A system and method for daily diet statistics for pregnant women with gestational diabetes

Abstract

The application provides a daily diet statistics system and method for pregnant women with gestational diabetes mellitus, which comprises a user authentication and basic information module, a smart diet input module, a daily nutrition statistics module, a personalized goal and evaluation module, a blood glucose and diet linkage module, and a reminding module.

Description

Technical Field

[0001] This application relates to the technical field of dietary management for gestational diabetes mellitus, specifically to a system and method for daily dietary statistics of pregnant women with gestational diabetes mellitus. Background Technology

[0002] Gestational diabetes mellitus (GDM) is a common complication of pregnancy, characterized primarily by abnormal glucose metabolism during pregnancy. Poor blood glucose control can easily lead to adverse outcomes such as macrosomia (large baby), premature birth, preeclampsia, increased cesarean section rates, and an increased risk of long-term maternal type 2 diabetes. Medical nutrition therapy is a first-line intervention for GDM, with core requirements including: regular and quantitative meals, frequent small meals, precise carbohydrate control, balanced nutrition, postprandial blood glucose monitoring, and long-term adherence to daily dietary records and nutrient analysis.

[0003] The current GDM diet management and recording system has the following pain points:

[0004] 1. Manual recording is tedious and has poor adherence: Pregnant women need to manually record food names, weights, meal times, cooking methods, and calculate carbohydrates, protein, fat, energy, and GI / GL values. The process is complicated, prone to errors, and difficult to maintain in the long term.

[0005] 2. Lack of GDM-specific rules: General diet apps do not adapt to physiological changes during pregnancy, energy requirements at different gestational weeks, GDM glucose control thresholds, and food exchange methods, and cannot generate personalized goals that conform to obstetric and clinical nutrition guidelines;

[0006] 3. Lack of data loop: Dietary data is disconnected from blood glucose, weight, exercise, and prenatal checkup data, making it impossible to form a dynamic feedback loop of "dietary intake - blood glucose fluctuation - treatment plan adjustment", which makes it difficult for doctors to intervene remotely and accurately;

[0007] 4. Insufficient intelligent recognition and convenience: Most apps rely on manual input and lack functions such as food image recognition, voice input, and QR code recognition, resulting in a high operating threshold; they also lack intelligent reminders, meal planning, abnormal warnings, and collaboration capabilities with doctors.

[0008] Weak statistical and follow-up capabilities: Unable to automatically generate daily / weekly / monthly dietary statistics reports, nutrient compliance rates, and meal distribution curves; does not support the export of clinical research data and individualized follow-up management. Summary of the Invention

[0009] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and provide a daily diet statistics system and method for pregnant women with gestational diabetes, which can improve the convenience of daily diet statistics and generate diet reminders based on dietary intake data and the resulting blood sugar changes, and is suitable for home diet management of pregnant women with gestational diabetes.

[0010] The first aspect of this application provides a daily diet statistics system for pregnant women with gestational diabetes, including:

[0011] The user authentication and basic information module is used to calculate dietary intake reference data based on the pregnant woman's physiological information, dietary preferences, activity data, blood glucose control targets and GDM clinical guidelines.

[0012] The GDM diet database module is used to store food data and pregnancy nutrition data. The food data includes nutritional data for various foods, GDM-specific labels, and food exchange rules. The pregnancy nutrition data includes the content of nutrients required during pregnancy.

[0013] The intelligent diet input module is used to input the pregnant woman's dietary information for each meal and analyze the dietary intake data for each meal.

[0014] The daily nutrition statistics module is used to collect data on the daily intake of multiple meals to obtain daily dietary intake data.

[0015] The personalized goal and assessment module is used to generate dietary intake assessment results based on the food intake data, the daily food intake data, the dietary intake reference data, and preset assessment rules.

[0016] The blood glucose and diet linkage module is used to generate a combined blood glucose and diet report based on the target pregnant woman's postprandial blood glucose data and food intake data.

[0017] The reminder module is used to generate dietary reminders based on the dietary intake assessment results and the combined report.

[0018] As one implementation method, the dietary intake reference data includes: total daily energy, carbohydrate target, and carbohydrate limit per meal;

[0019] Daily total energy = Basal energy × Activity factor + Pregnancy week-related energy supplement;

[0020] Carbohydrate target = Total energy × 40%–50% / 4;

[0021] The recommended carbohydrate intake per meal is 30–45g for a main meal and 15–30g for a snack.

