Blood glucose management system for gestation period

Abstract

The invention relates to a blood glucose management system for a gestation period, which comprises terminal equipment carried by a target user and a reference user and a cloud server in data connection with the terminal equipment, and the cloud server is configured to obtain physiological data and blood glucose data including the gestation period of the target user from the terminal equipment, calculating a characteristic index of the target user; acquiring various blood glucose data of a reference user from a plurality of terminal devices, forming an overall reference data set according to a manner associated with gestational weeks, and distributing the target user to a predetermined feature index value interval in the overall reference data set based on the feature index, and providing personalized health management suggestions for the target user based on the identified factors influencing the gestational diabetes risk of the target user.

Description

Technical Field

[0001] The present invention relates to the technical field of medical management, and particularly to a blood glucose management system for pregnancy. Background Art

[0002] Gestational diabetes mellitus (GDM) refers to the abnormal glucose metabolism that first appears or is diagnosed during pregnancy, and is one of the most common complications during the perinatal period. According to the statistical data of the International Diabetes Federation (IDF) in 2019, the incidence rate of GDM globally is approximately 13.2%, and it shows an increasing trend year by year. As an important public health challenge, GDM poses a significant threat to the health of mothers and infants. It not only increases the risks of perinatal complications (such as pregnancy-induced hypertension, preeclampsia, etc.) and adverse pregnancy outcomes (such as macrosomia, increased cesarean section rate, premature birth, etc.), but also is related to the increased likelihood of the mother developing type 2 diabetes (T2DM) and cardiovascular diseases in the future.

[0003] The occurrence mechanism of gestational diabetes mellitus is closely related to the changes in hormone levels in the pregnant woman's body. As pregnancy progresses to the second and third trimesters, various hormones and cytokines secreted by the placenta (such as tumor necrosis factor α, TNF-α) have the effect of antagonizing insulin, resulting in a gradually increasing insulin resistance. When the pregnant woman's own pancreatic β cells cannot fully respond to this physiological insulin resistance, or there is accompanying β cell dysfunction, GDM may occur.

[0004] Effective blood glucose management is crucial for reducing the risks brought by gestational diabetes mellitus (GDM). Traditional intervention measures emphasize controlling blood glucose levels through diet plans customized by professional medical staff. These plans usually include balanced nutritional intake, with special attention paid to the quality and quantity of carbohydrates.

[0005] When formulating a diet plan, a key consideration factor is the impact of carbohydrates in food on blood glucose levels, which can be measured by the glycemic index (GI value). The GI value is an index used to describe the speed and degree of blood glucose elevation caused by carbohydrates in a specific food. It is based on the standards obtained from tests on healthy non-diabetic adult populations, requiring the subjects to have normal fasting blood glucose levels and no other diseases affecting carbohydrate metabolism. The GI value helps identify which foods cause a rapid increase in blood glucose (high GI), and which foods can provide a more stable and continuous energy release (low GI).

[0006] However, existing blood glucose management programs generally lack the ability for individualized adjustment and dynamic monitoring, and it is difficult to meet the specific needs of different pregnant women. In addition, traditional dietary recommendations mainly rely on static GI values and do not fully consider individual differences and the impact of real-time blood glucose fluctuations.

[0007] CN114743673A discloses a health management method, device and electronic device for patients with gestational diabetes. This solution interacts with the target user to provide personalized dietary meal recommendations and pregnancy exercise guidance, supplemented by professional dietary and exercise consultations, aiming to promote the active health management of the target user, thereby reducing the probability of complications in GDM patients.

[0008] However, the above technical solution mainly relies on pre-set static data information to formulate dietary meal plans and exercise strategies, and does not fully consider the impact of individual differences on health management. The applicability and effectiveness of this method based on fixed parameters are limited when facing users with different physiques, living habits and health conditions. In addition, due to the lack of an effective feedback mechanism, this solution cannot dynamically adjust the diet and exercise plans according to the real-time monitored blood glucose data of the user. This results in the inability to update the health management measures in a timely manner even when the user's blood glucose level changes, thereby affecting the overall effect of blood glucose management.

[0009] In summary, there is an urgent need to develop a more intelligent and personalized blood glucose management system that can combine real-time blood glucose monitoring data, personal health records and other relevant factors to provide accurate and flexible treatment recommendations for GDM patients, so as to more effectively control blood glucose levels and reduce the risk of maternal and infant complications.

[0010] In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, although the applicant has studied a large number of documents and patents when making this invention, all details and content are not listed in detail due to space limitations. However, this does not mean that this invention does not possess the features of these prior arts. On the contrary, this invention already possesses all the features of the prior arts, and the applicant reserves the right to add relevant prior arts in the background art. Summary of the Invention

[0011] Aiming at the deficiencies of the prior art, the present invention aims to provide a blood glucose management system for pregnancy, which forms overall data through a large number of users using CGM devices (continuous glucose monitors), and provides personalized dietary recommendations according to the position of the user's blood glucose data in the overall data. At the same time, the GI value in the dietary recommendation is dynamically adjusted according to the change of the CGM reading to achieve more effective blood glucose management.

[0012] The present invention relates to a blood glucose management system for pregnancy, which includes a terminal device carried by a target user and a reference user, and a cloud server connected to the terminal device for data. The cloud server is configured to: obtain the physiological data and blood glucose data of the target user including pregnancy from the terminal device, and calculate the characteristic index of the target user accordingly; obtain various blood glucose data of the reference users from a number of terminal devices and form an overall reference data set in a manner associated with the gestational week, and assign the target user to a predetermined characteristic index value range in the overall reference data set based on the characteristic index, and determine the factors affecting the risk of gestational diabetes in the target user, so that the target user can manage the corresponding physiological parameters according to these factors.

[0013] The personalized allocation mechanism of the system of the present invention makes the health management advice more in line with the actual situation of the target user and improves the effectiveness of health management. By identifying the factors affecting the risk of gestational diabetes in the target user, the system can timely detect potential health risks. This risk identification mechanism provides a basis for clinical intervention, helps reduce the incidence of gestational diabetes, and improves the health level of pregnant women and fetuses. In addition, by integrating the data of the reference users into the overall reference data set, the system can utilize the advantages of big data analysis to improve the accuracy and reliability of the analysis. This integration of group data provides a more extensive reference basis for the calculation of the characteristic index and the generation of health advice.

