Dietary management system and method based on unmanned cooker

Abstract

The invention relates to the technical field of diet management, and discloses a diet management system and method based on an unmanned cooker, and the system comprises a normal mode and a barrier-free mode. The normal mode comprises a hospital server safety connection module, a physical examination report and disease diagnosis analysis module, a real-time health assessment and diagnosis and treatment suggestion optimization module, a user demand input module, a personalized dietary scheme customization and feedback module, a networking learning module, a food material and nutrient element automatic classification module and a multi-dimensional nutrition matching optimization module. The system comprises a cooking mode pre-adjustment module, an automatic food material purchasing and distribution module, an intelligent cooking and dynamic adjustment module and a nutrition management feedback module. According to the invention, a personalized dietary scheme can be automatically generated according to the latest nutrition and medical data, the method is realized through automatic purchasing and intelligent cooking, through a barrier-free mode and an automatic food material distribution service, the operation difficulty and technical threshold of the user are reduced, and the method is suitable for wide user groups.

Description

Technical Field

[0001] The present invention relates to the technical field of dietary management, and more specifically to a dietary management system and method based on an unmanned cooking machine. Background Art

[0002] Healthy diet management has the functions and effects of balanced nutrition, promoting digestion and absorption, maintaining body functions, preventing chronic diseases, and improving immunity. It can meet the needs of daily activities of the body by taking in necessary nutrients from various foods. Reasonable combination of various ingredients in the diet can help improve the gastrointestinal environment. Healthy diet management ensures the daily intake of appropriate calories and essential vitamins, minerals, etc. to support various physiological activities, and can lower blood pressure and blood sugar levels, and reduce cardiovascular risk factors such as cholesterol; thereby effectively preventing the occurrence of related diseases, enhancing the body's defense capabilities, and resisting the invasion of foreign pathogens;

[0003] However, when conducting dietary management today, such as the intelligent medical and nutrition security system based on remote audio and video interactive technology with application number CN202210757764.X, the accuracy and applicability of diagnosis and treatment recommendations are limited. The system relies on facial complexion analysis and doctors' remote diagnosis and treatment recommendations to determine the user's nutritional needs. This method has certain accuracy and applicability issues, because the health status reflected by facial complexion may be affected by external factors such as light and shooting angle. In addition, remote diagnosis and treatment may not be as comprehensive and accurate as face-to-face consultations; the diversity of nutritional matching is limited: the system matches nutritional needs by analyzing existing ingredients, and may not provide the best nutritional combination when the types of ingredients or cooking methods are limited. Especially when faced with certain special nutritional needs, the existing food database may not cover all the necessary nutrients; users are highly dependent on technology and have a high threshold for use: the system requires users to have certain technical capabilities, such as operating smart devices and understanding nutritional distribution maps. This may cause some users to find it difficult to use;

[0004] Another example is a dietary management system and method based on an unmanned cooking machine with application number CN202210056945.X. The data source is single: the health analysis of the module relies on the traditional health index calculation method, and lacks data support from wearable devices or other physiological sensors, resulting in insufficient diversity and real-time collection of indicators; limitations of technical implementation: Since the collection and processing of real-time health data requires more complex sensing technology and algorithm support, the current analysis module may not be able to meet this high dynamic requirement; contradiction between personalization and standardization: the system attempts to find a balance between automated cooking and personalized needs, but in automated cooking, there are technical challenges in achieving personalization, especially when it comes to subtle differences in taste and taste; limitations of hardware equipment: the cooking capacity of the unmanned cooking machine is limited by hardware design and mechanical structure, and it is difficult to achieve diverse and complex cooking requirements, which affects the diversity and sophistication of the meal. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the implementation regulations of the present invention provide a meal management system and method based on an unmanned cooking machine to solve the technical problems raised in the background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a dietary management system based on an unmanned cooking machine, comprising a normal mode and a barrier-free mode, wherein the normal mode comprises a hospital server security connection module, a physical examination report and disease diagnosis and analysis module, a real-time health assessment and diagnosis and treatment suggestion optimization module, a user demand input module, a personalized dietary plan customization and feedback module, an online learning module, an ingredient and nutrient element automatic classification module, a multi-dimensional nutrient matching optimization module, a cooking method pre-adjustment module, an automatic ingredient procurement and delivery module, an intelligent cooking and dynamic adjustment module, and a nutritional management feedback module;

