Dynamic customized meal management method and system based on multi-member data of a family

Abstract

This invention discloses a dynamic, customized dietary management method and system based on multi-family member data. The method includes: acquiring personal health data, dietary preference data, and physiological stage data throughout the entire life cycle of multiple family members; generating personalized dietary plans for each member based on a multi-objective optimization model, and integrating these to generate a unified food list suitable for the whole family; monitoring changes in the health data and physiological stages of multiple members in real time, automatically triggering dynamic updates when any member's health data changes; responding to user-inputted dietary requirement commands, verifying the health compliance of the commands, and automatically updating the family menu and corresponding food list if successful. This invention solves the core pain point of existing family dietary management technologies, which cannot simultaneously address the personalized needs of multiple family members and the costs of unified procurement, achieving a perfect balance between personalization and uniformity in a family setting, and significantly reducing the threshold and cost of healthy family eating.

Description

Technical Field

[0001] This invention relates to the field of dietary health management technology, and in particular to a dynamic and customized dietary management method and system based on multi-family member data. Background Technology

[0002] In existing technologies, dietary recommendations and management methods mostly focus on a single individual, such as generating personalized weight-loss or blood sugar-controlled meals for a particular user. However, a typical family setting is complex, potentially including elderly individuals with hypertension, parents who need to control their weight, and normally growing children. More importantly, families often include women in special physiological stages such as trying to conceive, pregnancy, postpartum recovery, or breastfeeding, or post-operative recovering individuals and elderly people with impaired digestive function who require special care.

[0003] The core flaw of these existing methods is that they cannot simultaneously acquire and process the personal health data, dietary preference data, and physiological stage data of multiple family members. Furthermore, they cannot generate personalized dietary plans for each member and integrate them into a single family-wide shopping list based on a unified model that simultaneously considers three conflicting goals: overall family nutritional balance, individual health suitability for each member, nutritional needs at specific physiological stages, and unified economic efficiency in food procurement. Therefore, this directly leads to significant inconvenience in family dietary management: either prepare meals individually for each member, which is costly and inefficient; or sacrifice the health needs of some members by adopting a "one-size-fits-all" diet plan, which may pose serious health risks to members in critical stages such as pregnancy, childbirth, and post-operative recovery. In addition, existing plans are mostly static recommendations, which remain fixed once generated and cannot respond to dynamic changes in the health status and physiological stages of family members (such as poor sleep, mood swings, weight gain or loss, transitioning from pregnancy to postpartum, and from breastfeeding to complementary feeding). They also cannot intelligently respond to personalized dietary needs expressed by users through natural language (such as "I want to eat hot pot this weekend"), and have failed to establish a closed loop from plan generation to food delivery.

[0004] Therefore, it is necessary to provide a dynamic and customized dietary management method and system based on multi-family member data to overcome the above-mentioned shortcomings. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic and customized dietary management method and system based on data from multiple family members. It aims to solve the problems of how to simultaneously meet the personalized health needs of multiple family members, the nutritional requirements of special physiological stages throughout the life cycle, ensure the uniformity and economy of family procurement, and dynamically respond to changes in the health of family members. It addresses the core pain point of the difficulty in achieving both "one plan per person" and "one procurement per family" in the family setting, and realizes intelligent, personalized, dynamic, and full-life-cycle coverage of family dietary management.

[0006] To achieve the above objectives, the first aspect of the present invention provides a dynamic and customized dietary management method based on multi-family member data, comprising the following steps: Obtain personal health data, dietary preference data, and physiological stages throughout the entire life cycle of multiple family members. data; Based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health suitability among multiple family members, and the loss of procurement uniformity, a personalized dietary plan is generated for each family member, and an integrated list of ingredients suitable for unified procurement by the whole family is generated. The system monitors the health data and physiological changes of the multiple members in real time. When any member's relevant data changes, the system automatically triggers a dynamic update of the personalized diet plan and the ingredient list. In response to user-inputted dietary requirements, the system performs a health compliance check on the requirements. If the check passes, the system automatically updates the family menu and the corresponding ingredient list.

