Gestational period auxiliary system based on blood glucose biosensor

Abstract

The invention relates to a gestation period auxiliary system based on a blood glucose biosensor, belongs to the technical field of medical management, and particularly relates to extended application of the biosensor. The system comprises a plurality of user terminals used for acquiring data information related to a gestation period of a user, including blood glucose data acquired based on a blood glucose biosensor and input basic information; the server is in communication connection with the user terminal, and a shared database constructed by the server can be divided into a plurality of subgroups based on a preset group division rule, so that each user can correspond to one subgroup in the server based on basic information of the user; the processed blood glucose data obtained by the user in the current pregnancy period can be compared with the processed blood glucose data obtained by other users in the corresponding subgroup in the same pregnancy period, so that the current blood glucose management condition of the patient is analyzed in a ranking determining mode, the server can determine a preset group division rule based on the category of the basic information.

Description

Technical Field

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

[0002] Gestational diabetes mellitus (GDM), as one of the most common pregnancy complications worldwide, is characterized by exacerbated insulin resistance in the second and third trimesters of pregnancy, leading to a chronic hyperglycemic state in the mother, which in turn causes obstetric complications such as macrosomia and preeclampsia, and significantly increases the risk of long-term metabolic syndrome in offspring. Traditional management programs rely on static indicators such as fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), but they have significant limitations: FPG only reflects the instantaneous blood glucose level and cannot capture the postprandial blood glucose fluctuation pattern unique to pregnancy; HbA1c is affected by the shortened lifespan of red blood cells and has a systematic underestimation bias in the second and third trimesters of pregnancy. Although the popularization of continuous glucose monitoring (CGM) technology in recent years has increased the data density, existing systems mostly use a fixed threshold (such as a target range of 3.9 - 7.0 mmol / L) for time in range (TIR) calculation, which fails to fully consider the dynamic changes in insulin sensitivity caused by gestational age progression and lacks personalized reference standards for high-risk subgroups such as multiple pregnancies and obesity.

[0003] CN109692007A discloses a gestational blood glucose monitoring system based on blood glucose monitoring, including a real-time dynamic blood glucose monitoring device, a dynamic blood glucose monitoring workstation, and a blood glucose intelligent analysis server. The invention presets a threshold group for judging blood glucose control corresponding to the information of no diabetes before pregnancy, diabetes combined with pregnancy, or gestational diabetes information respectively in combination with big data statistics, generates a blood glucose diagnosis and analysis report according to the received pregnancy user information and real-time blood glucose value according to the preset rules, and sends this blood glucose analysis report to the dynamic blood glucose monitoring workstation to remind relevant personnel to make timely treatment, so as to intelligently and accurately analyze the blood glucose control situation in a relatively long period of time and provide good data support for subsequent blood glucose control.

[0004] In addition, since there is no unified conclusion on the occurrence factors of gestational diabetes mellitus in current research, a large number of factors (especially hormone levels) have been found to greatly affect insulin sensitivity, thus causing insulin resistance.

[0005] L. Barbour, J. Shao, L. Qiao et al. published the article "Human placental growth hormone causes severe insulin resistance in transgenic mice" in the American Journal of Obstetrics and Gynecology in 2002. This study revealed the effect of PGH on insulin resistance through a transgenic mouse model (5 mice overexpressing human placental growth hormone PGH vs 6 control mice). The experiments showed that the PGH level in transgenic mice reached the level of the late pregnancy stage, the body weight was twice that of the control group, the bone density increased significantly and the body fat percentage decreased slightly. The fasting insulin level in transgenic mice was 4 times higher than that in the control group (1.57 ± 0.22 vs 0.38 ± 0.07 ng / mL, P < 0.001), and the insulin level 30 minutes after glucose stimulation was 7 times higher (4.17 ± 0.54 vs 0.62 ± 0.10 ng / mL, P < 0.0001); the insulin sensitivity decreased significantly. After injecting insulin, the blood glucose level in transgenic mice decreased slightly, while that in the control group decreased by more than 65% (P < 0.001). The conclusion pointed out that PGH is very likely to be a key mediator of insulin resistance during pregnancy through fasting / postprandial hyperinsulinemia and insulin-reactive hypoglycemia.

[0006] Ryan, E. and Enns, L. published the article "Role of gestational hormones in the induction of insulin resistance" in The Journal of clinical endocrinology and metabolism in 1998. The research showed that: placental lactogen (hPL) is closely related to insulin resistance during pregnancy. hPL reduces glucose transport but does not affect insulin binding, which may lead to a post-binding defect in insulin action; estradiol (E2) increases insulin receptor binding during pregnancy, but its effect may be offset by the effects of progesterone and cortisol, both of which reduce insulin binding and glucose transport.

[0007] Hivert, M., White, F., Allard, C., et al. published an article in Research Square in 2023 titled "Placental RNA sequencing implicates IGFBP1 in insulin sensitivity during pregnancy and in gestational diabetes". The study showed that IGFBP1 is positively correlated with insulin sensitivity. The study found that high expression of IGFBP1 in the placenta is associated with higher insulin sensitivity in mid-pregnancy, while low levels of IGFBP1 are associated with an increased risk of gestational diabetes. The circulating levels of IGFBP1 increase during pregnancy and decrease after delivery, indicating that it may originate from the placenta and play a role in regulating insulin sensitivity during pregnancy.

[0008] In current clinical practice, blood glucose assessment and intervention decisions mainly face the following technical bottlenecks:

[0009] (1) The population reference standard lacks dynamic adaptability: Existing clinical guidelines rely on static threshold assessments and are unable to dynamically adjust the reference range based on the characteristics of the pregnant population (such as age, metabolic indicators, and genetic background), leading to biased assessments of heterogeneous populations.

[0010] (2) Insufficient collaborative analysis of multimodal data: The separate storage of blood glucose monitoring data and non-time series data such as pre-pregnancy metabolic indicators and pregnancy and delivery history hinders the modeling of the association between complex risk factors and real-time blood glucose fluctuations.

[0011] (3) Lack of a longitudinal assessment system: Traditional methods mainly rely on single / single-day blood glucose testing, which lacks the ability to continuously track the trajectory of blood glucose changes throughout the pregnancy cycle and conduct cross-period comparative analysis.

[0012] (4) Personalized warning threshold solidification: The warning system based on the group mean is difficult to identify the abnormal fluctuation patterns of individuals in specific gestational weeks and physiological conditions, resulting in false alarms / missed alarms.

[0013] The above-mentioned defects cause the existing technical solutions to face prominent problems such as low compliance and limited warning sensitivity in clinical transformation, and there is an urgent need to achieve technological breakthroughs in data integration, dynamic modeling and personalized evaluation.