[0022] As one implementation method, the input method of the intelligent diet input module includes:

[0023] Quick text selection: Favorite food items, one-click reuse of history records, and default meal templates;

[0024] Image recognition and input: Take a photo of food, and automatically identify the type and weight through target detection, classification, volume and weight conversion;

[0025] Voice input: Use voice to convert text to search for food items and confirm portion sizes;

[0026] Scan to enter: Scan the barcode of pre-packaged food to obtain the nutrition facts label;

[0027] Manual additions: Customize food, cooking methods, serving time, and portion size.

[0028] As one implementation, the daily nutrition statistics module is also used to generate visual charts based on the daily dietary intake data.

[0029] As one implementation method, the types of daily dietary intake data include total energy, carbohydrates, protein, fat, dietary fiber, sodium, mean GI, total GL, meal distribution, and peak carbohydrate intake periods.

[0030] In one implementation, the daily nutrition statistics module is also used to obtain cumulative dietary intake data based on multiple food intake data accumulated on the day, and to generate a reference for the remaining dietary intake on the day in combination with the dietary intake reference data.

[0031] As one implementation method, the evaluation rules include:

[0032] Carbohydrate compliance: Daily dietary intake data of carbohydrates ≤ Daily carbohydrate intake target value of dietary intake reference data, and meal carbohydrate intake data ≤ Meal carbohydrate intake limit of dietary intake reference data;

[0033] Low GI requirement met: Low GI foods accounted for ≥70% of dietary intake data;

[0034] Regular meal schedule: Daily meals ≥ the reference number of meals according to dietary intake reference data, with meal intervals of 3–4 hours.

[0035] As one implementation method, the assessment rules also include multiple risk items, which at least include: excessive high-GI intake of food in the meal intake data, excessive carbohydrate intake of food in the meal intake data or daily dietary intake data, excessive fat intake of food in the meal intake data, and missed snacks.

[0036] As one implementation, the system also includes a doctor-patient collaboration module, which is used to communicate and interact with the doctor's end. The communication and interaction content includes the food intake data, the daily food intake data, the food intake assessment results, the postprandial blood glucose data and the joint report, consultation questions and answers or medical suggestions issued by the doctor's end.

[0037] Compared to related technologies, the daily dietary statistics system for pregnant women with gestational diabetes mellitus (GDM) of this application comprises multiple functional modules working collaboratively, including a user authentication and basic information module, a GDM dietary database, an intelligent dietary input module, a daily nutrition statistics module, a personalized goal and assessment module, a blood glucose and diet linkage module, a reminder module, and a doctor-patient collaboration module. It uses "GDM clinical guidelines as the core, daily statistics as the main thread, intelligent input as the entry point, blood glucose linkage as the closed loop, and doctor-patient collaboration as the extension" to solve problems such as cumbersome manual recording, lack of personalization, data fragmentation, and statistical difficulties, achieving standardized, intelligent, and convenient dietary management for pregnant women with GDM. Furthermore, because the collaborative work of multiple functional modules covers the entire closed-loop management process from data collection to clinical intervention, it can significantly improve the efficiency and compliance of dietary management for pregnant women with GDM.

[0038] A second aspect of this application provides a method for tracking the daily diet of pregnant women with gestational diabetes, including:

[0039] Based on the pregnant women's physiological information, dietary preferences, activity data, glycemic control targets, and GDM clinical guidelines, dietary intake reference data are calculated.

[0040] The system stores food data and pregnancy nutrition data. The food data includes nutritional information for various foods, GDM-specific labels, and food exchange rules. The pregnancy nutrition data includes the content of nutrients required during pregnancy.

[0041] Enter the pregnant woman's food information for each meal and analyze the food intake data for each meal;

[0042] By statistically analyzing the daily food intake data from multiple meals, we can obtain daily food intake data.

[0043] Based on the dietary intake data, the daily dietary intake data, the dietary intake reference data, and the preset evaluation rules, a dietary intake evaluation result is generated;

[0044] The system generates a combined report on blood glucose and diet based on the postprandial blood glucose and dietary intake data of the target pregnant women.

[0045] Based on the dietary intake assessment results and the combined report, a dietary reminder is generated.