[0014] In the blood glucose management system for pregnancy, "physiological parameters" refer to a set of basic indicators reflecting the individual health status of pregnant women and the dynamic changes of pregnancy, including BMI (body mass index), gestational week, weight gain rate, hormone levels (such as progesterone, hCG, estradiol), and continuous blood glucose monitoring data. These parameters are collected in real time through the terminal device or actively input by the user, constituting the original data basis for the system to evaluate the risk of gestational diabetes (GDM). For example, too high BMI may indicate the risk of obesity-related insulin resistance, and abnormal hormone levels can be directly related to the impact of placental function on glucose metabolism. The system calculates the characteristic index (FI) to integrate multi-dimensional physiological parameters into a standardized score value, eliminating the dimension difference and enabling cross-individual comparison, providing a unified framework for subsequent analysis.

[0015] "Key parameters" are the core factors selected from physiological parameters that have the most significant impact on the GDM risk of a specific user. Through a dynamic grouping strategy, the system classifies target users and reference users with highly similar gestational weeks, BMI, and weight gain rate into the same homogeneous subgroup, and then compares the deviation of their physiological parameters from the group mean. For example, if a user's BMI deviates from the group mean by 2 standard deviations and the weight gain rate exceeds the medical recommendation threshold, both are identified as key parameters; if the estradiol level increases abnormally by 40%, hormonal imbalance may further exacerbate insulin resistance, thus forming a compound risk profile. This precise identification mechanism avoids the drawbacks of the traditional approach of comprehensively intervening in all parameters and instead focuses on specific indicators that most need to be regulated.

[0016] The objective of the technical solution of the present invention is to solve the pain point of "blind intervention leading to metabolic disorders" in traditional blood glucose management. Through data-driven dynamic analysis, the system guides users to adjust their behaviors only for key parameters, rather than generally restricting diet or exercise. For example, for those with a too high BMI, it recommends adjusting the diet structure with low-GI foods (such as replacing white bread with oats) instead of simply reducing food intake, thus avoiding the cortisol increase and blood glucose rebound caused by insufficient energy intake; for those with an abnormal weight gain rate, it designs a daily calorie deficit of 300 kcal (achieved through diet records and low-intensity walking) to avoid the stress response caused by strenuous exercise. At the same time, the system dynamically adjusts the intervention intensity by continuously monitoring the changes in key parameters: if the user's weight gain rate meets the standard for two consecutive weeks, the GI limit is automatically relaxed to below 65; if there is no improvement, it triggers a medical collaboration process to ensure that the risk is controllable. This strategy optimizes blood glucose control while maintaining hormonal homeostasis and metabolic balance.

[0017] According to a preferred implementation, the cloud server further divides the reference users in this characteristic index range into multiple pregnancy groups according to the gestational weeks, and the cloud server further analyzes the deviation between the physiological parameters of the target user and the average value of the reference users in the same pregnancy group, so as to determine one or several key parameters among several physiological parameters that affect the blood glucose level of the target user. Among them, the key parameters include BMI, weight gain rate, and hormone level. This grouping method takes into account the physiological changes in different pregnancy stages, making the analysis more relevant and accurate, and helping to identify specific pregnancy risks. By analyzing the deviation between the physiological parameters of the target user and the average value of the reference users in the same pregnancy group, the system can identify the outliers of the target user. This method effectively helps to identify the key parameters affecting the blood glucose level and ensures the pertinence of the analysis. Once the key parameters affecting the blood glucose level are identified, the cloud server can provide personalized health management suggestions for the target user based on these factors. For example, for users with a too high BMI, the system can recommend adjusting the diet and increasing exercise, and for users with an abnormal weight gain rate, corresponding intervention measures can be formulated. This personalized recommendation helps to improve the health management effect of users.

[0018] Based on the Feature Index (FI) value, the cloud server can classify users with similar FI values into the same group. This way of integrating multiple factors affecting the risk of gestational diabetes into a comprehensive index enables the cloud server to standardize these multi-dimensional data. This means that even if the target user and each reference user have different performances in individual factors, they can find commonalities or similarities through the FI value, thus achieving effective cross-individual comparison. This grouping method ensures a high degree of homogeneity among group members, that is, they show similar trends or characteristics in multiple key health indicators. Therefore, comparing and analyzing blood glucose data within such a group is more meaningful because they are more likely to reflect the true impact of specific factors on blood glucose control rather than accidental fluctuations.

[0019] According to a preferred embodiment, the cloud server can calculate the percentile of the relative position of the target user based on the daily trend blood glucose data within the pregnancy group to which the target user belongs, and identify the blood glucose fluctuation pattern and potential abnormalities of the target user through comparative analysis with the blood glucose data of reference users. By calculating the relative position percentile of the target user's blood glucose data within its pregnancy group, the system can provide a relative health assessment indicator. This assessment can help users understand their position of blood glucose level among users in the same pregnancy, and thus better understand their own health status.

[0020] According to a preferred embodiment, the terminal device includes a collection unit for acquiring continuous blood glucose data. The terminal device uploads the blood glucose data recorded by the collection unit to the cloud server in a gestational week-associated manner through the configured communication unit, and receives personalized diet advice from the cloud server. By using a continuous blood glucose data collection unit, continuous monitoring of blood glucose levels can be achieved. Compared with the traditional point measurement method, it can provide more comprehensive and accurate information on blood glucose change trends. Associating the blood glucose data with the gestational week for uploading enables the system to more accurately analyze the blood glucose change characteristics in different pregnancies, thus providing more personalized advice that conforms to the physiological characteristics of pregnancy. In addition, the design of the terminal device allows users to perform blood glucose monitoring and data upload anytime and anywhere, improving the convenience and compliance of use.

[0021] According to a preferred embodiment, the terminal device is integrated with an interaction unit that is signal-connected to the communication unit. The interaction unit is configured to receive and display dietary suggestions from the cloud server and the storage unit to the user, and provide a platform for the user to input and update personal health information including the current gestational week, weight change, and dietary records. The interaction unit can receive and display dietary suggestions from the cloud server and the storage unit, providing a real-time information exchange platform for the user. This instant feedback mechanism helps the user adjust their eating habits according to the latest suggestions. In addition, the interaction unit provides a platform for the user to input and update personal health information, which encourages the user to actively participate in the health management process. The user can input information such as the current gestational week, weight change, and dietary records, enabling the system to better understand their personal health status.

[0022] According to a preferred embodiment, the cloud server groups the obtained overall reference data set based on similar gestational weeks. Among them, the cloud server adjusts the time window range of similar gestational weeks according to the stage of the user's pregnancy: for the early pregnancy stage and the late pregnancy stage, a relatively narrower time window is adopted; for the mid-pregnancy stage, a relatively wider time window is adopted. By grouping the overall reference data set based on similar gestational weeks, the cloud server can provide more accurate and relevant data analysis. This grouping method ensures that the data for comparison and analysis has higher relevance and comparability. In addition, adjusting the time window range of similar gestational weeks according to different stages of the user's pregnancy reflects the dynamic adaptability of the system. This flexible adjustment mechanism also better matches the physiological change characteristics of different stages during pregnancy.