[0014] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0015] In view of the deficiencies of the prior art, the present invention provides an auxiliary system for pregnancy based on a blood glucose biosensor to solve at least some of the above technical problems.

[0016] The present invention discloses an auxiliary system for pregnancy based on a blood glucose biosensor, which includes: a plurality of user terminals for obtaining data information related to the user's pregnancy, including blood glucose data obtained based on the blood glucose biosensor and basic information entered; a server communicatively connected to the user terminals. The shared database constructed by the server can be divided into several subgroups based on a preset population division rule, so that each user can be corresponding to a subgroup in the server based on their basic information. The processed blood glucose data obtained by the user during the current pregnancy can be compared with the processed blood glucose data obtained by other users in the same pregnancy in the corresponding subgroup, so as to analyze the current blood glucose management situation of the patient by determining the ranking. Among them, the server can determine the preset population division rule based on the category of the basic information.

[0017] The auxiliary system for pregnancy based on a blood glucose biosensor provided by the present invention obtains data information related to pregnancy through the user terminal and uses the server to aggregate and form a shared database, providing a scientific basis for individualized blood glucose management. Specifically, the system can not only monitor the user's blood glucose level in real time, but also perform comprehensive analysis in combination with basic health information (such as age, BMI, etc.). This integration method allows the system to generate personalized management suggestions according to the specific situation of each pregnant woman. For example, for those pregnant women with high-risk factors, the system can give early warnings and provide targeted intervention measures. In addition, by comparing the statistical data sets and reference statistical charts in the shared database, the system can evaluate the patient's blood glucose control status from a more comprehensive perspective, so as to make more accurate medical decisions.

[0018] According to a preferred embodiment, the blood glucose biosensor can periodically collect the original data of the user's blood glucose value based on continuous blood glucose monitoring technology. Among them, the blood glucose data can be uploaded to the server in the form of original data and / or processed data. The processed data is the preprocessed data obtained by the user terminal for preprocessing the original data and / or the analysis data obtained by performing a complete analysis process. The server can store all the received blood glucose data in the form of analysis data.

[0019] This method not only ensures the continuity and accuracy of data, but also makes subsequent data analysis more efficient. In particular, all blood glucose data received by the server are converted into analysis data in a unified format for storage. This standardized process simplifies the data analysis process and improves data utilization. The rich data generated by continuous monitoring helps to reveal the individual blood glucose fluctuation pattern, providing a reliable basis for formulating personalized treatment plans. For example, by analyzing the blood glucose fluctuation factors over a period of time in detail, the system can better understand the changes in the patient's insulin sensitivity, and then adjust the treatment strategy to reduce the risk of GDM occurrence.

[0020] According to a preferred embodiment, the user terminal is configured with an intelligent device for receiving the original data of the user's blood glucose value collected by the blood glucose biosensor. The intelligent device can directly forward the original data and / or process the original data into processed data and then upload it to the server. Among them, the user can enter basic information through the input module of the intelligent device, and the entered basic information is the risk factors related to gestational diabetes.

[0021] The intelligent device configured in the present invention can not only efficiently receive the original data collected by the blood glucose biosensor, but also flexibly process and upload it to the server according to actual needs. More importantly, the intelligent device allows the user to conveniently enter basic information such as risk factors related to gestational diabetes. These basic information are crucial for comprehensively understanding the patient's background. They not only help to identify high-risk populations, but also guide personalized health management strategies. For example, by combining detailed family medical history and BMI data, the system can more accurately predict the patient's risk of developing GDM and provide corresponding preventive measures.

[0022] According to a preferred embodiment, the basic information entered by the user through the input module of the intelligent device includes time information, physical information, and / or historical information. Among them, the time information includes age and the last menstrual period, the physical information includes the pre-pregnancy waist-hip ratio and / or the pre-pregnancy body mass index, and the historical information includes family diabetes history and / or adverse pregnancy and childbirth history.

[0023] The present invention enables users to conveniently input basic information including time information (such as age and last menstrual period), physical information (such as pre-pregnancy waist-to-hip ratio and BMI), and historical information (such as family history of diabetes and history of adverse pregnancy outcomes) through the input module of intelligent devices, laying a solid foundation for comprehensively assessing the risk of gestational diabetes. In particular, considering that age increase exacerbates insulin resistance, and a higher BMI is often accompanied by obesity and its associated metabolic problems, these factors together increase the incidence of GDM. At the same time, family history and previous adverse pregnancy experiences cannot be ignored either, as they reflect the impact of genetic susceptibility and previous health status on the current pregnancy. By detailed recording and analyzing various types of information mentioned above, the system can not only identify high-risk individuals at an early stage but also provide key basis for formulating effective intervention measures. For example, for pregnant women with a higher BMI, the system can recommend specific diet and exercise plans, thus effectively reducing the maternal and child health risks brought by GDM.

[0024] Preferably, the basic information of the present invention may further include hormone levels, especially hormone levels with a high degree of association with insulin sensitivity or insulin resistance. These hormones may be, for example, placental growth hormone (PGH), human placental lactogen (hPL), estradiol (E2), and / or insulin-like growth factor binding protein 1 (IGFBP1), etc.

[0025] According to a preferred embodiment, the user terminal can upload the user's basic information to the server when the user first uses it, so that the server can calculate the pregnancy period in which the subsequently uploaded data information is located based on the last menstrual period in the basic information. Among them, when constructing and / or updating the shared database, the server preferentially selects the data information with complete pregnancy period data.

[0026] This method ensures that all data can be interpreted in the correct pregnancy context, greatly improving the accuracy of data analysis. Especially when it comes to constructing and updating the shared database, preferentially selecting the information with complete pregnancy period data not only helps to capture the overall picture of blood glucose changes throughout the pregnancy but also improves the data integrity of the shared database. In the long run, this data management method helps to accumulate rich clinical experience and promote the development and improvement of the knowledge system in related fields. For example, by comparing the blood glucose fluctuation patterns in different pregnancy stages, researchers can more deeply explore the key factors affecting the occurrence and development of GDM and open up new ways for developing new prevention means.

[0027] According to a preferred embodiment, the server can group all the data in the shared database based on a preset population division rule, where the preset population division rule includes one or more combinations of several factors included in the basic information, the factor type and / or quantity of the population division rule are determined according to the data volume covered by the shared database, and the grouping method can be adjusted based on a dynamic adjustment mechanism.

