processed material and the corresponding processed material parameter label. Illustratively, the second identification model is a CNN model. The present disclosure does not limit 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, which will not be repeated here.

[0101] Step 303, obtaining a video of the processing stage in the home catering production process, determining the processing flow parameters according to the video of the processing stage, the third identification model is used to identify the processing flow parameters in the video.

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

[0103] The third identification model is a preset neural network model used to identify the processing procedure parameters from the video of the processing stage. The third identification model can be trained based on the existing technology based on the training method of the video sample of the processing stage and the processing procedure parameter label corresponding to the video sample. Illustratively, the third identification model is a CNN model, such as a Faster Region-based Convolutional Neural Network (Faster R-CNN) model. The present disclosure does not limit this.

[0104] In some embodiments, the process of collecting the video of the processing stage and determining the processing procedure parameters includes, but is not limited to, the following steps: 1. Video stream collection: First, acquire the video of the processing stage in the home catering production process. 2. Frame generation technology: Use frame generation technology to convert continuous video stream into a series of static image sequences, which allows detailed analysis of each frame image. 3. Application of neural network: Locate and identify the corresponding cooking action of each frame image through the third identification model. This model is specially trained to recognize different cooking actions such as stir-frying and boiling. 4. Action recognition and time labeling: Identify the cooking action corresponding to each frame image through the third identification model, and record the start and end time of these actions to provide accurate time labels for each processing procedure step. 5. Transition time detection: Further analyze the action recognition results to detect the transition time points between different processing procedure steps, thereby obtaining detailed information of the processing procedure steps and their durations.

[0105] Step 304, acquire the image of the dish, and determine the dish parameters through the preset fourth identification model according to the image of the dish. The fourth identification model is used to identify the dish parameters in the image.

[0106] In the case of home catering production is completed, especially in the last turning point of the processing flow, the image of the dish can be captured by real-time or timing photography technology. This process can use a camera to extract key frames from the captured video stream, which contains the image of the finished dish. The image includes the finished dish. The image of the dish is input into the fourth identification model, and the dish parameters are output, including the type and / or physical properties of the dish, including mass or volume.

[0107] The fourth identification model is a preset neural network model for identifying dish parameters in dish images. The fourth identification model can be trained based on existing technology based on training methods based on image samples of dishes and dish labels corresponding to the image samples. Illustratively, the fourth identification model is a CNN model. The present disclosure does not limit 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, which will not be described here.

[0109] Step 305, according to the raw material parameters, the processing material parameters, the processing flow parameters and the dish parameters, the heat calculation result and the blood glucose measurement result of the finished dish are determined by the preset target calculation model.

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

[0111] Among them, the raw material parameters and the processing material parameters are the necessary input data, while the processing flow parameters and the dish parameters are optional input data to adapt to different calculation requirements and accuracy requirements.

[0112] In some embodiments, according to the home catering parameters, the relevant data is retrieved from a large database, and the heat calculation result and the blood glucose measurement result of the dish are determined by combining the built-in algorithm.

[0113] Taking heat calculation as an example, the system can determine the heat calculation result of the dish according to the home catering parameters and the information in the database through the target calculation model; wherein the information in the database includes the unit heat of each of the plurality of raw materials and the unit heat of each of the plurality of processing materials. The unit heat is the heat contained in the unit physical property. That is, the unit heat is the heat contained in the unit mass or unit volume.

[0114] In some embodiments, the target computing model comprises a first computing model, a second computing model, a third computing model, and a fourth computing model, the dishes comprise one or more servings, the heat calculation result of the dishes comprises a total heat of the dishes and / or a heat of a single serving of the dishes, and the heat calculation result of the dishes is determined by the target computing model according to the home catering parameters and information in the database, comprising: determining a total raw material heat of n kinds of raw materials used by the first computing model according to the physical properties and unit heat of each of the n kinds of raw materials, and determining a total processing material heat of m kinds of processing materials used by the second computing model according to the physical properties and unit heat of each of the m kinds of processing materials, wherein n and m are positive integers; determining the total heat of the dishes by the third computing model according to the total raw material heat, the total processing material heat, and a cooking method coefficient, wherein the cooking method coefficient is preset or determined according to the processing procedure parameters; and determining the heat of a single serving of the dishes by the fourth computing model according to the total heat of the dishes, the physical properties of the dishes, and the physical properties of a single serving of the dishes.