[0007] The hospital server security connection module establishes an encrypted communication channel with the hospital server for securely transmitting the user's physical examination report and disease diagnosis data. The physical examination report and disease diagnosis parsing module parses the physical examination report and disease diagnosis data obtained from the hospital server to extract key health indicators and disease information. The real-time health assessment and diagnosis and treatment recommendation optimization module connects to the hospital data and automatically adjusts the diagnosis and treatment recommendations and nutritional demand matching strategies in combination with the user's previous diagnosis and treatment history and physical examination report. The user demand input module is used for the user to manually input their personalized dietary needs in the system. The personalized dietary plan customization and feedback module automatically customizes a personalized dietary plan based on the parsed medical data, real-time assessment results and user input needs.

[0008] The networked learning module is connected to the professional nutrition database and the academic literature database through the API interface to obtain new food information and the latest cooking methods in real time. The food and nutrient element automatic classification module automatically classifies the food and nutrition information obtained by the networked learning module. The multi-dimensional nutrition matching optimization module optimizes the nutrition matching plan based on the user's personalized nutrition needs and the latest food and cooking data. The cooking method pre-adjustment module pre-adjusts the cooking method in the meal plan according to the latest cooking method obtained by the networked learning module. After generating a personalized meal plan, the automatic food procurement and delivery module automatically orders the required food from the shops around the user and arranges delivery services. After the food is delivered to the site, the unmanned cooking machine of the intelligent cooking and dynamic adjustment module automatically cooks nutritious food according to the generated personalized meal plan. After the meal is completed and consumed by the user, the nutrition management feedback module prompts the user to evaluate and provide feedback on the nutritional effect, taste and cooking method of the meal.

[0009] In a preferred embodiment, the physical examination report and disease diagnosis analysis module calculates the user's health assessment value P, and the calculation formula of the health assessment value P is: XT is the user's blood sugar data, DG is the user's cholesterol data, XY is the user's blood pressure data, TZ is the user's body fat percentage, XF is the user's cardiopulmonary function ventilation data, and XZ is the correction coefficient. The physical examination report and disease diagnosis and analysis module calculates the user's health assessment value P and provides it to the user and the real-time health assessment and treatment recommendation optimization module.

[0010] In a preferred embodiment, the hospital server security connection module uses the SSL / TLS protocol for encryption, and the real-time health assessment and treatment recommendation optimization module receives the health assessment value P and the physical examination report and disease diagnosis data in the disease diagnosis analysis module. The real-time health assessment and treatment recommendation optimization module compares the health assessment value P with its internal threshold value Y. When the health assessment value P>threshold value Y, the real-time health assessment and treatment recommendation optimization module recommends low-sugar and high-fiber foods. When the health assessment value P≤threshold value Y, the real-time health assessment and treatment recommendation optimization module does not recommend low-sugar and high-fiber foods, and the real-time health assessment and treatment recommendation optimization module makes corresponding food recommendations for the physical examination report and disease diagnosis data.

[0011] In a preferred embodiment, the user demand input module allows the user to manually input personalized needs, and the user demand input module dietary preferences, allergy information, dietary goals and taste preferences, and the personalized dietary plan customization and feedback module integrates the data in the user demand input module, the data in the multidimensional nutrition matching optimization module and the data in the real-time health assessment and treatment advice optimization module to generate a personalized dietary plan.

[0012] In a preferred embodiment, the online learning module obtains the nutritional components of each ingredient under different cooking methods, and when the network is limited and the data source is unstable, the online learning module cooperates with local nutritionists to manually update the database, and the nutrition management feedback module provides feedback through scoring, voice feedback and text input.