[0046] Compared to related technologies, the daily dietary statistics method for pregnant women with gestational diabetes mellitus (GDM) proposed in this application, with "GDM clinical guidelines as the core, daily statistics as the main line, intelligent data entry as the entry point, blood glucose linkage as the closed loop, and doctor-patient collaboration as the extension," solves the problems of cumbersome manual recording, lack of personalization, data fragmentation, and statistical difficulties, achieving standardized, intelligent, and convenient dietary management for pregnant women with GDM. Furthermore, it covers the entire closed-loop management process from data collection to clinical intervention, which can significantly improve the efficiency and compliance of dietary management for pregnant women with GDM.

[0047] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the module connections of a daily diet statistics system for pregnant women with gestational diabetes according to an embodiment of this application.

[0049] Figure 2 This is a schematic diagram illustrating the interaction between a daily diet statistics system for pregnant women with gestational diabetes and a doctor, according to one embodiment of this application.

[0050] Figure 3 This is a flowchart illustrating a method for tracking the daily diet of pregnant women with gestational diabetes, according to one embodiment of this application.

[0051] 100. Daily Diet Statistics System for Pregnant Women with Gestational Diabetes; 101. User Authentication and Basic Information Module; 102. GDM Diet Database; 103. Intelligent Diet Input Module; 104. Daily Nutrition Statistics Module; 105. Personalized Goals and Assessment Module; 106. Blood Glucose and Diet Linkage Module; 107. Reminder Module; 108. Doctor-Patient Collaboration Module. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0053] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0054] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."

[0055] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0056] Please see Figure 1 , Figure 1 This is a schematic diagram of the module connections of a daily diet statistics system 100 for pregnant women with gestational diabetes according to an embodiment of this application. The daily diet statistics system 100 for pregnant women with gestational diabetes is applied to a user terminal or a pregnant woman terminal, and the system includes:

[0057] The user authentication and basic information module 101 is used to calculate dietary intake reference data based on the pregnant woman's physiological information, dietary preferences, activity data, blood glucose control targets and GDM clinical guidelines.

[0058] Specifically, the user authentication and basic information module 101 is used for pregnant women to register / log in to their accounts, verify their identity, and collect basic information, including age, height, pre-pregnancy weight, gestational age, number of fetuses, gestational age at GDM diagnosis, blood glucose control target, allergy history, dietary preferences, activity level, and automatically calculate BMI, total daily energy, macronutrient allocation, and carbohydrate limit.

[0059] Daily total energy = Basal energy × Activity factor + Pregnancy week-related energy supplement;

[0060] Carbohydrate target = Total energy × 40%–50% / 4;

[0061] The recommended carbohydrate intake per meal is 30–45g for a main meal and 15–30g for a snack.

[0062] Module 102 of the GDM diet database is used to store food data and pregnancy nutrition data. The food data includes nutritional data for various foods, GDM-specific labels, and food exchange rules. The pregnancy nutrition data includes the content of nutrients required during pregnancy.

[0063] The GDM diet database module 102 stores data through multiple databases, including: ① Basic food database: covering staple foods, meat and eggs, seafood, dairy products, soy products, vegetables, fruits, oils, snacks, and condiments, labeled with energy, carbohydrates, protein, fat, dietary fiber, sodium, GI value, and GL value per 100g edible portion; ② GDM exclusive labels: low GI preferred, foods to be eaten with caution, foods to avoid, meal portioning suggestions, and cooking method impact coefficient; ③ Food exchange portion database: categorized by grains and tubers, fruits and vegetables, meat and beans, dairy products, and oils, matching commonly used GDM exchange portion rules; ④ Pregnancy nutrient database: content of key nutrients such as folic acid, iron, calcium, and DHA to meet the needs of fetal development.

[0064] The GDM clinical guidelines include the "Chinese Guidelines for the Diagnosis and Treatment of Gestational Diabetes" and the "Dietary Guidelines for Pregnant Women." These guidelines can automatically generate daily references based on gestational age, weight, activity level, and blood glucose control goals. For example, daily references include: total energy, carbohydrates 40%–50%, protein 15%–20%, and fat 25%–30%; carbohydrate limit per meal: 30–45g for main meals and 15–30g for snacks; low-GI foods ≥70%; and corresponding daily limits for fruit, fat, and water intake.

