Lightweight AI health management method and system

Abstract

This invention belongs to the field of artificial intelligence health management technology and discloses a lightweight AI health management method and system. It uses the selection of primary prompt words and / or auxiliary prompt words to achieve accurate classification of the population. Then, by managing the validity period of the AI ​​module's historical calculation records, it can directly output health management prescriptions calculated with the same prompt words within the validity period, avoiding repeated calculations that would consume computing power. At the same time, by accurately classifying the population, it is not necessary to calculate all elements, which also reduces the resource consumption of the AI ​​module's computing power. Overall, it saves AI computing power while ensuring the effectiveness of health management.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence health management technology, specifically to a lightweight AI health management method and system based on prompt word classification. Background Technology

[0002] With the application of artificial intelligence technology in the field of health management, more and more AI health management systems are being developed and used. However, existing AI health management systems generally suffer from high computing power consumption: on the one hand, each user's health data is independently calculated by AI, resulting in a high rate of duplicate calculations and a significant waste of computing resources; on the other hand, the generation of health management prescriptions does not incorporate population characteristic typing, resulting in insufficient targeting of calculation results and a lack of effective reuse of historical calculation results, further increasing the system's computing power load.

[0003] Meanwhile, existing health management systems have a rather general classification of human health characteristics. They do not combine basic information such as age, gender, height, and weight with more comprehensive health indicators such as physical fitness, bad habits, stress levels, and medical history in a refined way. Therefore, they cannot generate personalized health management prescriptions for different groups of people, and it is also difficult to achieve standardized storage and efficient retrieval of health management data.

[0004] Therefore, there is an urgent need for a lightweight AI health management method and system that can effectively reuse historical calculation results by establishing standardized primary and secondary health tips and performing population segmentation, thereby reducing redundant AI calculations, lowering computing power consumption, and improving the targeting and generation efficiency of health management prescriptions. Summary of the Invention

[0005] Purpose of the invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a lightweight AI health management method and system based on prompt word classification. This method solves the problems of high computational power consumption, high rate of repetitive calculations, and imprecise population classification in existing AI health management systems. It enables efficient generation of health management prescriptions and reuse of historical data, reduces computational power requirements, and improves the personalization and standardization of health management.

[0007] Technical solution

[0008] To achieve the above objectives, the present invention provides a lightweight AI health management method, comprising the following steps:

[0009] S1: Establish and manage primary and secondary health indicators, including age, gender, height, and weight, and secondary indicators including physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets.

[0010] S2: Based on the main prompting words and / or auxiliary prompting words, the population is combined and classified;

[0011] S3: Set the age threshold that needs to be combined with auxiliary prompts. When a user enters their main prompt for health management, determine whether the user needs to combine with auxiliary prompts for combined calculation based on their age. Then check if there is an AI calculation record. If there is no historical calculation record, generate a health management prescription through AI calculation and send it to the user. At the same time, store the health management prescription and calculation time in the health management database.

[0012] S4: Set the validity period of health management prescriptions according to age groups. When a user enters the same main prompt and / or auxiliary prompt, first check the historical calculation records of the prompts in that group. If the last calculation time is within the validity period, directly extract the historical calculation results and send them to the client. If the last calculation time exceeds the validity period, recalculate using AI to generate a new health management prescription and send it to the user. At the same time, store the new health management prescription and calculation time in the health management database.

[0013] The present invention also provides a lightweight AI health management system, comprising:

[0014] The prompt manager is configured to create and manage primary and / or secondary prompts for human health. The primary prompts include age, gender, height, and weight, while the secondary prompts include physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets.

[0015] The crowd segmentation manager is configured to perform combined segmentation of the crowd based on the primary cue words and / or secondary cue words;

[0016] The health management prescription manager is configured to set an age threshold that needs to be combined with auxiliary prompts. When a user enters their main prompts for health management, the system determines whether the user needs to combine the auxiliary prompts for a combined calculation based on their age. Then, it checks whether there are any AI calculation records. If there are no historical calculation records, the system generates a health management prescription through AI calculation and sends it to the user's terminal. At the same time, the health management prescription and the calculation time are stored in the health management database.