[0007] In a preferred embodiment, the objective function of the multi-objective optimization model is expressed as: min F(x) =ω1·f1(x) +ω2·f2(x) +ω3·f3(x); Where f1(x) is the overall nutritional balance loss function of the family, f2(x) is the health fit loss function of multiple members, f3(x) is the procurement uniformity loss function, and ω1, ω2, ω3 are the corresponding dynamically adjustable weight coefficients.

[0008] In a preferred embodiment, the personalized dietary plan further includes: exclusive recipe suggestions, personalized serving sizes, a list of dietary restrictions for each member, and instructions on cooking methods, precise amounts of oil and salt, recommended oils and nutrient-locking techniques for each dish, along with associated cooking tutorial videos.

[0009] In a preferred embodiment, the health data and physiological stage changes include at least one or more of the following: sleep quality, emotional state, weight change, updated physical examination report, changes in physiological state, preparing for pregnancy, pregnancy, postpartum, breastfeeding, introduction of complementary foods for infants and young children, menopause, old age, and postoperative rehabilitation; the dynamic update includes automatically generating and delivering suitable sleep-aiding meals, depression-relieving meals, weight-loss meals, pregnancy meals, postpartum meals, breastfeeding meals, complementary food meals, soft food meals for the elderly, or postoperative rehabilitation meal plans.

[0010] In a preferred embodiment, the system also includes a dietary management step based on physiological stages throughout the entire life cycle. The steps specifically include: based on any specific physiological stage of the member, such as the pre-conception period, early pregnancy, mid-pregnancy, late pregnancy, postpartum period, lactation period, infant complementary feeding period, menopause, old age, or postoperative recovery period, the system automatically matches the corresponding medical dietary guidelines and dynamically generates and applies a scientific dietary plan for that stage. The postpartum recovery plan is further divided into four stages: blood stasis removal and uterine cleansing, repair and conditioning, lactation promotion and milk production, and strengthening and nourishing the body. It also automatically matches ingredients and restrictions based on the mother's constitution. The infant complementary food plan is further automatically matched with a list of complementary foods and recipes of appropriate textures based on the infant's age.

[0011] In a preferred embodiment, the step of verifying the health compliance of the instruction specifically includes: when the dietary requirements entered by the user do not meet health requirements, the system returns a clear reason for non-compliance and automatically generates one or more alternative dietary plans suitable for the health of the whole family.

[0012] In a preferred embodiment, the method further includes a full-scenario dining adaptation step: automatically adapting the ingredient list or meal plan to a home cooking plan, a bring-to-work plan, or a customized nutritious takeout plan based on the dining scenario selected by the user.

[0013] In a preferred embodiment, a dynamic member management step is also included: when a family member is detected to be temporarily away or a new member is added, the personalized meal plan and the amount and type of ingredients in the ingredient list are automatically adjusted, and a reminder to complete the member's health data is issued when the member's health data is missing.

[0014] In a preferred embodiment, the ingredient list includes: basic ingredients, milk, nuts, healthy snacks, health tea bags, and food and medicine homology products, and simultaneously generates personalized drinking recommendations, including recommended drinks, unrecommended drinks and their reasons; the method also includes an intelligent delivery step: based on a third-party delivery platform connected via API, according to the predetermined delivery cycle, a confirmation notification is sent to the user before delivery, and after the user confirms, payment is automatically deducted and delivery is arranged.

[0015] A second aspect of the present invention provides a dynamic, customized dietary management system based on multi-family member data, comprising: The data acquisition module is used to acquire personal health data, dietary preference data, and physiological stage data throughout the entire life cycle of multiple family members; The recipe recommendation module is designed to generate personalized meal plans for each family member based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health adaptability among multiple family members, and the loss of uniformity in procurement. It also integrates and generates a list of ingredients that can be uniformly purchased for the whole family. The dynamic adjustment module is used to monitor the health data and physiological stage changes of the multiple members in real time. When the relevant data of any member changes, the personalized diet plan and the ingredient list are automatically updated dynamically. The interactive verification module is used to respond to user input requirements and perform health and compliance verification. If the verification passes, the dynamic adjustment module is executed.

[0016] A third aspect of the present invention provides a terminal, the terminal including a memory, a processor, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the various steps of the dynamic customized dietary management method based on multi-family member data as described in any of the above embodiments.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the dynamic customized dietary management method based on multi-family member data as described in any of the above embodiments.