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

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

[0117] The third computing model combines the total raw material heat, the total processing material heat, and the cooking method coefficient to determine the total heat of the dishes. This model is based on the total raw material heat, the total processing material heat, and the cooking method coefficient, and the total heat of the dishes is obtained by mathematical calculation. The third computing model can be preset or pre-trained. The third computing model can be trained based on total raw material heat samples, total processing material heat samples, and cooking method coefficient samples, and the total heat labels of the dishes corresponding to the samples based on the training method in the prior art.

[0118] The fourth calculation model determines the heat of a single serving of a dish based on the total heat of the dish, the physical properties of the dish, and the physical properties of a single serving of the dish. This model determines the heat of a single serving of a dish based on the total heat of the dish, the physical properties of the dish, and the physical properties of a single serving of the dish through mathematical calculation. The fourth calculation model can be pre-set or pre-trained. The fourth calculation model can be trained based on the total heat of the dish sample, the physical properties of the dish sample, and the physical properties of a single serving of the dish sample, and the heat label of a single serving of the dish 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 material through classification algorithm. Retrieve the unit heat of each raw material from the database, which is the heat contained in unit mass or unit volume, denoted 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, and r n is the unit heat of the nth raw material.

[0121] 2. Three-dimensional information acquisition of raw materials: Use monocular or binocular cameras to take pictures of the raw materials used. Use advanced binocular three-dimensional imaging algorithms such as NeRF algorithm, or monocular image depth estimation-based three-dimensional reconstruction algorithms such as Metric3D algorithm to obtain the three-dimensional information of the raw materials, including voxel, point cloud and mesh, etc.

[0122] 3. Three-dimensional information processing: Smooth the three-dimensional information through interpolation method 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. Collect the physical properties (i.e. mass or volume) of the raw materials, denoted 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, and w n is the physical property of the nth raw material.

[0123] 4. Total heat calculation of raw materials: Determine the total heat Q of the n raw materials through the first calculation model, and the calculation formula can be expressed as:

[0124]

[0125] , where r i is the unit heat of the ith raw material, w i is the physical property of the ith raw material, i is a positive integer, and the value of i ranges from 1 to n. If r iw i is the volume of the i-th raw material; if r i is the heat contained in unit mass of the i-th raw material, then w i is the mass of the i-th raw material.

[0126] 5. Total heat of processed materials: The total heat of the m processed materials Q ’ , which can be expressed as:

[0127]

[0128] where R j is the unit heat of the j-th processed material, s j is the physical attribute of the j-th processed material, and j is a positive integer, with the value of j ranging from 1 to m. If R j is the heat contained in unit volume of the j-th processed material, then s j is the volume of the j-th processed material; if R j is the heat contained in unit mass of the j-th processed material, then s j is the mass of the j-th processed material.

[0129] 6. Total heat of the finished dish: The total heat of the dish Q * , which can be expressed as:

[0130] Q * ≈ A * (Q + Q ’ )

[0131] where A is the cooking method coefficient, which can be preset or determined according to the processing flow parameters. In some embodiments, the system can preset a cooking coefficient table, which includes processing flow parameters and their corresponding cooking method coefficients. For example, the cooking method coefficient corresponding to the processing flow step of frying is 1.4, the cooking method coefficient corresponding to the processing flow step of boiling first and then frying is 1.3, the cooking method coefficient corresponding to the processing flow step of frying first and then boiling is 1.2, and the cooking method coefficient corresponding to the processing flow step of boiling, frying, and frying in sequence is 1.8.

[0132] 7. Heat calculation of a single serving: After the dish is finished, the total mass of the dish M1 and the mass of a single serving M2 are measured. The heat of a single serving Q1 is determined by the fourth calculation model, and its calculation formula can be as follows:

[0133]

[0134] Through the above process, the calorie calculation result of the dish can be accurately calculated, and 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, thereby providing a scientific basis for healthy diet.

[0135] The blood glucose measurement result includes a nutritional component index in the dish that affects blood glucose level, and the nutritional component index that affects blood glucose level can include one or more of protein intake, fat intake, carbohydrate, salt intake, sugar intake, and glycemic index.

[0136] In some embodiments, the system determines the blood glucose measurement result of the finished dish according to the home catering parameters through a preset target calculation model, which can include: determining the blood glucose measurement result through the target calculation model according to the home catering parameters and information in the database; wherein the information in the database includes the unit nutritional component index of each of the plurality of raw materials and the unit nutritional component index of each of the plurality of processing materials, and the unit nutritional component index is the nutritional component index contained in the unit physical property. For related details, reference can be made to the process of determining the calorie calculation result of the dish according to the home catering parameters, which will not be described here.