[0017] The health management prescription updater is configured to set the validity period of health management prescriptions by age / age group. When a user enters the same main prompt and / or auxiliary prompt, it first checks the historical calculation records of the prompt group. If the last calculation time is within the validity period, the historical calculation result is directly extracted and sent to the client. If the last calculation time exceeds the validity period, a new health management prescription is recalculated using AI and sent to the user. At the same time, the new health management prescription and calculation time are stored in the health management database.

[0018] Each manager is modularly configured and works together to implement all the steps of the above-mentioned lightweight AI health management method, enabling integrated operation of prompt word management, population segmentation, prescription generation, prescription updating and historical data reuse.

[0019] Beneficial effects

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. Significantly reduce computing power consumption: By establishing standardized primary and secondary health prompts and combining them with expiration date management, the reuse of health management prescriptions with the same prompt combination can be achieved, avoiding repeated full AI calculations, significantly reducing the system's computing power requirements, and enabling lightweight computing power operation;

[0022] 2. Refined and Personalized Population Segmentation: The system combines key indicators such as age, gender, height, and weight with more comprehensive auxiliary indicators like physical fitness, unhealthy habits, stress levels, and medical history in stages to create segmented populations. Specifically, the inclusion of auxiliary indicators is dynamically determined based on age, allowing for more targeted health management prescriptions to be generated for different groups. For example, minors experience rapid changes in height and weight but rarely suffer from physiological diseases, thus eliminating the need for auxiliary indicators and allowing for shorter prescription validity periods (e.g., 180 days). In contrast, young adults and middle-aged / elderly adults show little change in height and weight, and often experience some illnesses and / or sub-health conditions. Therefore, more precise health management requires the use of auxiliary indicators, allowing for longer prescription validity periods (e.g., 360 days).

[0023] 3. Standardization and efficiency of health management data: By establishing a unified health prompt word system, standardized storage of health management data can be achieved. At the same time, the validity period of health management prescriptions can be set according to age groups, which can not only ensure the efficient retrieval of historical data, but also ensure the timeliness of prescriptions through recalculation.

[0024] 4. Modular and easy-to-maintain system architecture: The system is divided into multiple managers according to function. Each module can be configured independently but works together, which facilitates subsequent function upgrades, parameter adjustments and system maintenance. It also supports flexible modification of prompt words and prescription validity period.

[0025] 5. Easy to operate and excellent user experience: Users only need to enter standardized main and auxiliary prompts to quickly obtain health management prescriptions without submitting a large amount of complex data, simplifying the user operation process and improving the convenience of health management. Detailed Implementation

[0026] The technical details of the embodiments of the present invention will be clearly and completely described below in conjunction with the technical solutions of the present invention. Obviously, the described embodiments are merely preferred embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0027] Example 1: Lightweight AI Health Management Method

[0028] This embodiment provides a lightweight AI health management method, and the specific implementation steps are as follows:

[0029] 1. Step S1: Establish and manage primary and secondary cue words for human health.

[0030] S11: Establish age-related prompts. These can be based on specific ages or age groups, for example: 1-3 years old is the infant group, 4-15 years old is the minor group, 16-45 years old is the youth group, 46-60 years old is the middle-aged group, 61-75 years old is the adult group, 76-90 years old is the elderly group, and 91 years old and above is the late-life group.

[0031] For a more refined classification, age-related prompts can be processed in a more granular way, further subdividing the underage group into: 1-3 years old as infants, 4-6 years old as toddlers, 7-9 years old as primary school students, 10-12 years old as upper primary school students, and 13-15 years old as junior high school students; and further subdividing the adult group by 5-year intervals into: 16-20 years old as students, 21-25 years old as teenagers, 26-30 years old as young adults, and 31-35 years old as... The age groups are divided into: young and middle-aged group, 36-40 years old group, 41-45 years old group, 46-50 years old group, 51-55 years old group, 56-60 years old group, 61-65 years old group, 66-70 years old group, 71-75 years old group, 76-80 years old group, 81-85 years old group, and 86-90 years old group.