[0018] The present invention provides a dynamic and customized dietary management method and system based on multi-family member data. It constructs a multi-objective function with three core optimization objectives: "overall nutritional balance of the family", "health adaptation of individual members", and "minimization of procurement costs". By solving this function, a set of Pareto optimal solutions is found under the premise of satisfying the health constraints of all members. This simultaneously achieves the personalization of "one plan per person" and the economy of "unified family procurement". It also solves the core pain points of existing technologies in family dietary management that cannot take into account the personalized needs of multiple members, the scientific nutritional requirements of special physiological stages, and the unified procurement costs. It achieves a perfect balance between personalization, full life cycle coverage and uniformity in the family setting, and significantly reduces the threshold and cost of family healthy eating. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating the dynamic and customized dietary management method based on multi-family member data provided by this invention; Figure 2 This is a framework diagram of the dynamic customized dietary management system based on multi-family member data provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described in this specification are merely for explaining the invention and are not intended to limit the invention.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] Example 1 In embodiments of the present invention, a dynamic and customized dietary management method based on multi-family member data is provided, constructing a novel multi-objective optimization and dynamic monitoring framework to achieve intelligent, personalized, dynamic, and full-life-cycle coverage of family dietary management. To better and more thoroughly illustrate the technical solution of the present invention, the following will use a hypothetical typical three-generation family as an example. This family includes: a father (50 years old, suffering from hypertension), a mother (48 years old, suffering from type 2 diabetes), a son (10 years old, in good health), a daughter-in-law (30 years old, in late pregnancy), and a grandfather (75 years old) who has just been discharged from the hospital after hip replacement surgery.

[0025] like Figure 1As shown, the dynamic and customized dietary management method based on multi-family member data includes the following steps S10-S40. The step numbers are for simplicity and are not intended to limit the execution order.

[0026] Step S10: Obtain personal health data, dietary preference data, and physiological stage data throughout the entire life cycle of multiple family members.

[0027] Specifically, during the initialization process, the system creates an independent health record for each family member. For the father, the system records his age, gender, height, weight, medical history (hypertension), medication use, and dietary restrictions as prescribed by his doctor (such as low sodium). For the mother, the system records her diabetes type, blood sugar control target, and foods to avoid (such as high glycemic index foods). For the son, the system records his growth and development indicators and activity levels. For the daughter-in-law, the system focuses on recording her physiological stage data throughout her life cycle, specifically "late pregnancy (week 35)," and links this data to her prenatal checkup reports, including weight gain curves, blood pressure, and blood sugar levels. For the grandfather, the system records his physiological stage during "post-operative recovery" and includes assessments of his swallowing function and digestive capacity, such as the need for soft, easily digestible foods.

[0028] Therefore, this data can be automatically acquired through user manual input, connection to hospital medical examination report interfaces, or by binding with wearable devices such as smart bracelets and smart body fat scales. For example, the system can connect to the mother's smart bracelet via Bluetooth to automatically read her daily sleep duration, heart rate, and steps. Simultaneously, the system will also collect each member's dietary preferences, such as the father's preference for fish, the mother's dislike of cilantro, the son's preference for tomato flavor, the daughter-in-law's recent preference for sweet and sour flavors, and the grandfather's dental issues limiting his diet to soft foods. This preference data can be obtained through initial questionnaires or subsequent analysis of interaction logs.

[0029] Step S20: Based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health adaptability among multiple members, and the loss of procurement uniformity, a personalized dietary plan is generated for each member, and an integrated list of ingredients suitable for unified procurement by the whole family is generated.

[0030] Understandably, this step addresses the fundamental problem of existing technologies' inability to balance personalization and uniformity. Its implementation principle is to abstract the family meal planning problem into a constrained multi-objective optimization problem. Three core loss functions are constructed here: f1(x) represents the family's overall nutritional balance loss function. It measures the difference between the generated food combination and the recommended standards of the "Dietary Guidelines for Chinese Residents" in terms of calories, protein, fat, carbohydrates, and various vitamins and minerals. The smaller the difference, the smaller the loss, which means that the family's overall diet is more balanced.