[0023] According to a preferred embodiment, the cloud server can comprehensively group the overall reference data set according to the physiological data input by the user through the interaction unit of the terminal device, combining multi-dimensional grouping methods to improve the homogeneity of the members within each group. The physiological data includes the user's own BMI and weight gain rate regularly input or updated by the user. By comprehensively grouping the reference data set according to the physiological data input by the user (such as BMI and weight gain rate), the homogeneity of the members within each group can be significantly improved. This grouping method ensures the comparability and consistency of the data within the group, especially improving the accuracy of the user's corrected characteristic index.

[0024] According to a preferred embodiment, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in the distribution of its group, compares the user's blood sugar data with the preset healthy blood sugar range to determine the user's current blood sugar state, and generates graded dietary recommendations based on this. The dietary recommendations optimize the target user's dietary structure by selecting food types with different GI values. By calculating the basic statistical indicators of each group, the cloud server can accurately locate the specific percentile of the user's blood sugar data in its group. This method makes blood sugar assessment more accurate and can identify the user's current blood sugar state. The user's blood sugar data is compared with the preset healthy blood sugar range, which helps to individually assess the user's health status. This allows customized health recommendations to be provided for users with different blood sugar states. In addition, by selecting food types with different GI values ​​(glycemic index), the system can help users optimize their dietary structure. This method helps to stabilize blood sugar fluctuations and improve overall health.

[0025] According to a preferred embodiment, the cloud server is configured to divide users into different groups according to gestational age. Within each gestational age group, multiple BMI ranges are set so that users can be subdivided into multiple BMI subgroups. Within the same BMI subgroup, the cloud server can further subdivide users according to weight gain rate to identify users who gain weight too fast or too slow and assign them to special attention groups. The division of different groups and subgroups allows personalized health guidance and suggestions. This guidance can be adjusted according to the user's specific gestational age and weight changes to improve the effectiveness of health management.

[0026] According to a preferred embodiment, the cloud server can periodically re-evaluate the target user's gestational age information and adjust the group to which it belongs accordingly; the cloud server allows the user to object to the automatically assigned group and receive the gestational age information manually adjusted by the user through the interactive unit. Allowing the user to object to the automatically assigned group and manually adjust the gestational age information, on the one hand, helps the system correct possible errors or inaccuracies, thereby improving the accuracy of the overall data; on the other hand, it enhances the user's sense of participation and control, and improves the user's acceptance and compliance with health management recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a hardware topology diagram of a preferred blood glucose management system during pregnancy provided by the present invention;

[0028] Figure 2 It is a schematic diagram of a preferred process provided by the present invention for a cloud server to obtain a characteristic index representing the high or low blood sugar risk of a user during gestational diabetes;

[0029] Figure 3It is a schematic flow chart of the preferred process for the cloud server to divide the gestational weeks provided by the present invention;

[0030] Figure 4 It is a schematic flow chart of the preferred process for the cloud server to homogenize and group the overall reference data set according to gestational weeks, BMI, and weight gain rate provided by the present invention;

[0031] Figure 5 It is a hardware scenario diagram of the preferred gestational diabetes management system provided by the present invention;

[0032] Figure 6a It is an example diagram provided by the present invention showing the change of characteristic index with gestational weeks in the early pregnancy stage for a certain target user drawn by the cloud server;

[0033] Figure 6b It is an example diagram provided by the present invention showing the change of characteristic index with gestational weeks in the middle pregnancy stage for a certain target user drawn by the cloud server;

[0034] Figure 6c It is an example diagram provided by the present invention showing the change of characteristic index with gestational weeks in the late pregnancy stage for a certain target user drawn by the cloud server;

[0035] Figure 7 It is a comparison diagram provided by the present invention showing the blood glucose data of a certain target user drawn by the cloud server compared with the blood glucose data of other reference users within the same FI interval;

[0036] Figure 8 It is a daily trend blood glucose comparison diagram of an example target user provided by the present invention.

[0037] List of reference numerals

[0038] 100: Terminal device; 110: Acquisition unit; 120: Communication unit; 130: Storage unit; 140: Interaction unit; 200: Cloud server. Detailed implementation manners

[0039] The following is a detailed description with reference to the accompanying drawings.

[0040] In this application, the target user and the reference user refer to two types of pregnant women participating in the gestational diabetes management system. These two terms are used to distinguish individuals who play different but related roles within the same system.

[0041] The target users refer to pregnant women who are currently using the system and seeking personalized blood glucose management and dietary advice. These women usually have relatively high blood glucose levels or have a need for blood glucose management, and they optimize their own health management with the help of the system, especially for effectively controlling blood glucose levels. Their data is used to generate personalized health guidance programs.

[0042] Reference users refer to other pregnant women who are also in the pregnancy period and can share their own blood glucose data with the system. The blood glucose levels of these pregnant women usually remain within the normal range, and the anonymized data they provide constitutes a large reference database, providing a comparison benchmark and population analysis support for the target users. The data of reference users helps to establish typical blood glucose patterns at each pregnancy stage.

[0043] The identities of target users and reference users are not fixed. If a reference user who was originally a data contributor starts to need personalized blood glucose management services, she can activate all the functions of the intelligent monitoring terminal device 100 and transform into a target user who enjoys comprehensive services. This situation can occur when the reference user finds that her blood glucose level has increased. Conversely, if a target user has achieved effective blood glucose control or only wishes to continue contributing data to help others, she can choose to keep the basic monitoring function of the device turned on and continue to be a provider of reference data without receiving further personalized services.

[0044] Embodiment 1

[0045] The present invention relates to an intelligent system for blood glucose management during pregnancy, as Figure 1 , Figure 4 shown, the system includes a plurality of terminal devices 100 and a cloud server 200. The terminal device 100 can be a wearable device such as a smart bracelet or a smart watch, which is worn by target users and reference users for real-time collection and viewing of blood glucose data. At the same time, the terminal device 100 can intuitively display personalized dietary advice that matches the blood glucose condition of the target user to the target user. The cloud server 200, with its powerful computing power and storage capacity, quickly processes and deeply analyzes the massive data collected by the terminal, so as to generate optimized dietary advice for the target user.