[0032] S12: Establish gender prompts, including standardized prompts for both male and female genders.

[0033] S13: Create height prompts, which can be in centimeters or millimeters.

[0034] S14: Create weight prompts, which can be in kilograms or pounds.

[0035] S15: Establish auxiliary prompts, which include, but are not limited to, physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets. Physical fitness prompts may include: good, fair, satisfactory, poor, and may also include: vital capacity (ml), number of steps in place in 2 minutes, grip strength (kg), number of sit-to-stand repetitions in 30 seconds, number of dumbbell curls in 30 seconds, seated forward bend (cm, fingertips past toes is considered positive), single-leg standing with eyes closed (seconds, foot 20cm off the ground), choice reaction time (seconds), daily walking speed (meters / second). Bad habit prompts may include: alcohol consumption (ml / month), number of cigarettes smoked per day, and sedentary hours per day. The pressure level indicators may include: mild, moderate, and severe. The trauma history indicators may include: time of occurrence, type of trauma, treatment, and control status. The surgical history indicators may include: surgery date, surgery name, and recovery status. The allergy history indicators may include: date of occurrence, drug-induced, food-induced, contact-induced, and recovery status. The family history of genetic diseases indicators may include: name, kinship, gender, date of birth, disease name, and diagnosis date. The abnormal physical examination indicators may include: examination date, abnormal item, indicator value, unit, and conclusion. The long-term use of medicinal diet indicators may include: start time, reason, medicinal diet name, dosage, and end time.

[0036] 2. Step S2: Perform combined segmentation of the population based on the main cue words and / or auxiliary cue words.

[0037] S21: Establish a combination classification for the infant and minor groups, combining only age or age group, gender, height, and weight keywords.

[0038] S22: Establish body type classification for the youth group, combining only age group, gender, height, weight, and fitness cues.

[0039] S23: Establish a combination classification for middle-aged and young adult groups, and combine age group, gender, height, and weight prompts, and add some or all auxiliary prompts for combination.

[0040] S24: Establish a combined classification of the elderly and late-life groups, combining age group, gender, height, and weight prompts, and adding some or all auxiliary prompts for combination.

[0041] 3. Step S3: AI calculation and prescription generation when there is no historical record

[0042] S31: The user enters their main prompt and / or auxiliary prompt according to the prompt in the input box. The system first determines whether the user needs to combine the auxiliary prompt with the calculation based on the preset "age that needs to be combined with auxiliary prompt". The age that needs to be combined with auxiliary prompt can be set to the adult group, or a certain age, such as 36 years old or 45 years old.

[0043] S32: The system checks whether the set of prompt words has an AI calculation history.

[0044] S33: If there is no AI calculation history, the system sends the set of prompt words to the AI ​​module for calculation. The AI ​​module generates the corresponding health management prescription based on the population classification characteristics and health management algorithm, and sends the prescription to the user client in real time. At the same time, the set of prompt words, the corresponding health management prescription and the calculation time are associated and stored in the health management database.

[0045] 4. Step S4: Retrieval and recalculation of prescriptions with historical records

[0046] S41: The system pre-sets the validity period of historical health management prescriptions according to age groups, for example, 180 days for minors and 360 days for others.

[0047] S42: When a user inputs the same primary and / or secondary prompt words, the system first performs unit unification: the system automatically converts the input height / weight prompt words to millimeters and pounds to kilograms. If the units are consistent after conversion, it is determined to be the same prompt word combination. If a historical measurement record of this set of prompt words is detected in the database, the last measurement time is extracted and compared with the current time. If the last measurement time is within the validity period, the historical health management prescription is directly retrieved from the health management database and sent to the client.

[0048] S43: If the historical health management prescription has exceeded the validity period, the system will re-enter the set of prompt words into the AI ​​module for calculation and output a new health management prescription, while storing the new output time and the new health management prescription in the health management database.