[0028] The present invention introduces a grouping mechanism for a shared database based on a preset population division rule, which takes into account various factor combinations, including but not limited to age, pre-pregnancy waist-to-hip ratio, BMI, family history of diabetes, and history of adverse pregnancy outcomes, etc. This grouping method fully considers the physiological differences among different individuals and their impact on the risk of GDM, thus achieving more refined data classification. As the scale of the shared database grows, the system can dynamically adjust the grouping method according to the actual situation to ensure that the sample size in each subgroup is sufficient and representative. Such a design not only improves the effectiveness of data analysis but also promotes the development of customized medical services for specific populations. For example, by comparing the blood glucose management effects among different subgroups, researchers can more deeply explore the key factors affecting the occurrence and development of GDM, providing theoretical support for the development of new prevention measures.

[0029] When determining the preset population division rules of the present invention, it can also be carried out in the way of a combination of one hormone or multiple hormones. Preferably, the population division rules can be the similarities and differences in the influence trends of multiple hormones on insulin resistance or insulin sensitivity. Based on this population division rule, an inverse association subgroup and / or a direct association subgroup can be divided. This technical solution realizes a three-dimensional technical synergy effect at the levels of hormone action mechanism and mathematical modeling by establishing a dual division rule for the inverse association subgroup and the direct association subgroup: First, the inverse association subgroup adopts an antagonistic effect ratio model, the core of which is to transform the antagonistic effect between hormones into a non-linear weight ratio. This design breaks through the limitation that the traditional ratio only reflects the concentration ratio. By introducing the regression coefficient ratio (β1 / |β2|), the biological potency difference is quantified into a mathematical weight, enabling the model to capture the "threshold breakthrough effect" generated when the potency intensity of the pro-resistance hormone exceeds that of the protective hormone - that is, when the ratio exceeds the critical value, a small concentration change will trigger a stepwise deterioration of insulin sensitivity. This non-linear response characteristic advances the early intervention window period by 2-3 weeks compared with the traditional linear model. Second, the product index of the direct association subgroup simulates the positive feedback loop of the hormone signaling pathway mathematically by constructing a synergistic amplification factor. When the concentration product of two pro-resistance hormones reaches the critical value, their combined action will activate the oxidative stress pathway of the placental tissue. The model realizes the bioequivalence conversion of hormones with different dimensions through the normalization constant (1000) in the denominator, enabling the product index to directly map to the activation threshold of the molecular pathway of mitochondrial function damage. Finally, the parallel application of the two subgroup division rules forms a complementary verification mechanism - when an individual shows antagonistic imbalance (R>2.5) in the inverse association subgroup and does not reach the synergistic pathogenic threshold (PI<35) in the direct association subgroup, the system can identify the regional difference in hormone secretion caused by local ischemia of the placenta. This contradictory signal prompts the clinic to conduct an assessment of placental blood perfusion rather than immediately initiate drug treatment, thus avoiding excessive medical intervention. The root cause of the above technical effects lies in transforming the biological action mode (synergy / antagonism) of hormones into calculation models with different mathematical characteristics (product / ratio), and quantifying the difference in their action potencies through regression coefficients. This modeling method enables the metabolic load assessment to leap from judging a single hormone level to analyzing the dynamic balance of the hormone network, solving the technical defect of high false negative rate in traditional methods due to ignoring the type of interaction between hormones.

[0030] According to a preferred implementation manner, the data information stored in each subgroup of the shared database of the server is the analysis data of several users who meet the same population division rules at different time nodes or periods. The analysis data is a multi-dimensional blood glucose comprehensive evaluation index calculated based on the average blood glucose index, blood glucose fluctuation factor, postprandial peak gradient, and nocturnal blood glucose load related to blood glucose.

[0031] This multi-level data structure not only covers the time-series characteristics of blood glucose levels but also reflects the quality and stability of blood glucose control. Through in-depth analysis of this complex data, the system can obtain a comprehensive insight into the patient's blood glucose management pattern, thereby more accurately analyzing the user's blood glucose management situation. For example, by analyzing the nocturnal blood glucose load, doctors can identify the risk of nocturnal hypoglycemia events and adjust the treatment plan in a timely manner to avoid potential complications.

[0032] According to a preferred embodiment, the server is capable of performing statistics on all the analysis data in each subgroup stored in the shared database to obtain the statistical data sets of each subgroup, and generating a curve of the change over time of the corresponding multi-dimensional comprehensive blood glucose evaluation index for each user in each subgroup, so as to form a corresponding reference statistical graph for each subgroup. Among them, the server is capable of performing spatio-temporal clustering analysis on the statistical data set and / or the reference statistical graph of each subgroup to obtain the reference interval boundary value and / or the continuous reference curve.

[0033] The present invention not only provides a blood glucose control standard of a population with a similar background as a reference for individual patients but also provides a powerful tool for large-scale epidemiological studies.

[0034] According to a preferred embodiment, when the user terminal is first used by the user, it uploads the user's basic information to the server, so that the server can determine the corresponding subgroup based on the user's basic information and the preset group division rules. Among them, the server can generate a hash code based on the user's basic information and determine the target subgroup by comparing the Hamming distance with the pre-stored subgroup feature codes.

[0035] This automated workflow reduces the possibility of human error, ensuring that each user can be correctly classified into the most suitable subgroup for themselves. Once the target subgroup is determined, the user can enjoy the service content specially designed for them. Whether it is personalized health advice or a customized treatment plan, it becomes more feasible. For example, for pregnant women with high-risk factors, the system can provide more stringent blood glucose monitoring and management advice, thereby effectively reducing the risk of GDM, and can learn the useful blood glucose management experience of other users in the corresponding subgroup, which is more suitable than the general experience.

[0036] According to a preferred embodiment, the user terminal and / or the server can analyze the patient's current blood glucose management situation by calculating the percentile rank of the user's current multi-dimensional comprehensive blood glucose evaluation index. Among them, the user terminal and / or the server can directly or indirectly determine the warning level based on the percentile rank of one or more multi-dimensional comprehensive blood glucose evaluation indexes with the help of other auxiliary functions.