[0031] f2(x) represents the multi-member health fit loss function, which measures the degree to which each member's personal dietary plan (such as their individual food intake and food choices) conforms to their specific health constraints (such as low sodium requirements for hypertension and blood sugar control requirements for diabetes) and physiological stage requirements (such as controlling weight gain and increasing calcium and iron intake in late pregnancy; and high-protein, easily digestible foods for postoperative recovery). Any violation of these contraindications will increase the value of this loss function.

[0032] f3(x) represents the procurement uniformity loss function, which encourages the system to reuse the same ingredients as much as possible when generating family meals (for example, green peppers can be used in both stir-fried pork and stir-fried eggs), thereby reducing the types of ingredients that need to be purchased, reducing procurement costs and logistics complexity.

[0033] These three objective functions often conflict with each other. For example, to achieve ultimate personalization (minimizing f2), the whole family might eat three completely different meals, but this would lead to a sharp increase in the loss of f3 (purchasing uniformity). The core of this step is to find a set of Pareto optimal solutions through a multi-objective optimization solver, that is, to find an equilibrium point that minimizes the value of F(x) = ω1·f1(x) + ω2·f2(x) + ω3·f3(x). Here, ω1, ω2, and ω3 are dynamically adjustable weight coefficients. For example, for a wealthy family that pays special attention to health but is not concerned about purchasing costs, ω2 (health fit weight) can be increased. It should be noted that the solution process for Pareto optimal solutions can refer to existing technologies, and this invention does not limit it.

[0034] For example, as a specific implementation of this step, after obtaining data from three people, the system runs the above algorithm to generate a one-day plan: The family's standard dishes: stir-fried Shanghai bok choy (200g), steamed chicken breast (150g), and scrambled eggs with tomatoes (using 2 eggs and 100g tomatoes).

[0035] Personalized dietary plans: The system generates suggestions for the father: "Eat normally, but pay attention to post-meal blood pressure"; for the mother: "Reduce staple food intake by 1 / 3, prioritize chicken breast"; for the son: "Eat normally, you can add a serving of milk." For the daughter-in-law, the system generates a personalized suggestion: "This dish is rich in calcium and high-quality protein, meeting the nutritional needs of late pregnancy. Please eat normally, and keep the soup low in oil and salt." For the grandfather, the system generates special care suggestions: "Tofu is soft and easy to eat. Please be sure to chop the broccoli and mushrooms and cook them until soft before eating, as this will aid digestion and recovery." Additionally, the system generates chrysanthemum and cassia seed tea as a personalized tea bag for the father, mulberry leaf tea for the mother, and high-calcium milk for the child, daughter-in-law, and grandfather—all of which are part of the personalized dietary plan.

[0036] The unified food procurement list, generated after system integration, includes: 200g Shanghai bok choy, 150g chicken breast, 2 eggs, 100g tomatoes, 1 serving of chrysanthemum and cassia seed tea bags, 1 serving of mulberry leaf tea bags, and 200ml of high-calcium milk. As you can see, this list allows family members to meet everyone's healthy dietary needs for the day with just one purchase, regardless of their life stage.

[0037] Furthermore, to ensure users not only "know what to eat" but also "know how to cook it well and healthily," this personalized meal plan can include more detailed guidance. For example, for the dish "steamed chicken breast," the system will indicate its cooking method as "steaming," suggest precise amounts of oil and salt as "1 gram of salt and 2 grams of oil," recommend "olive oil or camellia oil," and provide cooking precautions and nutrient-locking techniques, such as "coating the chicken breast with a little cornstarch, steaming over high heat for 8 minutes, and letting it sit for 2 minutes after turning off the heat to maximize moisture and nutrient retention." Simultaneously, the system will link to a 30-second cooking tutorial video, which users can watch with a click, bridging the gap between "theoretical solutions" and "kitchen practice," greatly improving user experience and the feasibility of the plan.

[0038] Step S30: Monitor the health data and physiological stage changes of multiple members in real time, and automatically trigger dynamic updates to the personalized diet plan and ingredient list when any member's relevant data changes.

[0039] Among them, health data and physiological stage changes include at least one or more of the following: sleep quality, emotional state, weight change, updated physical examination report, changes in physiological state, and preparation for pregnancy, pregnancy, postpartum, breastfeeding, introduction of complementary foods for infants and young children, menopause, old age, and postoperative recovery; dynamic updates include automatically generating and delivering suitable sleep-aiding meals, depression-relieving meals, weight-loss meals, pregnancy meals, postpartum meals, breastfeeding meals, complementary food meals, soft food meals for the elderly, or postoperative recovery meal plans.