[0049] Example 2: Lightweight AI Health Management System

[0050] This embodiment provides a lightweight AI health management system for implementing the method in Embodiment 1. The system's hardware architecture includes a server, a client, and a storage device. The software architecture is designed with functional modularity, specifically including:

[0051] 1. Prompt word manager: Deployed on the server side, it includes units for creating age, gender, height, weight and various auxiliary prompt words, respectively realizing the creation, modification, deletion and standardized management of prompt words in S11-S15 of Example 1.

[0052] 2. Population Segmentation Manager: Deployed on the server side, it includes multiple segmentation units such as infant and minor group, youth group, middle-aged and adult group, and elderly and late-aged group. According to the rules of S21-S24 in Example 1, the population is combined and segmented based on the main and auxiliary prompt words to generate a standardized population segmentation library.

[0053] 3. Health Management Prescription Manager: Deployed on the server side, it communicates with the client, the AI ​​calculation module, and the health management database. Its core function is to determine whether the user needs to combine auxiliary prompts based on preset age rules, and then perform historical record detection, AI calculation triggering when there are no records, prescription generation, and data storage, corresponding to step S3 in Embodiment 1; the AI ​​calculation module performs template matching and local parameter fine-tuning based on the population subtyping library, rather than full model inference, further reducing computing power consumption and achieving lightweight operation.

[0054] 4. Health Management Prescription Updater: Deployed on the server side, it communicates with the health management prescription manager and the health management database to set and modify prescription validity periods, as well as determine validity periods when there are historical records, retrieve historical prescriptions, and trigger recalculation when prescriptions expire, corresponding to step S4 in Embodiment 1. It can also be configured to receive operation instructions from the administrator to modify and adjust the validity periods of health management data according to age groups.

[0055] 5. Health Management Database: Deployed on the storage device, it communicates with all the managers mentioned above and is used to standardize the storage of all relevant data such as health management prescriptions and calculation times corresponding to the main prompts and / or some auxiliary prompts for all users, supporting fast query and retrieval.

[0056] 6. AI Calculation Module: Deployed on the server side, it communicates with the client and the health management database to perform calculations based on prompts entered by the client and provide corresponding health management prescriptions.

[0057] 7. Client: This can be a computer or a mobile device. It communicates with the server, prompts users to input words, and receives health management prescriptions sent by the server.

[0058] The various modules of this system work together. Users operate through clients such as mobile phones and computers, while administrators configure and modify prompts, validity periods, and demographic rules through the management backend on the server. The entire system operates with low computing power, significantly reducing the computing load on the server. Attached Figure Description

[0059] The present invention will be further described below with reference to the accompanying drawings. The drawings are simplified schematic diagrams and are only used to clearly and completely illustrate the technical solution of the present invention, and are not intended to limit the present invention.

[0060] Figure 1 This is a schematic overall process diagram of the lightweight AI health management method of the present invention;

[0061] Figure 2 This is a schematic diagram of the process for establishing health reminder words in step S1 of the present invention;

[0062] Figure 3 This is a schematic diagram of the population combination typing process in step S2 of the present invention;

[0063] Figure 4 This is a schematic diagram of the modular architecture of the lightweight AI health management system of the present invention, where 10 represents the prompt word manager, 20 represents the population classification manager, 30 represents the health management prescription manager, 40 represents the health management prescription updater, 50 represents the health management database, 60 represents the AI ​​calculation module, and 70 represents the client.

[0064] Figure 1 This is a schematic overall process diagram of the lightweight AI health management method of the present invention; as shown. Figure 1 As shown, the lightweight AI health management method of the present invention mainly includes four core steps: First, establish and manage the main prompt words and auxiliary prompt words for human health (S1); then, perform combination classification of the population based on the prompt words (S2); next, set the age that needs to be combined with auxiliary prompt words. When the user inputs their own main prompt words, determine whether it is necessary to combine with auxiliary prompt words for combination calculation based on the age. If there is no historical record, generate a health management prescription through AI calculation and store it (S3); finally, set the validity period of the health management prescription according to age group. For repeated input of the same prompt words, if the historical record is within the validity period, it is directly retrieved; otherwise, it is recalculated and updated (S4). Through the above process, the effective reuse of historical prescriptions is realized, significantly reducing the consumption of AI computing power.