[0037] The early warning mechanism of the present invention enables the system to quickly identify those patients in a high-risk state, so as to take timely intervention measures. The percentile ranking not only reflects the individual's blood glucose control level relative to the population with the same background, but also further refines the early warning criteria by combining with the auxiliary function, improving the sensitivity and specificity of the early warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic diagram of the hardware connection of the pregnancy assistance system provided by the present invention;

[0039] Figure 2 is a schematic diagram of data transmission of the pregnancy assistance system provided by the present invention;

[0040] Figure 3 is a schematic diagram of the server grouping the shared database provided by the present invention;

[0041] Figure 4 is a schematic diagram of the server analyzing and statistically processing the analysis data in each subgroup of the shared database provided by the present invention;

[0042] Figure 5 is a change curve diagram of the multi-dimensional blood glucose comprehensive evaluation index over time (i.e., pregnancy) of an example provided by the present invention;

[0043] Figure 6 is a schematic diagram of the hardware function allocation for analyzing the blood glucose management situation of the user provided by the present invention;

[0044] Figure 7 is an enlarged analysis diagram of the change curve of the multi-dimensional blood glucose comprehensive evaluation index over time (i.e., pregnancy) of an example provided by the present invention.

[0045] LIST OF REFERENCE NUMERALS

[0046] 100: User terminal; 110: Blood glucose biosensor; 120: Intelligent device; 121: Input module; 200: Server; 400: Shared database. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following is a detailed description with reference to the accompanying drawings.

[0048] As Figure 1As shown in the figure, the present invention discloses a pregnancy assistance system based on a blood glucose biosensor 110, which includes: a plurality of user terminals 100 for acquiring data information related to the user's pregnancy period and a server 200 communicatively connected to the user terminals 100. The server 200 can (with the user's authorization) collect the data information of each user through the user terminals 100 to form a shared database 400 after summarizing it, so that any user terminal 100 can directly or indirectly obtain the blood glucose management situation of the corresponding user when acquiring the corresponding data information. In the present invention, the user using the user terminal 100 generally refers to a pregnant woman during pregnancy. Further, as Figure 2 shown, the data information related to the user's pregnancy period may include blood glucose data obtained by using continuous blood glucose monitoring technology. Among them, the blood glucose data can be uploaded to the server 200 in the form of raw data and / or processed data. The processed data can be preprocessed data obtained by simple preprocessing of the raw data and / or analysis data obtained by complex analysis and processing.

[0049] Preferably, as Figure 2 shown, the user terminal 100 of the present invention may include a communicatively connected blood glucose biosensor 110 and a smart device 120, so that the data information related to the user's pregnancy period acquired by the blood glucose biosensor 110 can be sent to the smart device 120 for forwarding and / or data processing. Preferably, the blood glucose biosensor 110 may adopt continuous blood glucose monitoring technology, for example, it can be configured as a continuous blood glucose monitor, which uses electrochemical induction technology or optical detection technology to measure glucose concentration. Among them, the electrochemical sensor preferably uses a platinum-silver / silver chloride electrode system, and the diameter range of the sensing probe is between 0.3 and 0.6 mm, and the length is controlled within the specification of 4 to 6 mm. Specifically, a disposable patch-type probe or a reusable calibration-type probe can be selected during implementation. Further preferably, the continuous blood glucose monitor of the present invention can adopt a disposable electrochemical probe with a diameter of 0.4 mm and a length of 5 mm, and perform dynamic monitoring with a cycle of 14 days, automatically record the blood glucose value every 5 minutes and encrypt and package it, so as to achieve dynamic encrypted transmission.

[0050] Preferably, the smart device 120 can be configured as a smart mobile device for receiving the encrypted and packaged blood glucose data sent by the blood glucose biosensor 110. Among them, the smart mobile device can be selected from a smart phone, a dedicated medical tablet or a wearable device. The built-in processor may include a dual-core architecture, and an independent secure element is provided in the local storage medium for storing biometric keys. Further, the built-in processor may be integrated with a signal amplification circuit and a noise filtering unit, and the analog-to-digital conversion accuracy can be selected from 12-bit to 16-bit resolution. Preferably, a 14-bit high-precision ADC chip (such as TI ADS131M04) is used in the implementation. Further preferably, asFigure 2 As shown, the user can enter basic information through the input module 121 of the smart mobile device, wherein the basic information that needs to be entered is mainly the risk factors related to gestational diabetes, such as time information, physical information and / or historical information. The entered time information may include age and last menstrual period time, wherein the risk of gestational diabetes (GDM) will also increase with the increase of the age of the pregnant woman, and the risk of GDM in pregnant women over 30 years old begins to rise significantly, especially pregnant women over 35 years old are considered to be high-risk groups. The entered physical information may include pre-pregnancy waist-to-hip ratio and / or pre-pregnancy body mass index (BMI), wherein the waist-to-hip ratio reflects the distribution of body fat, and a higher waist-to-hip ratio usually means more abdominal fat accumulation, and excessive abdominal fat will lead to increased insulin resistance, thereby increasing the risk of gestational diabetes; a higher BMI is usually associated with obesity, which can cause insulin resistance, thereby increasing the possibility of gestational diabetes, and it is found in existing studies that multifactor Logistic regression analysis shows that BMI is one of the independent risk factors for the onset of GDM. Furthermore, the physical information recorded is preferably measured before the planned pregnancy, so that the most accurate baseline data can be obtained to assess the health status of pregnant women before pregnancy and potential risk factors. However, many women may not realize that they are pregnant until a few weeks or even months after pregnancy. In this case, data from early pregnancy can be used as a substitute. The historical information recorded may include a family history of diabetes and / or adverse pregnancy and delivery history. Pregnant women with a family history of diabetes and / or adverse pregnancy and delivery history have an increased risk of gestational diabetes. Among them, the family history of diabetes can mainly consider the medical history of the individual and first-degree relatives (or immediate family members other than the spouse), and the adverse pregnancy and delivery history can mainly consider the history of macrosomia delivery.

[0051] Preferably, the smart device 120 of the user terminal 100 can upload the user's blood sugar data and basic information to the server 200, wherein the secure communication network architecture can include a multi-level transmission channel, the main communication link uses wireless transmission in accordance with the IEEE 802.11ax standard, and the backup link can be connected using a cellular network (4G / 5G) or low-power Bluetooth (BLE 5.2). The network middleware is deployed with a medical-grade gateway device, which is equipped with a protocol conversion interface and supports the conversion of multiple medical data standards such as HL7 FHIR and DICOM. The data buffer memory capacity is configured as a 4GB to 16GB DDR4 module, preferably an 8GB dual-channel configuration.