[0040] It should be noted that this step involves building a health data and life stage monitoring engine to continuously monitor various health indicators from wearable devices (such as smartwatches), medical examination report interfaces, user input, or calendar reminders (such as due date and age reminders for introducing complementary foods). Once the engine detects data changes exceeding preset thresholds or a change in status, it immediately triggers an event. This event serves as new input, driving the aforementioned multi-objective optimization model to recalculate, thereby achieving automatic updates to the solution.

[0041] For example, in one embodiment, the system uses data from the mother's smart bracelet to detect that her deep sleep duration has been less than 1 hour for the past three days (the normal value is 1.5 hours), indicating an adverse change in the health data related to "sleep quality." The system will automatically trigger the dynamic adjustment module. This module will rerun the optimization algorithm, adding "improving sleep" as a new high-priority constraint to f2(x). The system will then automatically generate a new dinner plan: updating the original menu to "Lily and Lotus Seed Porridge," which has calming and sleep-aiding effects, and "Steamed Sea Bass," which is rich in tryptophan. Simultaneously, a box of "Sour Jujube Seed and Poria Cocos Night Tea Bags" (with a special note indicating a safe formula suitable for pregnant women) will be added to the delivery list.

[0042] The updated plans and lists are automatically pushed to all family members' apps (e.g., mobile apps) and can be synchronized with the delivery module. Similarly, if the system detects that the mother's weight has increased by 1 kg in a week, it will automatically recommend a new low-calorie weight-loss diet plan; if the father's medical report is updated to show that his uric acid is also high, the system will immediately add a "high-purine food" filter to his list of foods to avoid and update all related recipes. Therefore, this plan achieves a leap from "passively following medical advice" to "proactive dynamic health intervention," enabling timely and effective improvement of family members' health through dietary means.

[0043] Furthermore, this method also includes dietary management steps based on physiological stages throughout the entire life cycle. Specifically, based on any specific physiological stage a member is in, such as the pre-conception period, early pregnancy, mid-pregnancy, late pregnancy, postpartum period, lactation period, infant complementary feeding period, menopause, old age, or postoperative recovery period, the system automatically matches the corresponding medical dietary guidelines and dynamically generates and applies a scientific dietary plan for that stage. Among them, the postpartum plan is further automatically divided into the postpartum recovery cycle into the blood stasis and uterine cleansing stage, the repair and conditioning stage, the lactation promotion and milk production stage, and the consolidation and beauty stage, and automatically adapts the ingredients and taboos according to the mother's constitution. The infant complementary feeding period plan is further automatically adapted to the appropriate texture of the stage's complementary food list and recipe according to the infant's age.

[0044] For example, when the daughter-in-law's physiological stage data changes, such as from "late pregnancy" to "postpartum period (confinement period)," the system's physiological stage dietary engine will be activated. It will immediately and seamlessly switch the dietary plan from the "weight control and smooth delivery" mode of late pregnancy to the "detoxification, repair, and lactation promotion" four-stage mode of the confinement period. Specifically, in the first week postpartum, the system will automatically generate a "detoxification and uterine cleansing" diet mainly composed of pork liver, red beans, and Sheng Hua Tang (a traditional Chinese medicine formula); in the second week, it will automatically transition to a "repair and conditioning" diet mainly composed of Eucommia ulmoides, walnuts, and black sesame seeds. It should be noted that this switching process is fully automatic and requires no manual settings from the user, ensuring that the mother receives the most scientific and timely dietary nutritional support during her weakest period. Similarly, when the grandfather's post-operative recovery period ends and he enters a stable period, the system will automatically relax the requirements for food texture, but will still maintain a high-protein, high-vitamin dietary framework.

[0045] Step S40: Respond to the user's input of dietary requirements (e.g., input via natural language) and perform health compliance verification on the instruction. If the verification passes, automatically update the family menu and the corresponding ingredient list.