[0046] Preferably, as Figure 1 shown, the system can support the access of multiple terminal devices 100 to cover more target users and reference users. Each terminal device 100 interacts with the cloud server 200 through a data connection to achieve data upload and feedback. The terminal device 100 includes a blood glucose collection unit 110, which can be in the form of a continuous glucose monitor (CGM), and uses a glucose sensor to dynamically monitor the change in glucose concentration in the interstitial fluid of the user's subcutaneous tissue, so as to achieve continuous and dynamic blood glucose data collection.

[0047] Preferably, as Figure 1 shown, the terminal device 100 is further configured with a communication unit 120 for data uploading and receiving. On the one hand, the communication unit 120 can upload the blood glucose data recorded by the acquisition unit 110 to the cloud server 200 in a way associated with the gestational week; on the other hand, it can also receive the personalized diet advice generated by the cloud server 200 for the target user. The communication unit 120 can be composed of a variety of wireless communication modules (such as Bluetooth Low Energy BLE, Wi-Fi, Zigbee, etc.) to ensure the security, reliability and real-time of data transmission.

[0048] Preferably, as Figure 1 shown, the terminal device 100 further includes a storage unit 130 which is equipped with a large-capacity storage space. On the one hand, it can save the personalized diet advice fed back by the cloud server 200; on the other hand, it can store the blood glucose change data of the target user since wearing the terminal device 100 to meet the user's need to view at any time.

[0049] Preferably, as Figure 1 shown, the terminal device 100 is further integrated with an interaction unit 140 which can exist in the form of a mobile application interface. The interaction unit 140 is connected to the communication unit 120 and can directly interact with the target user. It not only provides the user with information such as personalized health advice, reminder items and historical trend charts, but also supports the user to input and update key personal information, such as the current gestational week, weight change, diet record, etc.

[0050] Preferably, due to its stronger computing power and storage capacity than the terminal device 100, the cloud server 200 undertakes the core data processing tasks in the system. The cloud server 200 not only saves the blood glucose data and related information of all target users and reference users, but also deploys an analysis model to deeply analyze the complex blood glucose data and generate scientific and accurate personalized diet advice, so as to effectively assist the gestational diabetes management of the target user.

[0051] Preferably, as Figure 1As shown, the cloud server 200 can be configured to: obtain four physiological data of the target user, namely gestational age, BMI, weight gain rate, and hormone level, from the terminal device 100 of the target user, and initially calculate the feature index FI by substituting the four data into a built-in calculation model. The feature index FI is a comprehensive quantitative index calculated by the cloud server 200 based on various physiological parameters of the target user. It aims to unify pregnant women with different gestational ages, BMIs, weight gain rates, and hormone levels into a standardized comparison framework, so that the target user can be assigned to a group with similar or close FI values for research. By comparing the differences between the blood glucose data of the target users in these groups and the blood glucose data of the reference users, specific factors that have the greatest impact on the risk of gestational diabetes can be identified, and personalized health management suggestions can be provided for the target user.

[0052] Preferably, the cloud server 200 can calculate the feature index FI by means of the following formula:

[0053]

[0054] Since there are differences in the physiological conditions of each pregnant woman, directly comparing their blood glucose data may not accurately reflect the individual's health status. Therefore, the system comprehensively calculates multiple key health indicators (such as body mass index, gestational age, weight gain rate, and hormone level) of pregnant women through the feature index (FI), and adjusts the importance weights of different indicators, and finally generates a unified score value. This process eliminates the unit differences and magnitude effects of different indicators, enabling fair comparison of the complex health data of all pregnant women under the same standard. For example, the system will assign pregnant women with excessive weight gain or abnormal hormone levels to a specific score range and match and analyze their blood glucose data with other pregnant women in the same range. In this way, each pregnant woman can intuitively understand her position in the health status of similar people and clarify the health directions that need to be focused on (such as controlling the weight gain rate or monitoring hormone changes). This standardized scoring and grouping mechanism not only simplifies the complexity of data processing but also helps users manage health risks more targeted.

[0055] In the formula,

[0056] B represents the body mass index (BMI), which is obtained by dividing the weight (P) by the square of the height (H s ):

[0057]

[0058] Body Mass Index (BMI) has a significant impact on the risk of gestational diabetes. A higher BMI is usually associated with insulin resistance. Obesity can lead to a decrease in the body's sensitivity to insulin, which in turn causes an increase in blood sugar levels, thus raising the risk of gestational diabetes. In addition, obesity may also trigger chronic low-grade inflammation, which further affects insulin function and glucose metabolism. During pregnancy, the body needs to adapt to various metabolic changes, and a high BMI may make this adaptation process more complex, increasing the risk of abnormal glucose metabolism.

[0059] W represents the gestational week, which is the number of weeks of pregnancy calculated from the first day of the last menstrual cycle:

[0060] W = D c - LMP

[0061] D c is the current date, and LMP is the date of the last menstrual period. An increase in gestational week may be associated with an increased risk of gestational diabetes, especially in the late pregnancy.

[0062] The current date D c can be automatically obtained through the system time of the target user's terminal device 100 to ensure the real-time and accuracy of the time. The date of the last menstrual period LMP can be input by the user independently, and the specific format requirements for the user input (such as YYYY - MM - DD or other standard date formats) are provided.

[0063] G represents the weight gain rate, which indicates the value (G) of the weight increase per week during pregnancy and can be in kilograms per week.

[0064]

[0065] T c represents the current weight (kg), T pLet \(W_{prev}\) represent the previous measured body weight (kg), and \(\Delta t\) represent the time interval (weeks). Both the current body weight and the previous measured body weight can be obtained by the user regularly uploading body weight data (either manually input or automatically synchronized to the terminal device 100 via a Bluetooth body scale). If the time when the user uploads data is not fixed, \(\Delta t\) can be dynamically calculated in combination with the uploaded timestamp information to ensure the accuracy of the data. For users with a low upload frequency, the cloud server 200 can set a reminder mechanism to prompt the user to regularly upload physiological data via the terminal device 100 to ensure the accuracy of the evaluation results. The rate of weight gain has an important impact on the risk of gestational diabetes. Rapid weight gain may lead to increased insulin resistance, making it more challenging for the body to regulate blood sugar levels. In addition, rapid weight gain during pregnancy increases the metabolic burden, especially in the later stages of pregnancy, which may make blood sugar control more difficult. At the same time, rapid weight gain is often associated with an unhealthy diet and lack of exercise, and these lifestyle factors can further increase the risk of gestational diabetes.

[0066] \(H\) represents the hormone level, and a comprehensive hormone level index is used to reflect the combined effects of multiple hormones.