[0013] In a preferred embodiment, the barrier-free mode includes a barrier-free interface module and a personalized barrier-free setting module. The barrier-free interface module includes a simplified interface module, a voice interaction module, a visual assistance module and a tactile feedback module. After the user selects the barrier-free mode, the simplified interface module will automatically switch to a simplified interface layout, highlighting key functions and hiding complex options, and the buttons and texts within the interface elements will become larger.

[0014] In a preferred embodiment, the voice interaction module introduces a voice assistant to achieve human-computer interaction through voice commands and voice feedback. Users can perform health assessments, dietary recommendations, and data query operations through voice, and the system will also provide voice feedback and guide users on the next step.

[0015] In a preferred embodiment, the visual assistance module provides visual assistance functions, the interface changes to a high-contrast mode and a magnifying glass and color inversion, helping users with poor vision to better browse the interface content, and the visual assistance module increases the compatibility of screen readers so that the system can automatically read the interface text. When the user operates the interface, the tactile feedback module provides tactile response through vibration feedback. In the personalized accessibility setting module, the user can adjust the interface function according to his or her own needs in the accessibility settings.

[0016] In a preferred embodiment, the unmanned cooking machine accepts the meal plan in the personalized meal plan customization and feedback module and prepares the meal. The unmanned cooking machine is connected to the nutrition database of the online learning module to obtain the latest ingredients, cooking methods and nutritional composition data in real time and cook.

[0017] Technical effects and advantages of the present invention:

[0018] 1. The present invention can automatically generate personalized dietary plans based on the latest nutritional and medical data, and implement them through automated procurement and intelligent cooking. Through barrier-free mode and automatic food delivery services, the user's operating difficulty and technical threshold are reduced, and it is suitable for a wide range of user groups. It can not only update the types of ingredients and cooking methods in real time, but also make personalized adjustments based on the user's health data. It has strong adaptability and scalability, and realizes comprehensive nutritional management, intelligent food cooking and convenient user experience;

[0019] 2. When making food recommendations, the present invention not only makes corresponding food recommendations based on physical examination reports and disease diagnosis data, but also comprehensively considers blood sugar data XT, cholesterol data DG, blood pressure data XY and cardiopulmonary function ventilation data. The calculated health assessment value P can more accurately reflect the user's own physical condition, thereby ensuring that the user can be in a relatively healthy state for a long time and avoiding physical problems of the user. The present application can perform dietary management more comprehensively. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the module structure of the present invention in normal mode.

[0021] Figure 2 It is a schematic diagram of the module structure in the barrier-free mode of the present invention.

[0022] Figure 3 It is a schematic diagram of the process of the present invention.

[0023] Figure 4 Schematic diagram of the dietary management method of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are only examples. The dietary management system and method based on an unmanned cooking machine involved in the present invention are not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0025] Reference Figure 1The present invention provides a dietary management system based on an unmanned cooking machine, including a normal mode and a barrier-free mode. The normal mode includes a hospital server security connection module, a physical examination report and disease diagnosis and analysis module, a real-time health assessment and diagnosis and treatment suggestion optimization module, a user demand input module, a personalized dietary plan customization and feedback module, an online learning module, an ingredient and nutrient element automatic classification module, a multi-dimensional nutrition matching optimization module, a cooking method pre-adjustment module, an automatic ingredient procurement and distribution module, an intelligent cooking and dynamic adjustment module, and a nutrition management feedback module; the hospital server security connection module establishes an encrypted communication channel with the hospital server for securely transmitting the user's physical examination report and disease diagnosis data, the physical examination report and disease diagnosis and analysis module parses the physical examination report and disease diagnosis data obtained from the hospital server to extract key health indicators and disease information, the real-time health assessment and diagnosis and treatment suggestion optimization module connects to the hospital data, and automatically adjusts the diagnosis and treatment suggestions and nutrition demand matching strategies in combination with the user's previous diagnosis and treatment history and physical examination report, the user demand input module is used for the user to manually input his personalized dietary needs in the system, and the personalized The meal plan customization and feedback module automatically customizes personalized meal plans based on the parsed medical data, real-time evaluation results and user input requirements; the online learning module is connected to the professional nutrition database and the academic literature database through the API interface to obtain new food information and the latest cooking methods in real time; the food and nutrient element automatic classification module automatically classifies the food and nutrition information obtained by the online learning module; the multi-dimensional nutrition matching optimization module optimizes the nutrition matching plan based on the user's personalized nutrition needs and the latest food and cooking data; the cooking method pre-adjustment module pre-adjusts the cooking method in the meal plan based on the latest cooking method obtained by the online learning module; the automatic food procurement and delivery module automatically orders the required food from the shops around the user after generating a personalized meal plan and arranges delivery services; the intelligent cooking and dynamic adjustment module automatically cooks the nutritious food according to the generated personalized meal plan by the unmanned cooking machine after the food is delivered in place; the nutrition management feedback module prompts the user to evaluate and give feedback on the nutritional effect, taste and cooking method of the meal after the meal is completed and consumed by the user.