[0137] In step 306, feedback opinions are output according to the calorie calculation result and the blood glucose measurement result of the dish, and the feedback opinions are used to indicate personalized dietary recommendations for the user.

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

[0139] The system monitors the blood glucose measurement result in real time according to the dish prepared by the user at home, that is, it monitors the nutritional component index (such as the intake of protein, fat and carbohydrates) in the dish that affects blood glucose level in real time, and combines with the blood glucose monitoring by the intelligent device after a period of time after meal. In this way, the system can provide personalized monitoring and feedback based on the calorie calculation result and the blood glucose measurement result for the user.

[0140] Finally, the system will output detailed feedback, which will be based on the comparison of the ideal targets of the nutrients required by the user in a day and the measured targets of the nutrients. The feedback is used to indicate personalized dietary recommendations for the user, which not only includes the healthy dietary recommendations for the day, but also evaluates the healthiness of the food ingested by the user (e.g. whether it is high in oil and heat, trace element content, etc.). All this information can be collated into a daily diet feedback table to provide the user with intuitive dietary guidance and recommendations to help them make healthier dietary choices.

[0141] In one illustrative example, as Figure 4As shown, the processing device of the home catering heat and blood glucose monitoring system based on digital information technology includes a data acquisition unit 41, a digital algorithm unit 42, and an information feedback unit 43, combines Internet data, pre-collects various raw materials, processes unit heat, unit nutrient component indicators, and other related parameters of the materials, and establishes a large database system for calling, pre-enters the built-in algorithm, and writes it 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 a pre-trained convolutional neural network model, and use binocular three-dimensional imaging algorithms such as NeRF algorithm, or monocular three-dimensional reconstruction algorithms based on monocular image depth estimation such as Metric3D algorithm, to obtain three-dimensional information of the raw materials, including but not limited to voxel, point cloud, and grid output. The output is smoothed by interpolation method, and the mass or volume of the raw material is obtained by summing or integrating in space, so as to calculate the total heat of the raw material. Similarly, the total heat of the processed material is calculated. During the user's making process, the camera in the data acquisition unit records the video of the processing stage, determines the processing flow parameters in the dish processing according to the video of the processing stage, and obtains the image of the dish after the making is completed, and calculates the dish parameters according to the image of the dish, that is, the total heat of the raw material, the total heat of the processed material, the processing flow parameters, and the dish parameters are obtained through the data acquisition unit 41, and the heat calculation result and the blood glucose measurement result of the finally made dish are estimated through the target calculation model of the digital algorithm unit 42 and the information in the database. In this way, the total heat of the dish, the heat of a single dish, and the nutrient component indicators can be obtained. Personalized index detection is performed through the information feedback unit 43, and relevant dietary suggestions are given, including: estimating the ideal index of the nutrient component required by the user according to the physiological parameters and blood glucose information of the user. According to the dishes made by the user at home, the nutrient component index is monitored in real time, and the blood glucose is monitored by the intelligent device after a period of time. The ideal index of the nutrient component and the monitored nutrient component index are compared in a day, the health feedback opinion of the day is provided, the health degree (whether high oil and high heat, trace element content) of the food ingested in the day is evaluated, and the daily dietary feedback table is arranged and fed back to the user.

[0142] In summary, the embodiments of the present disclosure monitor the home diet process in an information digitized manner, obtain the heat calculation result and the blood glucose measurement result of the finally made dish in real time, perform personalized index detection on the user according to the user's own situation, and give relevant dietary suggestions. The precise prediction of the user's blood glucose is realized, and the effect of healthy and reasonable diet is achieved.

[0143] The following is an apparatus embodiment of the embodiments of the present disclosure. For parts not elaborated in the apparatus embodiment, please refer to the technical details disclosed in the above method embodiments.

[0144] The embodiments of the present disclosure also provide a home catering calorie and blood glucose monitoring device based on digital information technology. The device can realize all or part of the system through software, hardware, and a combination of the two. The device comprises:

[0145] A data acquisition unit is configured to acquire home catering parameters, the home catering parameters being used to indicate the characteristics of materials used in the home catering process.

[0146] A digital algorithm unit is configured to determine a calorie calculation result and / or a blood glucose measurement result of a completed dish according to the home catering parameters through a preset target calculation model, the target calculation model being used to calculate the calorie and / or measure the blood glucose of 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 the nutritional ingredient index affecting the blood glucose level in the dish.