[0067] \(H = h_1\cdot H_1+h_2\cdot H_2+h_3\cdot H_3\)

[0068] Where:

[0069] \(H_1\), \(H_2\), \(H_3\) represent the concentration values of different hormones (such as progesterone, human chorionic gonadotropin (hCG), estradiol); \(h_1\), \(h_2\), \(h_3\) represent the corresponding weight coefficients.

[0070] The measurement of hormone levels (H1, H2, H3) can be obtained through laboratory tests in medical institutions during the user's regular prenatal check-ups. The types of tests include, for example, progesterone, human chorionic gonadotropin (hCG), estradiol, etc. The user can upload the test report to the terminal device 100, and the terminal device 100 collects data through OCR (image recognition) or manual input by the user. Herein, h1, h2, and h3 can be set based on the initial weight values of medical research. By referring to authoritative medical literature and clinical research, analyze the intensity and correlation of the effects of progesterone (H1), hCG (H2), and estradiol (H3) on the risk of gestational diabetes mellitus (GDM). According to the effect sizes (such as regression coefficients, correlation coefficients, etc.) reported in the research, assign initial weights to each hormone. When the hormone test value received by the communication unit 120 exceeds the safety threshold pre-stored in the storage unit 130, the corresponding sub-weight is automatically increased to 1.5 to 2.0 times the original value. In addition, the weight coefficient can also be corrected based on the weight of statistical analysis. For example, by collecting a large amount of real data of pregnant women, including the concentration values of progesterone (H1), hCG (H2), estradiol (H3), and the diagnosis results of gestational diabetes mellitus. Then, evaluate the independent contribution or correlation of each hormone to the GDM risk by means of Pearson correlation coefficient, multiple linear regression analysis, or generalized linear model.

[0071] The changes in hormone levels during pregnancy have an important impact on the occurrence risk of gestational diabetes mellitus. As pregnancy progresses, the increase in hormone levels such as progesterone, human chorionic gonadotropin (hCG), and estradiol will gradually affect insulin sensitivity and glucose metabolism, leading to an increase in insulin resistance, thereby causing differences in the risk of gestational diabetes mellitus at different gestational weeks.

[0072] w1, w2, w3, and w4 are all weight coefficients, which are the relative importance weights of each factor in risk assessment. By setting weights, the different degrees of influence of various factors on the risk of gestational diabetes can be reflected. The setting of weight coefficients can be based on large-scale medical statistical data, and the weight values ​​can be optimized by methods such as data analysis. For example, the initial value setting of weight coefficients w1 to w4 is linked to the clinical standard database pre-stored in the storage unit 130 of the terminal device 100, which contains the "Guidelines for Health Management during Pregnancy" module regularly updated by cooperative medical institutions, in which BMI and gestational age use the weight-gestational age association weights recommended by the guidelines. "Clinical Pathway for Weight Management during Pregnancy" is a submodule of the database, formulated by consensus of endocrinology experts, and includes differentiated weight growth rate thresholds for early, middle and late pregnancy. The system automatically calls the w3 coefficient calculation logic of the corresponding stage according to the current gestational age value, and the dynamic adjustment range of the hormone level weight w4 can be carried out under the constraints of the gestational age grouping rules. In addition, the linear regression or logistic regression model is trained using historical data to optimize the weights of each parameter. For different populations (such as age, race, geographical region, etc.), an adaptive weight adjustment mechanism can be provided. For example, through the basic information input by the user, the corresponding weight set is loaded, and the weight is dynamically adjusted to reflect the actual risks of different groups of people.

[0073] K represents the exponential constant of weight gain rate, which is used to adjust the intensity of the impact of weight gain rate on risk. By changing the value of k, the nonlinear impact of weight gain rate in risk indicators can be controlled. The value of the weight gain rate exponential constant K can be based on historical medical data, and the initial value range suitable for most pregnant women can be obtained by fitting a nonlinear regression model. At the same time, dynamic adjustment technology can also be used, that is, the value of K can be adjusted according to the actual weight gain of each user. For example, if the weight gain rate is fast, the K value can be increased to better reflect its risk.

[0074] Z is the standardization factor, which is used to normalize the factors of each result, which can avoid the result deviation caused by variables of different dimensions and ranges, making FI more comparable. Its calculation formula is as follows:

[0075] Z=σ(B)+σ(W)+σ(G)+σ(H)

[0076] The calculation of the normalization factor Z can be based on a large-scale sample data set. The data set may include but is not limited to the cloud server 200 obtained by using a large number of reference users' terminal devices 100, and can contain historical statistical data of different populations (e.g., women of childbearing age). The calculation parameters of the normalization factor are regularly updated with the latest user data to maintain the accuracy and timeliness of the model. If the user data is insufficient, the cloud server 200 can be estimated in combination with public medical big data or based on statistical models.

[0077] σ(B), σ(W), σ(G), and σ(H) represent the standard deviation of BMI, the standard deviation of gestational age, the standard deviation of weight gain rate, and the standard deviation of comprehensive hormone level respectively, where

[0078]

[0079] In the formula, B i is the BMI of the i-th user (target user);

[0080] is the average BMI of all users (reference users);

[0081] N is the total number of samples.

[0082] Similarly, the calculation formulas for other standard deviations are as follows:

[0083]

[0084] In the formula, W i , G i and H i are the gestational age, weight gain rate, and comprehensive hormone level of the i-th user (target user) respectively, are the average gestational age, weight gain rate, and comprehensive hormone level of all users (reference users) respectively.

[0085] In summary, based on the built-in calculation model, the cloud server 200 can obtain the characteristic index FI for evaluating the risk of gestational diabetes in the target user. Preferably, as Figure 1 shown, the cloud server 200 can be configured to form a comprehensive overall reference dataset by collecting various blood glucose data including each pregnancy stage from the terminal devices 100 of each reference user. On this basis, after calculating the FI value of the target user, the cloud server 200 can incorporate it into the FI value interval corresponding to the FI value that has been pre-determined. These FI value intervals are obtained by the cloud server 200 through statistical analysis algorithms for homogenization grouping optimization based on a large amount of reference user data, aiming to ensure that users within each interval have highly similar risk characteristics. The purpose of this step is to narrow the comparison range from a large number of reference users, enabling the target user to conduct a more accurate comparative analysis with other reference users having similar FI values.

[0086] Within the same FI value interval, the cloud server 200 first further divides the reference users into multiple pregnancy groups using the gestational age. This stratification strategy takes into account the unique physiological change characteristics of different gestational age stages, ensuring comparison under the same or similar gestational age conditions and improving the relevance and reliability of the analysis results.