[0065] The intelligent diet input module 103 is used to input the pregnant woman's diet information for each meal and analyze the diet intake data for each meal.

[0066] The input methods of the intelligent diet input module 103 include:

[0067] Quick text selection: Favorite food items, one-click reuse of history records, and default meal templates;

[0068] Image recognition and input: Take a photo of food, and automatically identify the type and weight through target detection, classification, volume and weight conversion;

[0069] Voice input: Use voice to convert text to search for food items and confirm portion sizes;

[0070] Scan to enter: Scan the barcode of pre-packaged food to obtain the nutrition facts label;

[0071] Manual additions: Customize food, cooking methods, serving time, and portion size.

[0072] The daily nutrition statistics module 104 is used to collect daily food intake data from multiple meals to obtain daily dietary intake data.

[0073] The daily nutrition statistics module 104 is further used to generate visual charts based on the daily dietary intake data. These visual charts include, but are not limited to, daily nutrient intake curves, bar charts comparing to target values, meal carbohydrate distribution charts, and seven-day trend charts; and automatically generate a dietary diary. All visual charts support PDF export, screenshot sharing, and cloud archiving.

[0074] The types of daily dietary intake data include total energy, carbohydrates, protein, fat, dietary fiber, sodium, mean GI, total GL, meal distribution, and peak carbohydrate intake periods.

[0075] The daily nutrition statistics module 104 is also used to obtain cumulative dietary intake data based on multiple food intake data accumulated on the day, and to generate a reference for the remaining dietary intake on the day in combination with the dietary intake reference data.

[0076] The personalized target and assessment module 105 is used to generate a dietary intake assessment result based on the food intake data, the daily dietary intake data, the dietary intake reference data, and preset assessment rules.

[0077] The evaluation rules include:

[0078] Carbohydrate compliance: Daily dietary intake data of carbohydrates ≤ Daily carbohydrate intake target value of dietary intake reference data, and meal carbohydrate intake data ≤ Meal carbohydrate intake limit of dietary intake reference data;

[0079] Low GI requirement met: Low GI foods accounted for ≥70% of dietary intake data;

[0080] Regular meal frequency: Daily meals ≥ the reference number of meals according to the dietary intake reference data, with meal intervals of 3–4 hours;

[0081] Risk factors: excessive high-GI foods in the diet, excessive carbohydrates in the diet or daily diet, excessive fats in the diet, and missed snacks.

[0082] The blood glucose and diet linkage module 106 is used to generate a joint report on blood glucose and diet based on the postprandial blood glucose data and food intake data of the target pregnant woman.

[0083] Specifically, the blood glucose and diet linkage module 106 can connect to blood glucose meter data or manually input fasting, 1-hour postprandial, and 2-hour postprandial blood glucose; establish a diet and blood glucose correlation model and mark high-risk meals; when blood glucose exceeds the standard, it automatically traces back the carbohydrate, GI, portion size, and eating time of the corresponding meal and provides adjustment suggestions; and generate a joint daily diet and blood glucose report to support remote evaluation by doctors.

[0084] The reminder module 107 is used to generate dietary reminders based on the dietary intake assessment results and the combined report.

[0085] Dietary reminders include, but are not limited to:

[0086] Meal reminders: including timed reminders for three meals and 2-3 snacks;

[0087] Meal time reminder: Avoid fasting for too long or overeating;

[0088] Postprandial blood glucose monitoring reminder: A timely reminder will be given 2 hours after the meal;

[0089] Nutrient excess warning: excessive carbohydrates, excessive fats, excessive consumption of high-GI foods; abnormal weight alerts, water intake reminders, medication / insulin reminders.

[0090] Please read Figure 2 In one feasible embodiment, the system further includes a doctor-patient collaboration module 108, which is used to communicate and interact with the doctor's end. The communication and interaction content includes the food intake data, the daily food intake data, the food intake assessment results, the postprandial blood glucose data, the joint report, consultation questions, and the doctor's response to questions or medical advice.

[0091] Based on the doctor-patient collaboration module 108, the pregnant woman's app allows doctors to view dietary statistics, blood glucose data, and daily reports. The doctor's app provides functions such as viewing group statistics and individual trends, issuing dietary prescriptions, marking issues, and pushing educational materials. Combined with the doctor-patient collaboration module 108 on the pregnant woman's app, it enables collaborative functions such as online consultations, abnormal referral reminders, and synchronized prenatal checkup recordings.