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

[0148] In another possible implementation, the home catering parameters further comprise processing procedure parameters and / or dish parameters, the processing procedure parameters being used to indicate the processing procedure steps of the dish in the home catering process, and the dish parameters comprising the types and / or physical properties of the dish.

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

[0150] acquire an image of the raw materials, and determine the raw material parameters according to the image of the raw materials through a preset first recognition model, the first recognition model being used to recognize the raw material parameters in the image;

[0151] acquire an image of the processed materials, and determine the processed material parameters according to the image of the processed materials through a preset second recognition model, the second recognition model being used to recognize the processed material parameters in the image.

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

[0153] acquire a video of the processing stage in the home catering process, and determine the processing procedure parameters according to the video of the processing stage through a preset third recognition model, the third recognition model being used to recognize the processing procedure parameters in the video;

[0154] An image of the dish is acquired, and a dish parameter is determined according to the image of the dish by using a fourth recognition model, the fourth recognition model being used for recognizing the dish parameter in the image.

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

[0156] According to the home catering parameter and information in the database, a heat calculation result of the dish is determined by using a target calculation model.

[0157] The information in the database includes unit heat of each of a plurality of raw materials and unit heat of each of a plurality of processed materials, and the unit heat is 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 dishes, and the heat calculation result of the dish includes total heat of the dish and / or heat of a single dish, and the digital algorithm unit is further configured to:

[0159] According to the physical property and the unit heat of each of the n raw materials, a total raw material heat of the n raw materials is determined by using the first calculation model, and according to the physical property and the unit heat of each of the m processed materials, a total processed material heat of the m processed materials is determined by using the second calculation model, n and m are positive integers;

[0160] According to the total raw material heat, the total processed material heat and a cooking mode coefficient, the total heat of the dish is determined by using the third calculation model, and the cooking mode coefficient is preset or determined according to the processing flow parameter;

[0161] According to the total heat of the dish, the physical property of the dish and the physical property of a single dish, the heat of the single dish is determined by using the fourth calculation model.

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

[0163] Acquire personal information parameters, the personal information parameters including physiological parameters and blood glucose information of the user;

[0164] According to the personal information parameters, an ideal index of a nutritional component required by the user is estimated;

[0165] The ideal index of the nutritional component and the blood glucose measurement result of the dish are compared and analyzed;

[0166] According to a result of the comparison and analysis, a feedback opinion is determined, the feedback opinion being used for indicating to provide personalized dietary suggestions for the user.

[0167] It should be noted that the apparatus provided by the above embodiments is only exemplified by the above division of various functional modules when realizing its functions. In actual application, the above functions can be completed by different functional modules according to actual needs, that is, the content structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0168] As to the apparatus in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and will not be described in detail here.

[0169] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or contains modules that can be used to execute the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0170] The embodiments of the present disclosure also propose a computer-readable storage medium having computer program instructions stored thereon, the computer program instructions being executed by a processor to implement the above method. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0171] The embodiments of the present disclosure also propose a home catering heat and blood glucose 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 computer readable code or non-volatile computer readable storage medium carrying computer readable code, when the computer readable code is running in the processor of the electronic device, the processor in the electronic device executes the above method.

[0173] Figure 5 is a block diagram of an apparatus 1900 according to an exemplary embodiment. For example, the apparatus 1900 is used to execute the home catering heat and blood glucose monitoring method based on digital information technology, and the apparatus 1900 can be provided as a server or terminal device. Referring to 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 executable by the processing component 1922, such as an application program. The application program stored in the memory 1932 can include one or more than one module 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 apparatus 1900 can also include a power supply component 1926 configured to supply power to the apparatus 1900, a wired or wireless network interface 1950 configured to connect the apparatus 1900 to a network, and an input / output interface 1958 (I / O interface). The apparatus 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , MacOS X TM , Unix TM , Linux TM , FreeBSD TM , or the like.

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

[0176] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0177] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic 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 (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0178] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0179] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0180] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0181] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, to cause a series of operational elements or steps to be performed on the computer to produce a computer implemented process. Such instructions can also be stored and / or executed by other computer-readable media. Computer-readable media storing the computer readable instructions can include computers, processors, or other programmable data processing apparatuses capable of receiving, storing, and / or executing instructions.