[0087] In summary, the Feature Index FI is not only an important bridge connecting individual and population health information, but also an indispensable technical means for realizing intelligent and personalized gestational blood glucose management. It uses a mathematical model to transform complex physiological variables into numerical values that are easy to understand and apply, contributing to the improvement of the user's blood glucose condition.

[0088] Preferably, to improve the homogeneity of grouping, as Figure 3 shown, the cloud server 200 can first group the data based on the gestational age to improve the accuracy of data analysis. Specifically, the cloud server 200 automatically assigns the target user to the corresponding gestational age group according to the latest gestational age information reported by the target user. To maintain the timeliness and accuracy of the data, the cloud server 200 can re-evaluate the gestational age of the target user at regular intervals and adjust the group to which it belongs accordingly. Considering the continuity of the gestational age, the cloud server 200 can set a reasonable range to define "similar" gestational ages, such as ±2 weeks. For example, if a target pregnant woman is at week 20, her blood glucose data will be compared with that of other reference pregnant women between weeks 18 and 22.

[0089] Preferably, the cloud server 200 can adjust the definition of "similar" gestational ages according to the key events in different pregnancy trimesters. The key events can include fetal development milestones and maternal physiological changes.

[0090] In the early pregnancy stage, the hormone levels in the body rise rapidly. In particular, the changes in hormones such as human chorionic gonadotropin (hCG), estrogen, and progesterone significantly affect metabolic functions. This period is the most critical time for embryonic organ formation. As the placenta begins to form and secrete various hormones, the insulin sensitivity of the mother begins to change, which may lead to unstable blood glucose levels. Any external factor (such as blood glucose level) may have a long-term impact on the health of the fetus. In the late pregnancy stage, the fetus grows rapidly, and the maternal metabolic burden increases, especially the demand for glucose increases significantly. As the placenta secretes more hormones that antagonize the action of insulin, the insulin resistance of the mother gradually increases, and the physiological changes are more significant, which makes blood glucose control more difficult. Therefore, in the early pregnancy stage (0 - 12 weeks) and the late pregnancy stage (after 28 weeks), the cloud server 200 uses a finer-grained time window (such as ±1 week or specific days).

[0091] Compared with early pregnancy, hormone fluctuations in the second trimester (12 to 28 weeks) tend to be stable, and the fetal growth rate is accelerated but has not yet reached the highly active state in late pregnancy. During this stage, the weight gain of pregnant women gradually accelerates, but the physiological changes during this period are relatively stable, which is suitable for group comparisons within a larger range. Cloud server 200 uses a time window of ±2 weeks to cover a sufficient number of comparison objects without losing comparability due to a large time span. This setting helps to improve the validity and reliability of statistical analysis.

[0092] After the pregnancy groups are assigned, the cloud server 200 will conduct a detailed comparative analysis based on the target user's blood sugar data and the reference user's blood sugar data in the pregnancy group to which he belongs. The specific steps are as follows: First, calculate the percentile of the target user's blood sugar data in the pregnancy group to which he belongs, in order to quantify his relative position with respect to other reference users. The blood sugar data mentioned here preferably refers to the daily trend blood sugar, that is, the blood sugar changes of the user at different time points of the day obtained by a continuous blood glucose monitor, which is used to show the changing trend of blood sugar levels over time and daily activities in a day, which can be represented by a continuously changing blood sugar line. Figure 7 A diagram showing the daily trend of blood sugar for the reference user and the target user is shown. This approach provides a more comprehensive understanding of the target user's blood sugar fluctuation pattern, rather than just a snapshot at a single point in time.

[0093] If the target user's daily trend blood sugar data is higher than the set threshold (for example, more than 75% of the reference users), it is considered that there is an abnormality. This abnormality may indicate an increased risk of gestational diabetes or the presence of other health problems. For target users marked as abnormal, the cloud server 200 will further analyze in depth which key parameters of their physiological parameters (including hormone levels, BMI, weight gain rate) show significant differences compared with other reference users. This step is intended to identify specific factors that may cause abnormal blood sugar and provide a basis for subsequent health management. The setting of the 75% percentile threshold is dynamically associated with the blood sugar control benchmark library stored in the cloud server 200. When the 75th percentile blood sugar value of the target user's group exceeds the safety threshold of the corresponding gestational week in the benchmark library, the system automatically performs an early warning judgment of the blood sugar range. The benchmark library data can be regularly updated from the data interface of the cooperative medical institution through the communication unit 120 to ensure compliance with clinical practice requirements.

[0094] To achieve this goal, the cloud server 200 will compare the target user's physiological parameters with the average or median of the reference users in the same pregnancy group, and find out the parameters with large deviations. This method can quickly identify those factors that are significantly different from the group average, thereby preliminarily determining possible influencing factors.

[0095] Preferably, ifFigure 1 , Figure 4 As shown, in addition to the gestational age, the cloud server 200 can also perform comprehensive grouping based on other physiological data combined with the input of the user through the interaction unit 140 of the terminal device 100. In this embodiment, the physiological data can be the user's own BMI and weight gain rate input or updated regularly (e.g., weekly).

[0096] Preferably, the BMI and weight gain rate are manually input by the user through the interaction unit 140 of the terminal device 100. These information can also use the communication unit 120 of the terminal device 100 to obtain data from devices such as smart scales to achieve automatic synchronization. The cloud server 200 can send a prompt message to the user who first uses the terminal device 100 so that the user can provide complete height and weight information with the help of the interaction unit 140. The cloud server 200 can calculate the initial BMI accordingly and record the pre-pregnancy weight as a benchmark.

[0097] Preferably, the cloud server 200 can convert the BMI data collected by each terminal device 100 into a standard range (such as 18.5 - 24.9 kg / m 2 ), and represent the weight gain rate as a weekly average. For BMI, it can be classified according to the "Recommended Value Standard for Weight Gain of Pregnant Women" (WS / T 801 - 2022) issued by the National Health Commission. For example, the BMI range for underweight is ≤18.5 kg / m 2 ; the BMI range for normal weight is 18.5 - 24.9 kg / m 2 ; the BMI range for overweight is ≥25 kg / m 2 . For the weight gain rate threshold, the cloud server 200 can be reasonably set according to the latest pregnancy health guidelines and divided into at least three groups accordingly. For example, the first weight gain rate threshold can be ≤0.2 kg / week, the second weight gain rate threshold can be 0.2 - 0.5 kg / week, and the third weight gain rate threshold can be ≥0.5 kg / week. In addition, the cloud server 200 can also use statistical methods such as box plot method and Z-score method to identify potential outliers and avoid the influence of these extreme values on the accuracy of the overall statistical data. Preferably, the cloud server 200 can automatically optimize the grouping strategy according to the actual feedback of the user and the learning process within the system. For example, if it is found that the differences between users within a certain group are large, the system will automatically adjust the threshold range of BMI or weight gain rate to ensure the rationality of the grouping.