[0052] Preferably, if Figure 1 and Figure 3As shown, when the server 200 receives data information associated with corresponding users uploaded by multiple user terminals 100, it can build a shared database 400 based on this data information. Among them, when building the shared database 400, the data information with complete pregnancy data is preferably selected, that is, the data information basically includes three stages: early pregnancy (before the 13th week of pregnancy), second trimester (from the 14th to the 27th week of pregnancy), and late pregnancy (from the 28th week of pregnancy and later). After a pregnant woman gives birth, she usually no longer uses the auxiliary system of the present invention. The data information with complete pregnancy data uploaded by her as a user of the auxiliary system during pregnancy can be used to build or update the shared database 400, and she loses her user identity after giving birth, unless she becomes pregnant again to obtain a new user identity. Preferably, the user terminal 100 can upload the basic information of the user to the server 200 when the user first uses it, so that the server 200 can calculate the pregnancy period of the subsequently uploaded data information according to the last menstrual period in the basic information. Among them, "the user first uses" means that a pregnant woman who has confirmed pregnancy first enters her own basic information into the user terminal 100 during the current pregnancy cycle, and the user terminal 100 will not upload the basic information repeatedly during the current pregnancy cycle, unless the user changes the basic information. When building the shared database 400, the server 200 can also combine the existing clinical data or research data to enrich the data volume in the shared database 400. Among them, after the server 200 builds the shared database 400, the data information uploaded by the user terminal 100 can be used to update the shared database 400, and the data information for updating the shared database 400 also preferably selects the data information with complete pregnancy data. Further, when the server 200 finds that the data information uploaded by any user terminal 100 lacks data information for at least one stage, it can exclude it when building and updating the shared database 400, but still provide corresponding services for the user using the user terminal 100. Still further, if the stage missing from the data information uploaded by the user terminal 100 is a stage that the user has not reached during the current pregnancy cycle (for example, for a pregnant woman currently in the second trimester, the late pregnancy is a stage that has not been reached during the current pregnancy cycle and necessarily lacks the data information for this stage), the data information corresponding to the user will be temporarily stored until the data information corresponding to the missing stage is supplemented, and then it can be used to build or update the shared database 400, otherwise it will be excluded. The exclusion mainly includes: if it is estimated according to time that the user has reached or passed the stage missing from its data information, but the uploaded data information has not been received after a preset waiting period, the data information corresponding to the user can be excluded when building or updating the shared database 400, and at the same time, the data information corresponding to the user is deleted from the temporarily stored space.

[0053] Preferably, the server 200 can update the shared database 400 regularly or irregularly. Among them, the server 200 can be updated regularly according to a preset time interval, or can be updated irregularly according to a preset data volume or other factors. As Figure 3 shown, all the data in the shared database 400 can be divided into several subgroups based on a preset population division rule. Among them, the preset population division rule can include one or more combinations of factors such as age, pre-pregnancy waist-hip ratio, pre-pregnancy body mass index (BMI), family history of diabetes, and history of adverse pregnancy and childbirth. The factor type and / or quantity of the population division rule can be determined according to the data volume included in the shared database 400 to ensure that the data volume in each subgroup after division can meet the requirements of statistical efficacy. Further, when the shared database 400 is updated, the data information newly added to the shared database 400 can be added to the corresponding subgroup according to the preset population division rule to realize the data update of the corresponding subgroup.

[0054] Preferably, when constructing the population division rule, the grouping basis can be defined standardly by combining clinical guidelines and epidemiological research evidence. Exemplarily, for age grouping, the risk stratification standard recommended by the International Federation of Gynecology and Obstetrics (FIGO) can be adopted to divide pregnant women into four levels: 18-24 years old (stable reproductive function period), 25-34 years old (optimal childbearing age), 35-39 years old (advanced maternal age pregnancy), ≥40 years old (extremely advanced maternal age pregnancy). Among them, 35 years old as the age threshold for high-risk pregnancy has clear evidence-based medicine basis. For the pre-pregnancy waist-hip ratio (WHR) grouping, for the Asian population, 0.80 is used as the upper limit critical value of the normal range, and the division gradient is <0.75 (low waist-hip type), 0.75-0.79 (normal range), 0.80-0.84 (pre-central obesity), ≥0.85 (pathological obesity), and the rest of the races can be adjusted based on the standards of the corresponding ethnic groups. For the pre-pregnancy body mass index (BMI) grouping, the modified Asian standard can be adopted: <18.5 kg / m 2 (low weight group), 18.5-22.9 kg / m 2 (standard group), 23.0-24.9 kg / m 2 (pre-overweight), 25.0-29.9 kg / m 2 (grade I obesity), ≥30.0 kg / m 2 (grade II obesity), among which 23 kg / m 2The cut-off point is more stringent than the WHO standard and is more suitable for monitoring metabolic abnormalities during pregnancy. For the grouping of family diabetes history, a three-degree relative network model can be established, divided into no family history, positive history of first-degree relatives (parents / siblings), and positive history of second-degree relatives (grandparents / aunts and uncles). At the same time, an epigenetic weight coefficient is introduced. When there are ≥2 affected second-degree relatives, it is regarded as an equivalent first-degree relative risk. For the grouping of adverse pregnancy history, the cumulative risk scoring method can be used: no abnormal history (0 points), single abnormality (1 point: including ≥2 spontaneous abortions or a history of gestational hypertension or a history of delivering a macrosomic baby, etc.), complex abnormality (2 points: combined with fetal malformation / fetal death, etc.), recurrent pregnancy loss (≥3 points: three or more adverse outcomes). Further, a dynamic adjustment mechanism can be established during population division, that is, when the sample size of a specific subgroup is lower than the statistical power requirement (such as n < 30), clinical equivalent combination of adjacent levels is performed. For example, women aged 35 - 39 and ≥40 years old are combined into an "advanced age group", and at the same time, a data collection warning system is activated to supplement data for the weak subgroup directionally. Furthermore, all division rules are verified for the difference in group distribution through the Kolmogorov-Smirnov test to ensure that each subgroup has independent clinical intervention guiding value.

[0055] According to a preferred embodiment, the basic information uploaded by the user terminal 100 can also be an inspection report after a physical examination (such as a pregnancy examination). The inspection report may include the hormone levels of the patient. Among them, the hormone levels with a high degree of association with insulin sensitivity or insulin resistance can be uploaded to the server 200 so that the server 200 can use these hormone categories to preset the population division rules. Exemplarily, the uploaded hormone categories may include placental growth hormone (PGH), human placental lactogen (hPL), estradiol (E2), and / or insulin-like growth factor binding protein 1 (IGFBP1), etc.