[0026] In the embodiment of the present application, a secure connection with the hospital server is first established, and the user's medical data such as physical examination reports and disease diagnosis books are acquired in real time and integrated into the diet management and nutrition guarantee system, so that the system's diagnosis and treatment suggestions and nutrition plans are more personalized and accurate. The latest types of ingredients and cooking methods are learned through networking, and the ingredient database is dynamically updated to cover all necessary nutritional elements as much as possible, realizing a fully automated process from nutritional data learning, medical data integration to automatic ingredient procurement and cooking, and also providing a barrier-free user operation interface, which is convenient for different users to use;

[0027] In the hospital server security connection module, the hospital uses a virtual private network VPN to establish a secure connection. VPN connects the device to the remote server by using an encrypted tunnel. At this time, the user's physical examination report and disease diagnosis data are protected by security parameters to ensure the security and privacy of data transmission. The physical examination report and disease diagnosis analysis module extracts the user's blood sugar data, cholesterol data, blood pressure data and body fat rate, and the physical examination report and disease diagnosis analysis module extracts the user's disease information separately. The real-time health assessment and diagnosis and treatment recommendation optimization module receives the user's previous diagnosis and treatment history and physical examination report in the hospital, extracts the user's latest diagnosis and treatment recommendations, and performs real-time health assessment of the user based on the user's latest diagnosis and treatment recommendations. During health assessment, the user's blood sugar data, cholesterol data, blood pressure data and body fat rate can be used for assessment, and then nutritional needs matching and adjustment can be made based on the customer's real-time health assessment results. For example, when the customer's body fat rate is high, the user's fat intake can be reduced, and the user's vitamin and dietary fiber intake can be increased. The personalized meal plan customization and feedback module receives the user's medical data, which includes the user's own body data, and customizes the meal plan based on the customer's body data. After customization, the user inputs the needs based on the customization results, and customizes the meal plan again until the customer is satisfied. When customizing the meal plan, the results of the customization of the meal plan will be evaluated to understand the time of meal plan customization and the customer's satisfaction.

[0028] The automatic classification module for ingredients and nutrients divides ingredients into six categories, namely cereals, potatoes, animal foods, beans and nuts, vegetables and fruits, and pure energy foods. Cereals include rice, noodles and various grains, which mainly provide carbohydrates and some proteins. Potatoes include potatoes, sweet potatoes, etc., which are rich in starch and dietary fiber. Animal foods include meat, dairy products and eggs, etc., which mainly provide protein, fat and a variety of vitamins and minerals. Among beans and nuts, beans provide plant protein, and nuts contain healthy fatty acids. Vegetables and fruits provide vegetables and fruits that are rich in vitamins, minerals and dietary fiber. Pure energy foods include sugar, oil and wine, etc., which mainly provide energy. Different ingredients are classified, and each type of ingredient provides different nutrients. After understanding the customer's own personalized nutritional needs, the multi-dimensional nutrition matching optimization module understands the demand for each nutrient element, and obtains the weight of each ingredient based on the demand, so as to automatically select the ingredients. After selecting the ingredients, if the customer does not like a certain ingredient, it can be eliminated, which can meet the application needs while meeting the customer's taste requirements.