[0092] Compared to related technologies, the daily diet statistics system 100 for pregnant women with gestational diabetes mellitus (GDM) of this application includes multiple functional modules working collaboratively, such as a user authentication and basic information module 101, a GDM diet database 102, an intelligent diet entry module 103, a daily nutrition statistics module 104, a personalized goal and assessment module 105, a blood glucose and diet linkage module 106, a reminder module 107, and a doctor-patient collaboration module 108. It uses "GDM clinical guidelines as the core, daily statistics as the main line, intelligent entry as the entry point, blood glucose linkage as the closed loop, and doctor-patient collaboration as the extension" to solve problems such as cumbersome manual recording, lack of personalization, data fragmentation, and statistical difficulties, thus achieving standardized, intelligent, and convenient diet management for pregnant women with GDM. Furthermore, because the collaborative work of multiple functional modules covers the entire closed-loop management process from data collection to clinical intervention, it can significantly improve the efficiency and compliance of diet management for pregnant women with GDM.

[0093] Please see Figure 3The second embodiment of this application provides a method for tracking the daily diet of pregnant women with gestational diabetes, including:

[0094] S1: Calculate dietary intake reference data based on the pregnant woman's physiological information, dietary preferences, activity data, glycemic control targets, and GDM clinical guidelines.

[0095] For example, once a pregnant woman completes registration, she enters her height, weight, gestational age, GDM diagnosis results, and activity level; the app automatically calculates her BMI, total daily energy, macronutrient targets, carbohydrate limits per meal, and low-GI requirements, generating a personalized daily dietary reference for GDM.

[0096] S2: Enter the pregnant woman's food information for each meal and analyze the food intake data for each meal;

[0097] For example, when pregnant women select a meal, they can enter the food name, weight, and cooking method through text input, image recognition, voice, barcode scanning, or manual input; the system will automatically match the database and display the nutritional components and carbohydrate content of the food in real time.

[0098] S3: Collect daily food intake data from multiple meals to obtain daily food intake data;

[0099] For each food item entered, the system continuously accumulates the total daily energy, carbohydrates, protein, fat, average GI, and GL value; it automatically determines whether the daily / per meal carbohydrate threshold is exceeded and provides real-time alerts regarding the remaining edible amount. Furthermore, it can automatically complete daily statistics at 24:00: generating a nutrient summary table, meal distribution, target achievement rate, and risk points; displaying these in curve, bar, and list formats; and supporting one-click generation of a food diary.

[0100] S4: Generate a dietary intake assessment result based on the dietary intake data, the daily dietary intake data, the dietary intake reference data, and the preset assessment rules;

[0101] S5: Generate a combined report on blood glucose and diet based on the postprandial blood glucose and food intake data of the target pregnant woman;

[0102] For example, pregnant women can enter or synchronize their blood glucose data; the system can match the corresponding meal data, analyze the effects of carbohydrates, GI, and eating time on blood glucose, mark abnormal meals, and provide adjustment plans.

[0103] S6: Generate a dietary reminder based on the dietary intake assessment results and the combined report.

[0104] For example, based on preset time and intake status, it can trigger reminders for snacking, eating, and blood glucose monitoring; when carbohydrate intake is excessive, high-GI intake is excessive, or meal times are irregular, it can pop up warnings and recommend alternative foods.

[0105] In a feasible embodiment, the method further includes: S7: communicating and interacting with the doctor's terminal, the communication and interaction content including the food intake data, the daily food intake data, the food intake assessment results, the postprandial blood glucose data and the combined report, consultation questions and the doctor's terminal's response to questions or medical advice.

[0106] It should be noted that the daily diet statistics method for pregnant women with gestational diabetes provided in the second embodiment of this application is based on the same concept as the daily diet statistics system for pregnant women with gestational diabetes in the first embodiment of this application. The implementation process is detailed in the first embodiment and will not be repeated here.

[0107] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0108] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function selected in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.