[0056] Preferably, when determining the preset population division rules based on hormone categories, the server 200 can do so in the way of one hormone or a combination of multiple hormones. Among them, for relatively independent hormone categories (such as placental growth hormone, etc.), subgroups can be divided based on a single hormone level; for hormone categories that have a significant association with each other (such as human placental lactogen and estradiol, and human placental lactogen and insulin-like growth factor binding protein 1, etc.), subgroups can be divided based on a combination of multiple hormones. Further, when dividing subgroups by the way of a combination of multiple hormones (i.e., a composite hormone combination), the server 200 also needs to consider the association orientation of each hormone in the multiple hormones with insulin resistance, where the association orientation refers to whether the association between the hormone and insulin resistance is positive or negative. If the association orientations of all hormones are the same, it is a same-direction association subgroup; if the association orientation of at least one hormone is different, it is a different-direction association subgroup. Compared with the same-direction association subgroup, the different-direction association subgroup is more researchable. Among them, the subgroup formed by the combination of human placental lactogen and estradiol is a same-direction association subgroup, and the subgroup formed by the combination of human placental lactogen and insulin-like growth factor binding protein 1 is a different-direction association subgroup.

[0057] Preferably, the population division rules for a single hormone can be determined according to the dynamic threshold model of the corresponding hormone. For example, for placental growth hormone, a dynamic relationship between gestational week (GW) and hormone level can be calculated using a linear regression model, i.e.:

[0058] Critical value threshold = α×GW + β ± kσ,

[0059] In the formula, α and β are fitting coefficients from large-scale cohort studies; k is the multiple of the standard deviation, set according to the clinical risk level; σ is the standard deviation of the placental growth hormone (PGH) level.

[0060] Exemplarily, according to empirical values, when GW = 14, α = 0.183, β = 2.41, k = 1.96, the cut-off value at the early / mid-pregnancy boundary can be calculated as: Critical value threshold at GW = 14 = 0.183×14 + 2.41 + 1.96×0.78 = 4.28 ng / mL. Therefore, the population division rules for a single hormone of placental growth hormone can be, for example: early pregnancy (<14 weeks) ≤ 4.2 ng / mL (normal proliferative phase), mid-pregnancy (14 - 28 weeks) 4.3 - 7.8 ng / mL (metabolic adaptation phase), late pregnancy (≥29 weeks) ≥ 7.9 ng / mL (insulin resistance trigger phase).

[0061] Preferably, for the same-direction association subgroup in the composite hormone combination, its division rules can be determined according to the product index (PI). Taking the same-direction association subgroup of hPL + E2 as an example, its product index (PI) can be calculated by the following formula:

[0062]

[0063] Among them, w1 and w2 are the weight coefficients of hPL and E2, which can be weighted by principal component analysis and / or Delphi method. According to experience, w1 and w2 can be taken as 0.68 and 0.71 respectively.

[0064] Furthermore, the threshold division basis based on the product index (PI) is:

[0065] Low metabolic load group: PI < μ-1.5σ;

[0066] Compensatory balance group: μ-1.5σ≤PI<μ+1.5σ;

[0067] Co-pathogenic group: PI ≥ μ + 1.5σ,

[0068] Wherein, μ and σ are the mean (μ=26.5) and standard deviation (σ=6.3) of PI in the healthy control group.

[0069] Exemplarily, for the same-direction associated subgroups (hPL+E2) in the compound hormone combination, the following group division rules can be used: product index <17 (low metabolic load group); 17≤product index <36 (compensatory balance group); product index ≥36 (co-pathogenic variable group);

[0070] Preferably, for the heterotropically associated subgroups in the compound hormone combination, the division rule can be determined according to the antagonistic effect ratio (R). Taking the heterotropically associated subgroup of hPL+IGFBP1 as an example, the antagonistic effect ratio (R) can be calculated by the following formula:

[0071]

[0072] Where: β1 and β2 are the regression coefficients of hPL and IGFBP1 on HOMA-IR, respectively, which can be weighted by principal component analysis and / or Delphi method. According to experience, β1 and β2 can be taken as 0.78 and -0.53, respectively.

[0073] Furthermore, the threshold division based on the antagonistic effect ratio (R) is based on:

[0074]

[0075] Wherein, R0 is the baseline equilibrium value of the antagonistic effect ratio; IQR(R) is the interquartile range of the healthy population; MAD(R) is the median absolute deviation; k corresponds to the 95% confidence interval.

[0076] Exemplarily, for the subgroup with opposite association (hPL + IGFBP1) in the composite hormone combination, R0 can take the value of 1.123, IQR(R) can take the value of 1.2, and MAD(R) can take the value of 0.85. Therefore, C can be calculated as follows lower =-1.229 (since it is negative, the physiological lower limit 0.8 can be taken), C upper =2.79≈2.8, and the following population division rules can be adopted: when the antagonistic effect ratio ≥ 2.8, the hPL insulin resistance-promoting effect dominates; when 0.8 ≤ the antagonistic effect ratio < 2.8, the dual-hormone antagonistic equilibrium state; when the antagonistic effect ratio < 0.8: the IGFBP1 protective effect dominates.

[0077] Preferably, as Figure 3 shown, in each subgroup of the shared database 400, there are stored analysis data of several users who meet the same population division rules at different time nodes or periods. The analysis data can be obtained by the user terminal 100 and / or the server 200 after processing the original data. Among them, the original data is the blood glucose value automatically recorded by the blood glucose biosensor 110 at preset intervals within a preset sampling period. Further, by comprehensively analyzing the blood glucose values recorded over a period of time (such as 24 hours), the analysis data that can be stored in the shared database 400 can be obtained. The data processing hardware for the original data can be selected based on the computing power configuration of the user terminal 100. For example, for the user terminal 100 with insufficient computing power configuration, the original data can be directly uploaded to the server 200 for data processing; for the user terminal 100 with average computing power configuration, the original data can be simply preprocessed on the user terminal 100 to form preprocessed data, so that the server 200 can perform subsequent processing on the received preprocessed data; for the user terminal 100 with sufficient computing power configuration, the original data can be directly analyzed and processed on the user terminal 100 to obtain the analysis data, so that the server 200 can directly receive the final analysis data. The data processing hardware for the original data can also be selected and adjusted based on various factors such as user requirements and / or server 200 load.

[0078] Preferably, as Figure 3 shown, the analysis data can be a comprehensive evaluation index calculated based on one or more parameters related to blood glucose. Among them, the parameters related to blood glucose can include the average blood glucose index, blood glucose fluctuation factor, postprandial peak gradient, nocturnal blood glucose load, etc.

[0079] Preferably, the comprehensive evaluation index can be configured as a multi-dimensional blood glucose comprehensive evaluation index (CGCI), and its mathematical expression form is:

[0080] CGCI = α·AGI + β·GVF + γ·PPG + δ·NGL,

[0081] In the formula, AGI is the average blood glucose index; GVF is the blood glucose fluctuation factor; PPG is the postprandial peak gradient; NGL is the nocturnal blood glucose load; α, β, γ, and δ are the weight factors of each item respectively.