[0098] Preferably, the cloud server 200 can adopt a multi-dimensional grouping logic, making the reference users within each group more homogeneous, thereby improving the relevance and guiding significance of the comparison results of target users. Specifically, the cloud server 200 can combine gestational age and BMI, that is, first divide users into different groups according to gestational age, and then further subdivide them according to BMI within each gestational age group. Within the same BMI subgroup, the cloud server 200 can further subdivide the user groups into each small group according to the weight gain rate of the user. For example, for users with too fast or too slow weight gain, the system will assign them to a special attention group and provide more personalized diet advice and support, including but not limited to regularly sending reminders to pay attention to eating habits and suggesting contacting a dietitian or doctor to the terminal device 100 of the user.

[0099] Preferably, the cloud server 200 can set a reasonable update period for gestational age information, such as once a week or once every two weeks, and remind users to update their personal information in a timely manner through the interaction interface. At the same time, users are allowed to manually trigger an immediate update at any time, especially after experiencing major life events or medical examinations. In addition, the cloud server 200 also allows users to raise objections to the automatically assigned groups and provides simplified manual adjustment options. This can not only enhance the user experience but also collect valuable user feedback for further optimizing the grouping algorithm.

[0100] According to an exemplary embodiment, the target user is a pregnant woman at 24 weeks of gestation, with a BMI of 28.5 (obesity classification) and a weight gain rate of 0.6 kg / week (exceeding the upper limit of 0.5 kg / week recommended by medical guidelines). The terminal device 100 worn by the target user integrates an acquisition unit 110, and synchronizes the physiological parameters including gestational age, BMI, and blood glucose data to the cloud server 200 in real time through the communication unit 120. Based on the gestational age, BMI, and weight gain rate of the target user, and combining the aforementioned preset rules for dividing homogeneous subgroups (as Figure 4 shown), it is dynamically assigned to the "22-26 weeks of gestation + obese BMI + rapid weight gain" subgroup, where the basis for dividing the homogeneous subgroup is the group homogeneity optimization algorithm associated with multi-dimensional parameters.

[0101] The cloud server 200 extracts the daily trend blood glucose curve of the target user (as shown by the red broken line in Figure 8 ), and conducts a point-by-point comparison and analysis of it with the blood glucose data of the reference users within the homogeneous subgroup. Specifically, the cloud server 200 calculates the 25%-75% percentile interval (as shown by the light blue filled area in Figure 8 at each time point within 24 hours of the daily trend blood glucose data of the reference users and the median (as shown by Figure 8Dark blue dashed line). The analysis results show that the blood glucose peak value of the target user during the post-breakfast period (7:00 - 9:00) is 7.2 mmol / L (at 8:00), exceeding the 75th percentile threshold (6.8 mmol / L) of the same proton group; the blood glucose peak value during the post-lunch period (12:00 - 14:00) is 7.5 mmol / L (at 12:30), exceeding the 75th percentile threshold (7.0 mmol / L). The system determines that the cumulative abnormal period of the target user throughout the day is 4.5 hours, and generates a "high to medium risk of gestational diabetes" warning signal in combination with the risk characteristics (obese BMI and rapid weight gain) of the subgroup to which the user belongs.

[0102] The cloud server 200 further analyzes the deviation of the physiological parameters of the target user from those of the reference users in the same proton group: the BMI of the target user (28.5) deviates from the group mean (26.2) by +2.3 standard deviations (Z value), located at the 95th percentile of this subgroup; the weight growth rate (0.6 kg / week) exceeds the group median (0.35 kg / week) by 71%, and deviates from the medical recommended value (0.28 - 0.50 kg / week) by 20%. In addition, the estradiol concentration of the target user (450 pg / mL) is 40% higher than the group mean (320 pg / mL), while the progesterone level (18 ng / mL) is lower than the mean (22 ng / mL). Based on the association model between hormone levels and insulin resistance, the system determines that hormonal imbalance may lead to an exacerbation of the risk of insulin resistance.

[0103] Based on the above analysis results, the cloud server 200 can generate the following dynamic hierarchical health management plan for the target user:

[0104] 1. Dietary structure adjustment: Replace high-GI foods (such as white bread, GI = 75) with low-GI alternatives (such as oatmeal, GI = 55), which is expected to reduce the post-meal 1-hour blood glucose peak value by 15% - 20%; increase the proportion of non-starchy vegetables to 60% and pair with low-GI staple foods (such as quinoa, GI = 53) to slow down the glucose absorption rate.

[0105] 2. Weight control strategy: Record the daily dietary calories through the interaction unit 140, and combine with low-intensity walking to form a daily calorie deficit of about 300 kcal.

[0106] 3. Dynamic feedback mechanism: If the weight growth rate of the target user drops below 0.4 kg / week for two consecutive weeks, the system automatically relaxes the GI limit to below 65; if not up to the standard, it triggers the doctor collaborative intervention process and sends a warning notice to the designated medical terminal through the communication unit 120.

[0107] Preferably, the cloud server 200 of the present invention does not simply rely on the fixed reference threshold of blood glucose levels preset in existing medical guidelines (such as "Chinese Guidelines for the Joint Management of Mothers and Infants with Gestational Diabetes Mellitus") to set a unified control recommendation level for target users. Instead, the system deeply analyzes the distribution characteristics of the blood glucose data of target users in the overall reference dataset on a larger scale, and combines the reference user data highly similar to the physiological state of the target user to dynamically adjust the recommendation parameters. Through this dynamic analysis and adjustment mechanism, individual differences can be more accurately reflected, thereby providing personalized diet management recommendations for users. Compared with the scheme generated based on fixed reference values, this method better meets the actual needs of users and helps to improve the trust and compliance of users with system recommendations.

[0108] In addition, the cloud server 200 continuously optimizes the overall reference dataset through real-time analysis and dynamic update of massive user data, enabling the generated control recommendations to maintain high timeliness and scientificity. This data-driven personalized recommendation generation mode has higher flexibility and adaptability compared to the traditional method based on static medical guidelines. It can not only reflect the latest medical research results and clinical experience but also effectively adapt to the dynamic health status of users, providing a more scientific and practical management solution for users, thereby enhancing the overall effect of health management.

[0109] Embodiment 2

[0110] This embodiment is a supplement to the foregoing embodiment, and repeated content will not be elaborated.