[0029] Reference Figure 1 The physical examination report and disease diagnosis analysis module calculates the user's health assessment value P. The calculation formula of the health assessment value P is: XT is the user's blood sugar data, DG is the user's cholesterol data, XY is the user's blood pressure data, TZ is the user's body fat percentage, XF is the user's cardiopulmonary function ventilation data, and XZ is the correction coefficient. The physical examination report and disease diagnosis analysis module calculates the user's health assessment value P and provides it to the user and the real-time health assessment and diagnosis and treatment advice optimization module. The hospital server security connection module uses the SSL / TLS protocol for encryption. The real-time health assessment and diagnosis and treatment advice optimization module receives the health assessment value P and the physical examination report and disease diagnosis data in the physical examination report and disease diagnosis analysis module. The real-time health assessment and diagnosis and treatment advice optimization module compares the health assessment value P with its internal threshold value Y. When the health assessment value P>threshold value Y, the real-time health assessment and diagnosis and treatment advice optimization module recommends low-sugar and high-fiber foods. When the health assessment value P≤threshold value Y, the real-time health assessment and diagnosis and treatment advice optimization module does not recommend low-sugar and high-fiber foods, and the real-time health assessment and diagnosis and treatment advice optimization module makes corresponding food recommendations for the physical examination report and disease diagnosis data.

[0030] In the embodiment of the present application, making corresponding food recommendations based on the physical examination report and disease diagnosis data is a routine recommendation of the current management system. For example, when blood sugar is high, low-sugar and high-fiber foods are recommended, and when cholesterol is high, high-protein foods are recommended. The present application does not make detailed limitations on this. When making food recommendations, the present application, in addition to making corresponding food recommendations based on the physical examination report and disease diagnosis data, comprehensively considers blood sugar data XT, cholesterol data DG, blood pressure data XY and cardiopulmonary function ventilation data. The calculated health assessment value P can more accurately reflect the user's own physical condition. For example, although the patient's blood sugar or cholesterol data are within a normal range, his cardiopulmonary function ventilation data is relatively low. At this time, when the user consumes some high-sugar foods, it will also bring a greater burden to the body. Therefore, low-sugar foods will also be recommended, thereby ensuring that the user can be in a relatively healthy state for a long time and avoiding physical problems of the user. The present application can perform dietary management more comprehensively.

[0031] Reference Figure 1 The user demand input module allows the user to manually input personalized needs, and the user demand input module includes dietary preferences, allergy information, dietary goals and taste preferences, and the personalized dietary plan customization and feedback module integrates the data in the user demand input module, the data in the multidimensional nutrition matching optimization module and the data in the real-time health assessment and treatment advice optimization module to generate a personalized dietary plan.

[0032] Reference Figure 1When making personalized needs, dietary preferences such as vegetarian, low-fat diet, gluten-free diet, etc.; allergy information such as allergy information of specific ingredients such as nuts, seafood, dairy products, etc.; dietary goals such as weight loss, muscle gain, maintaining blood sugar stability, etc.; taste preferences such as spicy, sour, salty and other personalized taste requirements. This module allows users to manually input personalized needs, making the system's meal recommendations more personalized and more convenient to use.

[0033] Reference Figure 1 The online learning module obtains the nutritional components of each ingredient under different cooking methods, and when the network is limited and the data source is unstable, the online learning module cooperates with the local nutritionist to manually update the database. The nutrition management feedback module provides feedback through scoring, voice feedback and text input. The online learning module can be updated in real time, and can also be updated manually when the network is unable to connect to the Internet to ensure the accuracy of the dietary data. The nutrition management feedback module is provided with different feedback methods and different feedback contents, which is convenient for automatically adjusting the cooking method or ingredient combination in subsequent recommendations. The feedback methods include nutritional effects such as physical feelings after eating, satiety, physical state, etc., taste satisfaction such as whether the taste meets expectations, the texture and freshness of the ingredients, etc., and the adaptability of the cooking method such as whether the cooking time is too long or too short, whether the seasoning is appropriate, etc.