[0082] Further, the average blood glucose index (AGI) maps the mean blood glucose (G avg ) within a period of time to the interval (0, 1) through an S-shaped function (Logistic function), reflecting the degree of deviation of the overall blood glucose level from the ideal range. Among them, the specific calculation formula is:

[0083]

[0084] In the formula, G avg is the mean blood glucose (mmol / L); λ is the sensitivity adjustment factor, which can take a value of 0.3 and is used to adjust the steepness of the S-shaped curve and control the sensitivity to blood glucose changes; θ is the clinical anchor value, and the ideal fasting blood glucose value in the ADA guidelines can be referred to as 5.5.

[0085] Further, the blood glucose fluctuation factor (GVF) quantifies the amplitude of blood glucose fluctuations through the coefficient of variation (the ratio of the standard deviation of blood glucose σ to the mean blood glucose G avg ), and logarithmic transformation correction is performed on high-volatility data (>τ). Among them, the specific calculation formula is:

[0086]

[0087] In the formula, σ is the standard deviation of blood glucose (mmol / L); G avg is the mean blood glucose (mmol / L); τ is the cardiovascular risk inflection point threshold, and the value in the DECODE study can be referred to as 36%.

[0088] Further, the postprandial peak gradient (PPG) is used to measure the postprandial blood glucose increase rate and metabolic recovery ability, and can be calculated by the ratio of the "peak-baseline difference" to the "meal interval time". Among them, the specific calculation formula is:

[0089]

[0090] In the formula, G peak,t is the blood glucose peak value 2 hours after a meal (mmol / L); G pre,t is the pre-meal baseline value (mmol / L); ΔT t is the meal interval time (hours); n is the standard number of meals, and usually can take a value of 3.

[0091] Furthermore, the Nighttime Glucose Load (NGL) comprehensively evaluates the glucose exposure level (AUC term) and the hypoglycemia risk (penalty term) during the night (e.g., 00:00–06:00), and the specific calculation formula is as follows:

[0092]

[0093] In the formula, the AUC term is the area under the curve, reflecting the cumulative damage of chronic hyperglycemia to tissues; the penalty term is triggered when the minimum nighttime blood glucose Gmin is less than η (which can take a value of 3.9 mmol / L), simulating the acute harm of hypoglycemia; κ is the penalty coefficient, making a single hypoglycemic event equivalent to a 1 mmol / L increase in AUC, and can take a value of 5.

[0094] Preferably, the weight coefficients α, β, γ, δ can be set in the manner of α > β > γ > δ, because the average blood glucose (index) is a strong predictor of glycated hemoglobin (HbA1c) and is directly related to microvascular lesions, while the others are independent risk factors, and their contribution to increasing the risk gradually decreases. Further, in the case of special circumstances, such as nighttime metabolic disorders, specific weight coefficients can be weighted and warned separately.

[0095] Preferably, as Figure 4 shown, the server 200 can perform statistics on all the analysis data in each subgroup stored in the shared database 400 to obtain the statistical data sets of each subgroup, and can generate the change curves of the corresponding multi-dimensional blood glucose comprehensive evaluation indicators over time (i.e., during pregnancy) for each user in each subgroup (as Figure 5 shown), so as to form corresponding reference statistical charts for each subgroup. Further, the server 200 can perform spatio-temporal clustering analysis on the statistical data sets and / or reference statistical charts of each subgroup. For each subgroup, perform sliding window segmentation processing according to gestational weeks, with the window width set to 4 weeks and the sliding step length to 1 week, ensuring that there is a 3-week overlap between adjacent windows to smooth physiological fluctuations. Within each gestational week window, use the kernel density estimation method to calculate the probability distribution function of the CGCI value, and extract several percentiles (e.g., the 5th, 10th, 25th, 50th, 75th, 90th, 95th percentiles) as the boundary values of the reference interval. For non-integer gestational week data points, construct a gestational week-percentile mapping function based on the cubic spline interpolation algorithm, so that any gestational week t ∈ [1, 40] can pass through the function f(t) = a0 + a1t + a2t 2 + a3t 3 + Σβ i (t - k i ) + 3 to generate a continuous reference curve, where k iis a known gestational age node, and the coefficient is determined by least squares fitting. The reference interval data is stored in a distributed database in a binary tree structure. Each node contains the starting point of the gestational age, subgroup coding, and percentile value matrix, enabling fast retrieval with a time complexity of O(log n).

[0096] Preferably, for a user in the gestational period, the user terminal 100 can upload the user's basic information to the server 200 when the user first uses it, so that the server 200 can determine the corresponding subgroup based on the user's basic information and preset group division rules. Further, the server 200 can generate a 128-bit hash code based on the user's basic information, and determine the target subgroup by comparing the Hamming distance with the pre-stored subgroup feature code. Since the preset group division rules are generally judgments based on relatively stable basic information, except for input errors, the basic information is usually not changed. Therefore, any user will generally not be regrouped after determining the corresponding subgroup, unless the preset group division rules are adjusted based on a dynamic adjustment mechanism.

[0097] Preferably, as Figure 6 shown, for a user in the gestational period, the user terminal 100 can upload the user's blood glucose data to the server 200, and can also download the statistical data set and / or reference statistical chart of the corresponding subgroup from the server 200, so that the user terminal 100 and / or the server 200 can obtain the user's blood glucose management situation through analysis. Among them, the analysis hardware for the user's blood glucose management situation can be selected and adjusted based on various factors such as the computing power configuration of the user terminal 100, user requirements, and / or the load of the server 200.

[0098] Preferably, when the user terminal 100 and / or the server 200 analyze the blood glucose management situation of a user in the gestational period, they can call the gestational age-percentile mapping function f(t) to calculate the percentile values at the current gestational age (for example, {p5, p 10 ,…, p 95}), and use linear interpolation to accurately calculate the percentile rank of the user's current CGCI value (i.e., c0). For example, if c0 ≤ p5, then the percentile rank PR = 5%; if p5 < c0 ≤ p 10 , then through the linear interpolation formula PR = 5 + 5 * (CGCI - p5) / (p 10- - p5), and so on until c0 > p 95 when PR = 100%. Preferably, the user terminal 100 and / or the server 200 can be set with several percentile thresholds, so that when the percentile rank of the user's CGCI value exceeds or continuously exceeds the percentile thresholds for multiple times, a warning is issued, where different percentile thresholds can correspond to different warning levels. Further, as Figure 6As shown, the user terminal 100 and / or the server 200 may also be provided with auxiliary functions to further improve the accuracy of early warning.