[0065] Figure 2 This is a schematic diagram of the process for establishing health reminder words in step S1 of the present invention; for example... Figure 2As shown, step S1 is further subdivided into five sub-steps: Establishing age prompts (S11), which can be divided by age or age group, and supports finer-grained divisions, such as subdividing the underage group into preschool, primary school, upper primary school, junior high school, etc.; Establishing gender prompts (S12), including standardized prompts for males and females; Establishing height prompts (S13), which can be in centimeters or millimeters; Establishing weight prompts (S14), which can be in kilograms or pounds; Establishing auxiliary prompts (S15), including physical fitness prompts: for example: excellent, good, satisfactory, poor, and can also be: vital capacity (ml), number of steps in place in 2 minutes, grip strength (kg), number of sit-to-stand repetitions in 30 seconds, number of dumbbell curls in 30 seconds, seated forward bend (cm, fingertips past toes is considered positive), single-leg standing with eyes closed (seconds, foot 20cm off the ground), choice reaction time (seconds), daily... The following indicators are included: normal walking speed (m / s); negative habits indicator words may include: alcohol consumption (ml / month), smoking (cigarettes / day), sedentary hours / day; stress level indicator words may include: mild, moderate, severe; trauma history indicator words may include: time of occurrence, type of trauma, treatment, and control status; surgical history indicator words may include: surgery date, surgery name, and recovery status; allergy history indicator words may include: date of occurrence, drug-induced, food-induced, contact-induced, and recovery status; family history of genetic diseases indicator words may include: name, kinship, gender, date of birth, disease name, and diagnosis date; abnormal physical examination indicator words may include: examination date, abnormal item, indicator value, unit, and conclusion; long-term use of medicinal diet indicator words may include: start time, reason, medicinal diet name, dosage, and end time. All indicator words are standardized and stored to form a unified health indicator word system, laying the foundation for subsequent population segmentation and prescription generation.

[0066] Figure 3 This is a schematic diagram of the population combination typing process in step S2 of the present invention; as shown Figure 3 As shown, step S2 differentiates the population based on age groups: for infants and minors (S21), combinations are made using only age or age group, gender, height, and weight cues; for young adults (S22), combinations are made using age group, gender, height, weight, and fitness cues; for middle-aged to adult adults (S23), combinations are made using age group, gender, height, and weight cues, with some or all additional cues; for the elderly to late-aged (S24), combinations are made using age group, gender, height, and weight cues, with some or all additional cues. This phased and differentiated combination classification strategy ensures both precise classification and consideration of the health characteristics differences among different age groups, making the generated health management prescriptions more targeted.

[0067] Figure 4This is a schematic diagram of the modular architecture of the lightweight AI health management system of the present invention; as shown. Figure 4 As shown, this system adopts a modular architecture design, mainly including: a prompt word manager, responsible for establishing and managing primary and secondary prompt words; a population classification manager, responsible for combining and classifying populations based on prompt words; a health management prescription manager, responsible for determining whether to combine secondary prompt words based on age rules, checking historical records, and triggering AI calculation to generate prescriptions and store them in the health management database if no records are found; a health management prescription updater, responsible for setting prescription validity periods, checking whether repeated inputs with historical records are within the validity period, directly retrieving them if they are within the validity period, and recalculating and updating if they are expired; a health management database, responsible for storing all prompt words, population classification data, health management prescriptions, and calculation times; an AI calculation module, responsible for calculating and outputting health management prescriptions based on the input prompt words; and a client, responsible for receiving prompt words input by the user and displaying the generated health management prescriptions. The client has prompt word input verification and error prompting functions, and provides real-time reminders for non-standardized inputs (such as non-numeric height input or missing secondary prompt words) to improve the user experience. The modules communicate and work together through data interfaces to achieve efficient generation of health management prescriptions and intelligent reuse of historical data.