[0034] Reference Figure 2 The barrier-free mode includes a barrier-free interface module and a personalized barrier-free setting module. The barrier-free interface module includes a simplified interface module, a voice interaction module, a visual assistance module and a tactile feedback module. After the user selects the barrier-free mode, the simplified interface module will automatically switch to a simplified interface layout, highlighting key functions and hiding complex options, and the buttons and texts in the interface elements will become larger.

[0035] In the embodiment of the present application, in the barrier-free mode, the user experience of the system is optimized and the usage threshold is lowered by simplifying the interface, voice guidance, visual assistance, and personalized adjustment functions. This solution will enable users to easily obtain the system's services and support without having a high level of technical skills, so that different fixed users can use it.

[0036] Reference Figure 2The voice interaction module introduces a voice assistant to achieve human-computer interaction through voice commands and voice feedback. Users can perform health assessments, dietary recommendations, and data query operations through voice. The system will also provide voice feedback and guide the user's next operation. The visual assistance module provides visual assistance functions. The interface changes to a high-contrast mode and a magnifying glass and color inversion to help users with poor eyesight better browse the interface content. The visual assistance module increases the compatibility of screen readers so that the system can automatically read the interface text. When the user operates the interface, the tactile feedback module provides tactile response through vibration feedback. In the personalized barrier-free setting module, users can adjust the interface function according to their own needs in the barrier-free setting. The unmanned cooking machine accepts the personalized meal plan customization and the meal plan in the feedback module and prepares the meal. The unmanned cooking machine is connected to the nutrition database of the online learning module to obtain the latest ingredients, cooking methods, and nutritional composition data in real time and cook.

[0037] In the embodiment of the present application, the voice interaction module reduces the user's dependence on the visual interface and touch operation, which is particularly suitable for visually impaired users and users who are not proficient in using touch screen devices, and improves the visual accessibility of the system, which is particularly suitable for users with limited vision or color blindness.

[0038] Reference Figure 2 The unmanned cooking machine accepts personalized meal plan customization and meal plan in the feedback module and prepares meals. The unmanned cooking machine is connected to the nutrition database of the online learning module to obtain the latest ingredients, cooking methods and nutritional composition data in real time and cook. The unmanned cooking machine is an intelligent meal management system based on the integration of multiple innovative technologies. It combines dynamic learning of nutrition data, real-time integration of medical data, automatic food procurement and distribution, and barrier-free operation modes that meet the needs of different users. The unmanned cooking machine realizes intelligent and personalized nutrition management and automatic cooking functions.

[0039] Reference Figure 3 and Figure 4 , a dietary management method based on an unmanned cooking machine, comprising the following steps:

[0040] Step S1: Log in through the operation interface of the unmanned cooking machine or the mobile application, and automatically read the latest health data and dietary preference settings from the user's personal account;

[0041] Step S2, connecting with the hospital server, extracting health indicators from the user's physical examination records, and generating a current health status report;

[0042] Step S3: Ensure that the food material database and nutritional content are up to date through online learning;

[0043] Step S4: Generate a dietary plan suitable for the user based on the user's health data and input needs;

[0044] Step S5: After the meal plan is determined, an order is generated based on the required ingredients and placed with surrounding shops, and delivery service is arranged;

[0045] Step S6: After the ingredients are delivered, the unmanned cooking machine automatically adjusts the cooking parameters according to the cooking method of the meal plan and starts cooking;

[0046] Step S7: After the meal is completed, the user is prompted to rate or provide text feedback on the nutritional effect and taste, and optimization is performed based on the user's feedback.