[0111] Preferably, the cloud server 200 can also use a piecewise function model to describe the characteristic index FI based on different pregnancy stages:

[0112]

[0113] Among them, in the early pregnancy stage, f1(W) uses a non-linear regression model, focusing on the impact of hormone levels on insulin sensitivity; in the mid-pregnancy stage, f2(W): uses a linear regression model, suitable for a relatively stable change trend; in the late pregnancy stage, f3(W): uses a non-linear regression or logistic regression model, focusing on the impact of insulin resistance.

[0114] Preferably, in this embodiment, for the calculation of the characteristic index FI, the influence of gestational weeks is weakened, and instead, different calculation models are set only according to the early, mid, and late pregnancy stages, considering more the changes in BMI, weight gain rate, and / or hormone levels, which have a significant impact on blood glucose in gestational diabetes.

[0115] Specifically,

[0116] f1(W) = w1·(ek·G(W) -1) + w2·ln(B + 1)

[0117] The exponential term e k·G(W) represents the risk increase caused by hormonal changes. The logarithmic term ln(B + 1) adjusts the impact of BMI on the early risk, avoiding numerical instability of the model due to excessive values. G(W) is a function of the weight gain rate; k is a parameter that adjusts the hormonal change rate; B is the BMI value; w1 and w2 are weight coefficients. Preferably, Figure 6a shows an example graph of the characteristic index plotted for a certain target user varying with gestational weeks in the early pregnancy stage.

[0118] f2(W) = w3·G(W) + w4·B + w5·H(W)

[0119] The linear terms G(W) and B mainly reflect the impact of the pregnant woman's weight gain and BMI on the GDM risk. The hormonal level term H(W) is kept at a relatively low weight because hormonal fluctuations are relatively stable. H(W) is a function of the hormonal level (with relatively small changes at this time). w3, w4, and w5 are weight coefficients. Preferably, Figure 6b shows an example graph of the characteristic index plotted for a certain target user varying with gestational weeks in the mid - pregnancy stage.

[0120] f3(W) = w6·(W - 28) 2 + w7·G(W) + w8·H(W)

[0121] The quadratic term (W - 28) 2 emphasizes the non - linear risk increase in the late pregnancy stage. The weight gain rate term G(W) and the hormonal level term H(W) have a superimposed impact on the GDM risk. w6, w7, and w8 are weight coefficients. Preferably, Figure 6c shows an example graph of the characteristic index plotted for a certain target user varying with gestational weeks in the late pregnancy stage.

[0122] It should be noted that the above - mentioned specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the description of the present invention and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents. The description of the present invention contains multiple inventive concepts. Expressions such as "preferably" or "according to a preferred embodiment" indicate that the corresponding paragraphs disclose an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the features guided by "preferably" are only optional and should not be construed as being necessarily provided. Therefore, the applicant reserves the right to waive or delete relevant preferred features at any time.

Claims

1. A blood glucose management system for pregnancy, comprising a terminal device (100) carried by a target user and a reference user and a cloud server (200) data-connected to the terminal device (100), characterized in that: The cloud server (200) is configured as follows: Acquiring physiological data including pregnancy data and blood sugar data of a target user from the terminal device (100), and calculating a characteristic index of the target user based on the data; Acquire multiple blood sugar data of reference users from a plurality of terminal devices (100) and form an overall reference data set in a manner associated with gestational age, Based on the characteristic index, the target user is assigned to a predetermined characteristic index value interval in the overall reference data set, and the factors affecting the risk of gestational diabetes mellitus of the target user are determined so that the target user can manage the corresponding physiological parameters according to the factors.

2. The system according to claim 1, characterized in that The cloud server (200) further subdivides the reference users in the characteristic index interval into a plurality of pregnancy groups according to the gestational age, and further analyzes the deviation between the physiological parameters of the target user and the average values ​​of the reference users in the same pregnancy group, thereby determining one or several key parameters among the physiological parameters that affect the blood sugar level of the target user, wherein the physiological parameters include BMI, weight gain rate and hormone level.

3. The system according to claim 2, characterized in that The cloud server (200) is capable of calculating the percentile of the relative position of the target user based on the daily trend blood sugar data of the target user within the pregnancy group to which the target user belongs, and performing comparative analysis with the blood sugar data of reference users to identify the blood sugar fluctuation pattern and potential abnormalities of the target user.

4. The system according to claim 3, characterized in that The terminal device (100) comprises a collection unit (110) for acquiring continuous blood sugar data. The terminal device (100) uploads the blood sugar data recorded by the collection unit (110) to the cloud server (200) in a manner associated with gestational age through a configured communication unit (120), and receives personalized dietary advice from the cloud server (200).

5. The system according to claim 4, characterized in that The terminal device (100) is integrated with an interaction unit (140) connected to the communication unit (120) by signal, and the interaction unit (140) is configured to receive and display dietary advice from the cloud server (200) and the storage unit (130) to the user, and provide a platform for the user to input and update personal health information including the current gestational age, weight changes, and dietary records.

6. The system according to claim 5, characterized in that The cloud server (200) groups the acquired overall reference data set based on similar gestational weeks, wherein the cloud server (200) adjusts the time window range of similar gestational weeks according to the stage of pregnancy of the user: for the mid-term pregnancy stage, the time window is wider than that of the early-term pregnancy stage and the late-term pregnancy stage.

7. The system according to claim 6, characterized in that The cloud server (200) can comprehensively group the overall reference data set according to the physiological data input by the user through the interactive unit (140) of the terminal device (100), combining a multi-dimensional grouping method to improve the homogeneity of members in each group, wherein: The physiological data includes the user's own BMI and weight gain rate which are input or updated regularly by the user.

8. The system according to claim 1, characterized in that The cloud server (200) calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in the distribution within the group to which it belongs, and compares the user's blood sugar data with a preset healthy blood sugar range to determine the user's current blood sugar status, and generates graded dietary recommendations based on this, wherein the dietary recommendations optimize the target user's dietary structure by selecting food types with different GI values.

9. The system according to claim 6, characterized in that The cloud server (200) is configured to divide users into different groups according to gestational age. Within each gestational age group, multiple BMI ranges are set so that users can be subdivided into multiple BMI subgroups. Within the same BMI subgroup, the cloud server (200) can further subdivide users according to weight gain rate to identify users whose weight gain is too fast or too slow and assign them to a special concern group.

10. The system according to any one of claims 1 to 9, characterized in that: The cloud server (200) is capable of periodically re-evaluating the gestational age information of the target user and adjusting the group to which the target user belongs accordingly; The cloud server (200) allows the user to raise objections to the automatically assigned group, and receives gestational age information manually adjusted by the user through the interactive unit (140).

Images