[0111] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0112] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0113] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0114] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A daily dietary statistics system for pregnant women with gestational diabetes, characterized in that, include: The user authentication and basic information module is used to calculate dietary intake reference data based on the pregnant woman's physiological information, dietary preferences, activity data, blood glucose control targets and GDM clinical guidelines. The GDM diet database module is used to store food data and pregnancy nutrition data. The food data includes nutritional data for various foods, GDM-specific labels, and food exchange rules. The pregnancy nutrition data includes the content of nutrients required during pregnancy. The intelligent diet input module is used to input the pregnant woman's dietary information for each meal and analyze the dietary intake data for each meal. The daily nutrition statistics module is used to collect data on the daily intake of multiple meals to obtain daily dietary intake data. The personalized goal and assessment module is used to generate dietary intake assessment results based on the food intake data, the daily food intake data, the dietary intake reference data, and preset assessment rules. The blood glucose and diet linkage module is used to generate a combined blood glucose and diet report based on the target pregnant woman's postprandial blood glucose data and food intake data. The reminder module is used to generate dietary reminders based on the dietary intake assessment results and the combined report.

2. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 1, characterized in that, The dietary intake reference data includes: total daily energy, carbohydrate target, and carbohydrate limit per meal; Daily total energy = Basal energy × Activity factor + Pregnancy week-related energy supplement; Carbohydrate target = Total energy × 40%–50% / 4; The recommended carbohydrate intake per meal is 30–45g for a main meal and 15–30g for a snack.

3. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 1, characterized in that, The input methods of the intelligent diet input module include: Quick text selection: Favorite food items, one-click reuse of history records, and default meal templates; Image recognition and input: Take a photo of food, and automatically identify the type and weight through target detection, classification, volume and weight conversion; Voice input: Use voice to convert text to search for food items and confirm portion sizes; Scan to enter: Scan the barcode of pre-packaged food to obtain the nutrition facts label; Manual additions: Customize food, cooking methods, serving time, and portion size.

4. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 1, characterized in that, The daily nutrition statistics module is also used to generate visual charts based on the daily dietary intake data.

5. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 4, characterized in that, The types of daily dietary intake data include total energy, carbohydrates, protein, fat, dietary fiber, sodium, mean GI, total GL, meal distribution, and peak carbohydrate intake periods.

6. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 5, characterized in that, The daily nutrition statistics module is also used to obtain cumulative dietary intake data based on multiple food intake data accumulated on the day, and to generate a reference for the remaining dietary intake on the day in combination with the dietary intake reference data.

7. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 1, characterized in that, The evaluation rules include: Carbohydrate compliance: Daily dietary intake data of carbohydrates ≤ Daily carbohydrate intake target value of dietary intake reference data, and meal carbohydrate intake data ≤ Meal carbohydrate intake limit of dietary intake reference data; Low GI requirement met: Low GI foods accounted for ≥70% of dietary intake data; Regular meal schedule: Daily meals ≥ the reference number of meals according to dietary intake reference data, with meal intervals of 3–4 hours.

8. The daily dietary statistics system for pregnant women with gestational diabetes according to claim 7, characterized in that, The assessment rules also include several risk items, which include at least: excessive high-GI intake in the food intake data, excessive carbohydrate intake in the food intake data or daily dietary intake data, excessive fat intake in the food intake data, and missed snacks.

9. The daily dietary statistics system for pregnant women with gestational diabetes as described in claim 1, characterized in that, The system also includes a doctor-patient collaboration module, which is used to communicate and interact with the doctor. The communication and interaction content includes the food intake data, the daily food intake data, the food intake assessment results, the postprandial blood glucose data and the combined report, consultation questions, and the doctor's response to questions or medical advice.

10. A method for statistically analyzing the daily diet of pregnant women with gestational diabetes, characterized in that, include: Based on the pregnant women's physiological information, dietary preferences, activity data, glycemic control targets, and GDM clinical guidelines, dietary intake reference data are calculated. The system stores food data and pregnancy nutrition data. The food data includes nutritional information for various foods, GDM-specific labels, and food exchange rules. The pregnancy nutrition data includes the content of nutrients required during pregnancy. Enter the pregnant woman's food information for each meal and analyze the food intake data for each meal; By statistically analyzing the daily food intake data from multiple meals, we can obtain daily food intake data. Based on the dietary intake data, the daily dietary intake data, the dietary intake reference data, and the preset evaluation rules, a dietary intake evaluation result is generated; The system generates a combined report on blood glucose and diet based on the postprandial blood glucose and dietary intake data of the target pregnant women. Based on the dietary intake assessment results and the combined report, a dietary reminder is generated.

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