[0046] Specifically, this step can be combined with voice input to build a natural language interaction engine. For example, on a weekend, a user (e.g., the father) enters "I want to eat Chongqing hot pot this Saturday noon" in the system interaction window. After receiving this natural language instruction, the system will perform semantic parsing and extract key information: time (Saturday noon) and type of dish (Chongqing hot pot). Next, the compliance verification module starts working. It compares the nutritional components of "traditional Chongqing hot pot" (high oil, high salt, high purine, spicy) with the health database of each family member. The comparison results show that the dish does not meet the father's low-sodium diet requirements for high blood pressure, does not meet the mother's low-fat and blood sugar control requirements for diabetes, does not meet the daughter-in-law's salt and oil control requirements in late pregnancy, does not meet the grandfather's light diet principle for postoperative recovery, and is also detrimental to the gastrointestinal health of a 10-year-old child.

[0047] Therefore, if the verification fails, the system will not simply reject the request, but will first return a clear reason for non-compliance, such as: "Sorry, traditional Chongqing hot pot is high in oil and salt, which may cause fluctuations in the father's blood pressure and raise the mother's blood sugar. It is also unsuitable for the daughter-in-law in late pregnancy and the grandfather recovering from surgery, and it can also irritate the child's stomach." Next, the system will automatically generate one or more alternative dietary plans suitable for the whole family's health, such as: "We suggest changing to a 'Family Reunion Nourishing Hot Pot' base, using clear chicken or mushroom broth as the base, paired with plenty of leafy green vegetables, lean beef slices, tofu, and shrimp paste, and using low-sodium soy sauce and balsamic vinegar as dipping sauce. This retains the fun of hot pot while meeting the health requirements of the whole family, including pregnant women and the elderly." After the user confirms acceptance of the alternative plan, the system will automatically update the family menu and corresponding ingredient purchase list for Saturday noon. Therefore, this step, while respecting the user's dietary culture and personal wishes, provides professional, flexible, and guided health management, greatly improving the user's acceptance and adherence to healthy eating.

[0048] In order to build a complete and intelligent business closed loop, the preferred embodiments of the present invention may also include several additional steps.

[0049] For example, the present invention may also include a full-scenario dining adaptation step. Specifically, users can select a dining scenario when ordering food. If a user selects "bring lunch to the office," the system will automatically adapt the lunch plan to dishes suitable for microwave heating and less prone to spoilage, such as "broccoli stir-fried with chicken breast" instead of "stir-fried leafy greens," and suggest using a compartmentalized lunchbox to maintain the texture. If a user selects "customized nutritional takeout," the system will send standardized nutritional parameters (e.g., a takeout order of 500 kcal, 30g protein, 15g fat, and 55g carbohydrates) to partner healthy restaurants for customized preparation, rather than providing standard takeout items.

[0050] For example, the present invention may also include a dynamic member management step. Specifically, when the system detects that the father is going on a business trip for a week, he will mark it as "temporary departure" on the app. The system will immediately and automatically adjust the ingredient quantities of all recipes, removing the father's portion. For example, if steamed sea bass originally required 250 grams, it will now be adjusted to 150 grams. At the same time, the shopping list will also be updated accordingly. When the system detects that an elderly person (grandmother) is joining the family as a new member, it will first remind the user to complete the grandmother's health data (such as age, whether she has chronic diseases, etc.). After the data is entered, the system will use the grandmother as a new optimization constraint, re-run the multi-objective optimization model, and generate a new meal plan for the whole family of four.

[0051] Understandably, to provide more comprehensive health support, the food list generated by this invention can go beyond basic ingredients and include: milk, daily nuts, sugar-free healthy snacks, freeze-dried vegetable crisps, and health-preserving tea bags and food-medicine homologous foods (such as hawthorn, dried tangerine peel, poria cocos, postpartum tetrapanax papyriferus, and vaccaria segetalis) customized according to individual constitution and physiological stage. Simultaneously, the system will also generate personalized drinking recommendations. For example, for the father with high blood pressure, the system will recommend "chrysanthemum tea or cassia seed tea, which helps to lower blood pressure" and add it directly to the delivery list; while explicitly warning "no high-sugar carbonated drinks or functional beverages are recommended, as they can cause blood pressure fluctuations." For the mother, "mulberry leaf tea" will be recommended, with a warning "no fruit juice or sugary yogurt is recommended." For the breastfeeding daughter-in-law, the system will recommend "tetrapanax papyriferus and crucian carp soup or red bean water," with a explicit warning "no barley tea or other drinks that have a milk-reducing effect."