[0068] The accompanying drawings and descriptions provide a more intuitive understanding of the technical solution of this invention, the logical relationships between the steps, and the collaborative working principle of the system modules, which helps those skilled in the art to better implement this invention.

[0069] Scope of Protection Statement: The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It should be noted that the above embodiments are merely illustrative of the core concept and technical solution of the present invention. Any modifications, equivalent substitutions, improvements, supplements, or extraction of some features based on the technical concept of the present invention, as well as any changes, modifications, substitutions, combinations, or simplifications made without departing from the principles of the present invention, all fall within the scope of the technical concept for which protection is sought. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the actual definition of the claims; the specification and drawings are only used to interpret the content of the claims.

Claims

1. A lightweight AI-based health management method, characterized in that, Includes the following steps: S1: Establish and manage primary and secondary health indicators, including age, gender, height, and weight, and secondary indicators including physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets. S2: Based on the main prompting words and / or auxiliary prompting words, the population is combined and classified; S3: Set the age threshold that needs to be combined with auxiliary prompts. When a user enters their main prompt for health management, determine whether the user needs to combine with auxiliary prompts for combined calculation based on their age. Then check if there is an AI calculation record. If there is no historical calculation record, generate a health management prescription through AI calculation and send it to the user. At the same time, store the health management prescription and calculation time in the health management database. S4: Set the validity period of health management prescriptions by age / age group. When a user enters the same main prompt and / or auxiliary prompt, first check the historical calculation records of the prompt group. If the last calculation time is within the validity period, directly extract the historical calculation results and send them to the client. If the last calculation time exceeds the validity period, recalculate using AI to generate a new health management prescription and send it to the user. At the same time, store the new health management prescription and calculation time in the health management database.

2. A lightweight AI health management system, characterized in that, include: The prompt manager is configured to create and manage primary and / or secondary prompts for human health. The primary prompts include age, gender, height, and weight, while the secondary prompts include physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets. The crowd segmentation manager is configured to perform combined segmentation of the crowd based on the primary cue words and / or secondary cue words; The health management prescription manager is configured to set an age threshold that needs to be combined with auxiliary prompts. When a user enters their main prompts for health management, the system determines whether the user needs to combine the auxiliary prompts for a combined calculation based on their age. Then, it checks whether there are any AI calculation records. If there are no historical calculation records, the system generates a health management prescription through AI calculation and sends it to the user's terminal. At the same time, the health management prescription and the calculation time are stored in the health management database. The health management prescription updater is configured to set the validity period of health management prescriptions by age / age group. It can also be configured to receive operation instructions from the administrator to modify and adjust the validity period of health management prescriptions. When a user enters the same main prompt and / or auxiliary prompt, the system first checks the historical calculation records of that set of prompts. If the last calculation time is within the validity period, the system directly extracts the historical calculation results and sends them to the client. If the last calculation time exceeds the validity period, the system recalculates using AI to generate a new health management prescription and sends it to the user. At the same time, the new health management prescription and calculation time are stored in the health management database.

3. The lightweight AI health management method according to claim 1, characterized in that, Step S1 includes the following sub-steps: S11: Establish age prompts, which can be divided by age or by age group, for example, 1-3 years old is the infant group, 4-15 years old is the minor group, 16-45 years old is the youth group, 46-60 years old is the middle-aged group, 61-75 years old is the adult group, 76-90 years old is the elderly group, and 91 years old and above is the old age group; S12: Establish gender prompt words, including male and female; S13: Establish a height prompt word, which can be in centimeters or millimeters; S14: Establish a weight prompt word, which can be in kilograms or pounds; S15: Establish auxiliary prompts, including physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diets, etc.