[0047] In the embodiment of the present application, after logging in, the user's data will be automatically read, and the data will be read using an API interface. After the data is read, it will be connected to the hospital's remote server through an encrypted tunnel. At this time, the customer's physical examination records can be obtained from the hospital, and health indicators can be obtained based on the physical examination records. Health indicators are obtained by feature extraction. Feature extraction types include blood sugar data, cholesterol data, blood pressure data, and body fat rate. Network learning is performed using a web crawler to automatically obtain the latest food database, nutritional components, and cooking methods. At this time, the food database and nutritional components are matched with the extracted user health indicators. At this time, a meal plan is generated. When generating a meal plan, the customer uses The unmanned cooking machine performs manual demand setting. After the setting, a meal plan is generated at this time. After the meal plan is produced, the web crawler is used again to obtain the surrounding food information. After obtaining the surrounding food information, an order is placed for the food. After waiting for the food to be delivered, it is automatically cooked according to the cooking method that has been obtained. After cooking, data for scoring or text feedback is collected, the collected data is summarized, and then processed and optimized after summary. When processing the collected data, the cooking effect is understood according to the score, and the text feedback during the scoring is collected. If a low score appears, the text feedback is blurred. At this time, optimization can be performed. The optimization direction is to reduce the cooking time or lower the cooking temperature next time, so as to achieve optimization based on user feedback.

[0048] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The units and algorithm steps of each example described in the embodiments can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0050] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0051] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dietary management system based on an unmanned cooking machine, characterized in that: It includes a normal mode and an accessible mode. The normal mode includes a hospital server security connection module, a physical examination report and disease diagnosis and analysis module, a real-time health assessment and treatment recommendation optimization module, a user demand input module, a personalized meal plan customization and feedback module, an online learning module, an ingredient and nutrient element automatic classification module, a multi-dimensional nutrition matching optimization module, a cooking method pre-adjustment module, an automatic ingredient procurement and delivery module, an intelligent cooking and dynamic adjustment module, and a nutrition management feedback module; The hospital server security connection module establishes an encrypted communication channel with the hospital server for securely transmitting the user's physical examination report and disease diagnosis data. The physical examination report and disease diagnosis parsing module parses the physical examination report and disease diagnosis data obtained from the hospital server to extract key health indicators and disease information. The real-time health assessment and diagnosis and treatment recommendation optimization module connects to the hospital data and automatically adjusts the diagnosis and treatment recommendations and nutritional demand matching strategies in combination with the user's previous diagnosis and treatment history and physical examination report. The user demand input module is used for the user to manually input their personalized dietary needs in the system. The personalized dietary plan customization and feedback module automatically customizes a personalized dietary plan based on the parsed medical data, real-time assessment results and user input needs. The networked learning module is connected to the professional nutrition database and the academic literature database through the API interface to obtain new food information and the latest cooking methods in real time. The food and nutrient element automatic classification module automatically classifies the food and nutrition information obtained by the networked learning module. The multi-dimensional nutrition matching optimization module optimizes the nutrition matching plan based on the user's personalized nutrition needs and the latest food and cooking data. The cooking method pre-adjustment module pre-adjusts the cooking method in the meal plan according to the latest cooking method obtained by the networked learning module. After generating a personalized meal plan, the automatic food procurement and delivery module automatically orders the required food from the shops around the user and arranges delivery services. After the food is delivered to the site, the unmanned cooking machine of the intelligent cooking and dynamic adjustment module automatically cooks nutritious food according to the generated personalized meal plan. After the meal is completed and consumed by the user, the nutrition management feedback module prompts the user to evaluate and provide feedback on the nutritional effect, taste and cooking method of the meal.

2. A dietary management system based on an unmanned cooking machine according to claim 1, characterized in that: The physical examination report and disease diagnosis analysis module calculates the user's health assessment value P. The calculation formula of the health assessment value P is: XT is the user's blood sugar data, DG is the user's cholesterol data, XY is the user's blood pressure data, TZ is the user's body fat percentage, XF is the user's cardiopulmonary function ventilation data, and XZ is the correction coefficient. The physical examination report and disease diagnosis and analysis module calculates the user's health assessment value P and provides it to the user and the real-time health assessment and treatment recommendation optimization module.