[0052] For example, this invention may also include intelligent delivery steps. The system is deeply integrated with third-party fresh food delivery platforms via API (Application Programming Interface). Once a three-day food list is generated, the system will automatically push a confirmation notification to the user's app at 10:00 AM the day before the delivery date, based on the user's preset delivery cycle (e.g., delivery every Tuesday and Friday). The notification includes a detailed food list, estimated delivery time, and total price. The user can modify the order (e.g., change "sea bass" to "salmon") or adjust the delivery time within a certain time (e.g., 1 hour). After user confirmation, the system will automatically deduct the corresponding amount from the user's stored value account and call the delivery platform's order-taking interface to arrange for a rider to deliver at the agreed time. This ensures the freshness of the food and the certainty of delivery, forming a highly sticky service loop from health assessment to food delivery.

[0053] Example 2 This invention also provides a dynamic, customized dietary management system 100 based on multi-family member data, such as... Figure 2 As shown, it includes: Data acquisition module 10 is used to acquire personal health data, dietary preference data and physiological stage data throughout the entire life cycle of multiple family members; The recipe recommendation module 20 is used to generate personalized meal plans for each member based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health adaptability among multiple members, and the loss of procurement uniformity. It also integrates and generates a list of ingredients that can be uniformly purchased for the whole family. The dynamic adjustment module 30 is used to monitor the health data and physiological stage changes of the multiple members in real time. When the relevant data of any member changes, the dynamic update of the personalized diet plan and the ingredient list is automatically triggered. The interactive verification module 40 is used to respond to the user's input requirements and perform health and compliance verification. If the verification passes, the dynamic adjustment module 30 is executed. The physiological stage engine 50 is used to automatically generate and call the corresponding scientific dietary plan logic based on the specific physiological stage of the member, such as the pre-conception period, pregnancy, postpartum period, breastfeeding period, complementary food period, menopause, old age or postoperative recovery period.

[0054] The system modules and method steps correspond one-to-one, and its implementation principle and beneficial effects are as described in the previous method section, so they will not be repeated here. This system can be deployed as a standalone application on users' smartphones, smart home speakers, or cloud servers to achieve intelligent, end-to-end management of family dietary health.

[0055] Example 3 The present invention provides a terminal, the terminal including a memory, a processor and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the various steps of the dynamic customized diet management method based on multi-family member data as described in any of the above embodiments.

[0056] Example 4 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the dynamic customized dietary management method based on multi-family member data as described in any of the above embodiments.

[0057] In summary, this invention constructs a multi-objective function with three core optimization goals: "overall family nutritional balance," "individual health adaptation for family members," and "minimization of procurement costs." By solving this function, a set of Pareto optimal solutions is found while satisfying the health constraints of all members. This simultaneously achieves the personalization of "one plan per person" and the economy of "unified family procurement." Furthermore, it addresses the core pain points of existing technologies in family dietary management, which cannot simultaneously consider the personalized needs of multiple family members, the scientific nutritional requirements of special physiological stages throughout the life cycle, and the costs of unified procurement. It achieves a perfect balance between personalization, full life cycle coverage, and uniformity in the family setting, significantly reducing the threshold and cost of healthy family eating.

[0058] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0059] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0060] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0061] In the embodiments provided by this invention, it should be understood that the disclosed systems, devices / terminal equipment, and methods can be implemented in other ways. For example, the system or device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of systems or units may be electrical, mechanical, or other forms.

[0062] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0064] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. A dynamic, customized dietary management method based on multi-family member data, characterized in that, Includes the following steps: Obtain personal health data, dietary preference data, and physiological stages throughout the entire life cycle of multiple family members. Segment data; Based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health suitability among multiple family members, and the loss of procurement uniformity, a personalized dietary plan is generated for each family member, and an integrated list of ingredients suitable for unified procurement by the whole family is generated. The system monitors the health data and physiological changes of the multiple members in real time. When any member's relevant data changes, the system automatically triggers a dynamic update of the personalized diet plan and the ingredient list. In response to user-inputted dietary requirements, the system performs a health compliance check on the requirements. If the check passes, the system automatically updates the family menu and the corresponding ingredient list.

2. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, The objective function of the multi-objective optimization model is expressed as: min F(x) =ω1·f1(x) +ω2·f2(x) +ω3·f3(x); Where f1(x) is the overall nutritional balance loss function of the family, f2(x) is the health fit loss function of multiple members, f3(x) is the procurement uniformity loss function, and ω1, ω2, ω3 are the corresponding dynamically adjustable weight coefficients.

3. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, The personalized dietary plan also includes: exclusive recipe suggestions, personalized serving sizes, a list of dietary restrictions for each member, as well as cooking methods, precise amounts of oil and salt, recommended oils and nutrient-locking techniques for each dish, and linked cooking tutorial videos.

4. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, The health data and physiological stage changes include at least one or more of the following: sleep quality, emotional state, weight change, updated physical examination report, changes in physiological state, and preparation for pregnancy, pregnancy, postpartum, breastfeeding, introduction of complementary foods for infants and young children, menopause, old age, and postoperative recovery; the dynamic updates include automatically generating and delivering suitable sleep-aiding meals, depression-relieving meals, weight-loss meals, pregnancy meals, postpartum meals, breastfeeding meals, complementary food meals, soft food meals for the elderly, or postoperative recovery meal plans.

5. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, It also includes dietary management steps based on physiological stages throughout the entire life cycle. These steps specifically include: automatically matching the corresponding medical dietary guidelines and dynamically generating and applying a scientific dietary plan for that stage based on any specific physiological stage that a family member is in, such as the pre-conception period, early pregnancy, mid-pregnancy, late pregnancy, postpartum period, lactation period, infant complementary feeding period, menopause, old age, or postoperative recovery period. The postpartum recovery plan is further divided into four stages: blood stasis removal and uterine cleansing, repair and conditioning, lactation promotion and milk production, and strengthening and nourishing the body. It also automatically matches ingredients and restrictions based on the mother's constitution. The infant complementary food plan is further automatically matched with a list of complementary foods and recipes of appropriate textures based on the infant's age.

6. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, The steps for verifying the health compliance of the instructions specifically include: when the dietary requirements entered by the user do not meet health requirements, the system returns a clear reason for non-compliance and automatically generates one or more alternative dietary plans suitable for the health of the whole family.

7. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, It also includes a full-scenario dining adaptation step: based on the dining scenario selected by the user, the ingredient list or meal plan is automatically adapted to a home cooking plan, a lunch-to-work plan, or a customized nutritious takeout plan.

8. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, It also includes dynamic member management steps: when a family member is detected to be temporarily away or a new member is added, the personalized meal plan and the amount and type of ingredients in the ingredient list are automatically adjusted, and a reminder is issued to complete the member's health data when it is missing.

9. The dynamic customized dietary management method based on multi-family member data as described in claim 1, characterized in that, The ingredient list includes: basic ingredients, milk, nuts, healthy snacks, health tea bags, and food and medicine homology products. Personalized drinking recommendations are generated simultaneously, including recommended drinks, unrecommended drinks and their reasons. The method also includes an intelligent delivery step: based on a third-party delivery platform connected via API, a confirmation notification is sent to the user before delivery according to the predetermined delivery cycle, and payment is automatically deducted and delivery is arranged after user confirmation.

10. A dynamic, customized dietary management system based on multi-family member data, characterized in that, include: The data acquisition module is used to acquire personal health data, dietary preference data, and physiological stage data throughout the entire life cycle of multiple family members; The recipe recommendation module is designed to generate personalized meal plans for each family member based on a multi-objective optimization model, with the goal of simultaneously minimizing the loss of overall family nutritional balance, the loss of health adaptability among multiple family members, and the loss of uniformity in procurement. It also integrates and generates a list of ingredients that can be uniformly purchased for the whole family. The dynamic adjustment module is used to monitor the health data and physiological stage changes of the multiple members in real time. When the relevant data of any member changes, the personalized diet plan and the ingredient list are automatically updated dynamically. The interactive verification module is used to respond to user input requirements and perform health and compliance verification. If the verification passes, the dynamic adjustment module is executed.

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