4. The lightweight AI health management method according to claim 3, characterized in that, In step S11, the age prompts can be further subdivided, with the underage group further divided into: 4-6 years old as preschoolers, 7-9 years old as primary school students, 10-12 years old as upper primary school students, and 13-15 years old as junior high school students. The adult group can also be subdivided in 5-year intervals: 16-20 years old as students, 21-25 years old as teenagers, 26-30 years old as young adults, and 31-35 years old as middle school students. The age groups are divided into four groups: youth, 36-40 (senior youth), 41-45 (advanced youth), 46-50 (young middle-aged), 51-55 (young middle-aged), 56-60 (senior middle-aged), 61-65 (young adult), 66-70 (middle adult), 71-75 (senior adult), 76-80 (young elderly), 81-85 (middle elderly), and 86-90 (senior elderly).

5. The lightweight AI health management method according to claim 1, characterized in that, Step S2 includes the following sub-steps: S21: Establish a combined classification of the infant and minor groups, combining only age or age group, gender, height, and weight cues; S22: Establish a combination classification for the youth group, combining only age group, gender, height, weight, and fitness cues; S23: Establish a combination classification of middle-aged and young adult groups, and combine age group, gender, height, and weight prompts. Some or all of the auxiliary prompts can be added for combination. S24: Establish a combined classification of the elderly and late-life groups, combining age group, gender, height, and weight prompts, and adding some or all auxiliary prompts for combination.

6. The lightweight AI health management method according to claim 1, characterized in that, Step S3 includes the following sub-steps: S31: The user enters their main prompt and / or auxiliary prompt according to the prompts in the input box; S32: The system checks whether the set of prompt words has an AI calculation history; S33: If there is no AI calculation history, send the set of prompts to the AI ​​module for calculation and output a health management prescription. At the same time, store the output time and health management prescription in the health management database.

7. The lightweight AI health management method according to claim 1, characterized in that, Step S4 includes the following sub-steps: S41: The system pre-sets the validity period of historical health management prescriptions according to age groups; S42: If the set of prompts has an AI calculation history, determine whether the historical health management prescription is within the validity period. If it is within the validity period, directly retrieve the historical health management prescription from the health management database and send it to the client. S43: If the historical health management prescription has exceeded the validity period, the set of prompt words is re-entered into the AI ​​module for calculation and a new health management prescription is output. At the same time, the new output time and the new health management prescription are stored in the health management database.

8. The lightweight AI health management system according to claim 2, characterized in that, The prompt word manager includes: The age prompt word establishment unit can be divided into groups according to age, or into groups according to age range, for example: 1-3 years old is the infant group, 4-15 years old is the minor group, 16-45 years old is the youth group, 46-60 years old is the middle-aged group, 61-75 years old is the adult group, 76-90 years old is the elderly group, and 91 years old and above is the old age group. The gender prompt word creation unit is configured to create gender prompt words for males and females; The height prompt word creation unit is configured to create height prompt words in centimeters or millimeters. The weight prompt word creation unit is configured to create weight prompt words in kilograms or pounds. The auxiliary prompt word creation unit is configured to include prompt word creation units such as physical fitness, bad habits, stress level, history of trauma, surgical history, allergy history, family history of genetic diseases, abnormal physical examination items, and long-term use of medicinal diet items.

9. The lightweight AI health management system according to claim 2, characterized in that, The population classification manager includes infant and minor group classification units, youth group classification units, middle-aged and adult group classification units, and elderly and late-aged group classification units. The infant and minor group classification units are configured to combine only age or age group, gender, height, and weight prompts; The youth group classification unit is configured to combine only age group, gender, height, weight, and fitness cues; The middle-aged and adult-aged group classification unit is configured to combine age group, gender, height, and weight prompts, and can add some or all of the auxiliary prompts for combination; The elderly and late-age group classification unit is configured to combine age group, gender, height, and weight prompts, and may add some or all of the auxiliary prompts for combination.

10. The lightweight AI health management system according to claim 2, characterized in that, The system also includes a health management database, configured to store health management prescriptions and calculation times corresponding to the main prompts and / or some auxiliary prompts for all users.

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