3. A dietary management system based on an unmanned cooking machine according to claim 2, characterized in that: The hospital server security connection module uses the SSL / TLS protocol for encryption, the real-time health assessment and treatment recommendation optimization module receives the health assessment value P and the physical examination report and disease diagnosis data in the disease diagnosis analysis module, the real-time health assessment and treatment recommendation optimization module compares the health assessment value P with its internal threshold value Y, when the health assessment value P>threshold value Y, the real-time health assessment and treatment recommendation optimization module recommends low-sugar and high-fiber foods, when the health assessment value P≤threshold value Y, the real-time health assessment and treatment recommendation optimization module does not recommend low-sugar and high-fiber foods, and the real-time health assessment and treatment recommendation optimization module makes corresponding food recommendations for the physical examination report and disease diagnosis data.

4. A dietary management system based on an unmanned cooking machine according to claim 1, characterized in that: The user demand input module allows the user to manually input personalized needs, and the user demand input module dietary preferences, allergy information, dietary goals and taste preferences, and the personalized dietary plan customization and feedback module integrates the data in the user demand input module, the data in the multidimensional nutrition matching optimization module and the data in the real-time health assessment and treatment advice optimization module to generate a personalized dietary plan.

5. The dietary management system based on the unmanned cooking machine according to claim 1, characterized in that: The online learning module obtains the nutritional components of each ingredient under different cooking methods, and when the network is limited and the data source is unstable, the online learning module cooperates with local nutritionists to manually update the database. The nutrition management feedback module provides feedback through scoring, voice feedback and text input.

6. The dietary management system based on an unmanned cooking machine according to claim 1, characterized in that: The barrier-free mode includes a barrier-free interface module and a personalized barrier-free setting module. The barrier-free interface module includes a simplified interface module, a voice interaction module, a visual assistance module and a tactile feedback module. After the user selects the barrier-free mode, the simplified interface module will automatically switch to a simplified interface layout, highlighting key functions and hiding complex options, and the buttons and texts within the interface elements will become larger.

7. A dietary management system based on an unmanned cooking machine according to claim 6, characterized in that: The voice interaction module introduces a voice assistant to achieve human-computer interaction through voice commands and voice feedback. Users can perform health assessments, dietary recommendations, and data query operations through voice, and the system will also provide voice feedback and guide users on the next step.

8. The dietary management system based on the unmanned cooking machine according to claim 6, characterized in that: The visual assistance module provides visual assistance functions, the interface changes to high contrast mode and uses a magnifying glass and color inversion to help users with poor vision better browse the interface content. The visual assistance module increases the compatibility of screen readers, allowing the system to automatically read the interface text. When the user operates the interface, the tactile feedback module provides tactile response through vibration feedback. In the personalized accessibility setting module, the user can adjust the interface function according to his or her own needs in the accessibility settings.

9. The dietary management system based on an unmanned cooking machine according to claim 1, characterized in that: The unmanned cooking machine accepts personalized meal plan customization and meal plan in the feedback module and prepares meals. The unmanned cooking machine is connected to the nutrition database of the online learning module to obtain the latest ingredients, cooking methods and nutritional content data in real time and cook.

10. A dietary management method based on an unmanned cooking machine, characterized in that: A dietary management system based on an unmanned cooking machine as described in any one of claims 1 to 9, comprising the following steps: Step S1: Log in through the operation interface of the unmanned cooking machine or the mobile application, and automatically read the latest health data and dietary preference settings from the user's personal account; Step S2, connecting with the hospital server, extracting health indicators from the user's physical examination records, and generating a current health status report; Step S3: Ensure that the food material database and nutritional content are up to date through online learning; Step S4: Generate a dietary plan suitable for the user based on the user's health data and input needs; Step S5: After the meal plan is determined, an order is generated based on the required ingredients and placed with surrounding shops, and delivery service is arranged; Step S6: After the ingredients are delivered, the unmanned cooking machine automatically adjusts the cooking parameters according to the cooking method of the meal plan and starts cooking; Step S7: After the meal is completed, the user is prompted to rate or provide text feedback on the nutritional effect and taste, and optimization is performed based on the user's feedback.

Images