[0182] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational elements or steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable elements, or other

[0183] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operations of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions ("instructions"). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0184] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not exhaustive, and is not limited to the embodiments disclosed. Numerous modifications and adaptations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of words in this document is intended to best explain the principles of the embodiments, practical application, or improvement over the technology in the market, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring the caloric and glycemic intake of a meal at home based on digital information technology, characterized in that, The method comprises: obtaining home catering parameters, the home catering parameters being used to indicate material characteristics used in a home catering production process; determining, according to the home catering parameters, a calorie calculation result and / or a blood glucose measurement result of a finished dish 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 a nutritional component index in the dish that affects blood glucose level; the home catering parameters comprise raw material parameters and processed material parameters, the raw material parameters comprising the types and physical properties of raw materials, and the processed material parameters comprising the types and physical properties of processed materials, the physical properties comprising mass or volume; the home catering parameters further comprise processing procedure parameters and / or dish parameters, the processing procedure parameters being used to indicate processing procedure steps of the dish in the home catering production process, and the dish parameters comprising the types and / or physical properties of the dish; the obtaining of the home catering parameters comprises at least one of the following manners: obtaining an image of the raw material, and determining the raw material parameters through a preset first recognition model according to the image of the raw material, the first recognition model being used to recognize the raw material parameters in the image; obtaining an image of the processed material, and determining the processed material parameters through a preset second recognition model according to the image of the processed material, the second recognition model being used to recognize the processed material parameters in the image; the obtaining of the home catering parameters further comprises at least one of the following manners: obtaining a video of a processing stage in the home catering production process, and determining the processing procedure parameters through a preset third recognition model according to the video of the processing stage, the third recognition model being used to recognize the processing procedure parameters in the video; obtaining an image of the dish, and determining the dish parameters through a preset fourth recognition model according to the image of the dish, the fourth recognition model being used to recognize the dish parameters in the image; the determining, according to the home catering parameters, of the calorie calculation result of the finished dish through the preset target calculation model comprises: determining the calorie calculation result of the dish through the target calculation model according to the home catering parameters and information in a database; wherein the information in the database comprises the unit calorie of each of a plurality of raw materials and the unit calorie of each of a plurality of processed materials, and the unit calorie is the calorie contained by a unit physical property; the target calculation model comprises a first calculation model, a second calculation model, a third calculation model and a fourth calculation model, the dish comprises one or more dishes, the calorie calculation result of the dish comprises the total calorie of the dish and / or the calorie of a single dish, and the determining of the calorie calculation result of the dish through the target calculation model according to the home catering parameters and the information in the database comprises: According to the physical properties and unit heat of each of the n raw materials, a first calculation model is used to determine the total heat of the n raw materials, and according to the physical properties and unit heat of each of the m processed materials, a second calculation model is used to determine the total heat of the m processed materials, wherein n and m are positive integers; According to the total heat of the raw materials, the total heat of the processed materials and a cooking method coefficient, a third calculation model is used to determine the total heat of the dish, wherein the cooking method coefficient is preset or determined according to a processing procedure parameter; According to the total heat of the dish, the physical properties of the dish and the physical properties of the single serving dish, a fourth calculation model is used to determine the heat of the single serving dish.

2. The method of claim 1, wherein, The method further comprises: Obtaining personal information parameters, wherein the personal information parameters include physiological parameters and blood sugar information of a user; According to the personal information parameters, an ideal index of a nutritional component required by the user is estimated; Comparative analysis is performed on the ideal index of the nutritional component and the blood sugar measurement result of the dish; According to the result of the comparative analysis, feedback is determined, wherein the feedback is used to indicate personalized dietary suggestions for the user.

3. A digital information technology based home catering calorie and blood glucose monitoring system characterized in that, The system for implementing the method of any one of claims 1 or 2 comprises: An image acquisition device, which is used to acquire images in a home cooking process; A processing device, which is used to determine home cooking parameters according to the acquired images, wherein the home cooking parameters are used to indicate the characteristics of materials used in the home cooking process; according to the home cooking parameters, a target calculation model is used to determine a heat calculation result and / or a blood sugar measurement result of a dish prepared, wherein the target calculation model is used to calculate the heat of the dish and / or measure the blood sugar of the dish, the heat calculation result is used to indicate the heat of the dish, and the blood sugar measurement result is used to indicate an index of a nutritional component affecting the blood sugar level in the dish; according to the heat calculation result and / or the blood sugar measurement result, feedback is determined; A feedback device, which is used to output the feedback, wherein the feedback is used to indicate personalized dietary suggestions for a user.

4. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions are executed by a processor to implement the method of any one of claims 1 or 2. The computer program instructions are executed by a processor to implement the method of any one of claims 1 or 2.

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