[0099] Preferably, the user terminal 100 and / or the server 200 may generate a fitting curve by applying the locally weighted scatterplot smoothing method (Loess) based on the user historical data sequence. The weight function is set to w i =(1 - |t - t i | 3 ) 3 , with a window span of 8 weeks to capture the medium- and long-term trends of blood glucose control. Among them, the user historical data sequence is the user historical CGCI sequence {c 1, c2, …, c0} and its corresponding gestational weeks {t1, t2, …, t0}. Preferably, the user terminal 100 and / or the server 200 may calculate the multiple of the standard deviation Z = (c 50 - p 0- ) / σ of the current CGCI value (i.e., c0) relative to the subgroup median line (i.e., p 50 ), where σ is the standard deviation of the current gestational week window of the subgroup.

[0100] Exemplarily, the auxiliary system of the present invention may be provided with two-level dynamic early warning thresholds. Among them, the first-level early warning is triggered when the percentile rank of the user's CGCI value exceeds the 90th percentile of the target subgroup for 3 consecutive times, or the Z value continues to be > 2.0 for 2 weeks; the second-level early warning is activated when any of the following conditions is met:

[0101] a) The percentile rank of the CGCI value breaks through the 95th percentile of the target subgroup and is accompanied by the NGL penalty term

[0102] b) The slope of the Loess curve and the current PR ≥ 85%;

[0103] c) The logarithmically transformed value log(GVF) of GVF in a single measurement exceeds the subgroup mean by 3σ.

[0104] In an example, the change curve of the multi-dimensional blood glucose comprehensive evaluation index of a certain user over time (i.e., during pregnancy) is as Figure 5 shown. When analyzing the user's blood glucose management situation using the user terminal 100 and / or the server 200, it is found that there is an abnormal situation at the 34th week (i.e., Figure 5 point A shown in Figure 5 ). Further magnifying and analyzing point A in Figure 7 can obtain Figure 7The curve showing the change of the multi-dimensional blood glucose comprehensive evaluation index of the user in the 34th week over time is presented. By comparing with the CGCI value at the 90th percentile of the (first) target subgroup, it can be intuitively seen that the percentile ranking of the user's CGCI value for 3 consecutive times (i.e., from the 3rd day to the 5th day of the 34th week) has exceeded the 90th percentile of the (first) target subgroup, that is, the triggering condition for the first-level warning has been reached. After the warning is triggered, the server 200 can screen out the population with a similar background to the user from the current target subgroup and send the intervention plan selected by this population as a suggestion to the user terminal 100, so that after the user executes effective intervention measures based on the suggestion, the blood glucose can return to the normal level (such as Figure 5 the curve change after point A shown).

[0105] Figure 7 The curves corresponding to the 90th percentiles of two different target subgroups formed based on two different population division rules are presented. Among them, the first target subgroup can be, for example, the reverse association subgroup (hPL + IGFBP1) in the composite hormone combination, and the second target subgroup can be, for example, the direct association subgroup (hPL + E2) in the composite hormone combination. Since the population division rules adopted by the two target subgroups are different, even for target subgroups whose population division rules contain some same factors (such as hPL), there may be significant differences in their 90th percentile curves, so that when the user is included in different target subgroups, the comparison criteria for their analysis data are different, and thus different analysis results can be obtained. For example, Figure 7 The curve showing the change of the multi-dimensional blood glucose comprehensive evaluation index of the user in the 34th week over time presented, when compared with the CGCI value at the 90th percentile of the first target subgroup, can trigger a first-level warning, while when compared with the CGCI value at the 90th percentile of the second target subgroup, no warning will be triggered. Therefore, when analyzing the blood glucose management situation of the user, the user terminal 100 and / or the server 200 can retrieve the data of one or more target subgroups matching the user, so as to achieve multi-dimensional analysis and comparison to avoid missing the alarm opportunity.

[0106] Preferably, when the intelligent device 120 of the user terminal 100 is equipped with a display, the warning information can be displayed visually on the display of the intelligent device 120, such as the mobile phone screen, etc.

[0107] It should be noted that the above 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 and drawings of the present invention 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. Phrases 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 ways and should not be understood as being required to be provided. Therefore, the applicant reserves the right to waive or delete relevant preferred features at any time.

Claims

1. An auxiliary system for pregnancy based on a blood glucose biosensor, characterized in that, It includes: A number of user terminals (100) for obtaining data information related to the user's pregnancy period, including blood glucose data obtained based on a blood glucose biosensor (110) and entered basic information; A server (200) communicatively connected to the user terminal (100), wherein, The shared database (400) related to blood glucose management during pregnancy constructed by the server (200) can be divided into several subgroups based on preset group division rules, so that each user can be corresponding to a subgroup in the server (200) based on their basic information. The processed blood glucose data obtained by the user in the current pregnancy can be compared with the processed blood glucose data obtained by other users in the same pregnancy in the corresponding subgroup, so as to analyze the current blood glucose management situation of the patient by determining the ranking. Among them, the server (200) can determine the preset group division rules based on the category of the basic information.

2. The system according to claim 1, characterized in that, The blood glucose biosensor (110) can periodically collect the original data of the user's blood glucose value based on continuous blood glucose monitoring technology. Among them, the blood glucose data can be uploaded to the server (200) in the form of original data and / or processed data. The processed data is the preprocessing data obtained by the user terminal (100) preprocessing the original data and / or the analysis data obtained by performing a complete analysis process. The server (200) can store all the received blood glucose data in the form of analysis data.

3. The system according to claim 1 or 2, characterized in that, The user terminal (100) is configured with an intelligent device (120) for receiving the original data of the user's blood glucose value collected by the blood glucose biosensor (110). The intelligent device (120) can directly forward the original data and / or process the original data into processed data and then upload it to the server (200). Among them, the user can enter the basic information through the input module (121) of the intelligent device (120), and the entered basic information is the risk factors related to gestational diabetes.

4. The system according to any one of claims 1 to 3, characterized in that The basic information entered by the user through the input module (121) of the intelligent device (120) includes time information, physical information and / or historical information. Among them, the time information includes age and the last menstrual period time, the physical information includes the pre-pregnancy waist-to-hip ratio and / or the pre-pregnancy body mass index, and the historical information includes a family history of diabetes and / or a history of adverse pregnancy and childbirth.

5. The system according to any one of claims 1 to 4, characterized in that The user terminal (100) can upload the user's basic information to the server (200) when the user uses it for the first time, so that the server (200) can calculate the pregnancy period of the subsequent uploaded data information based on the last menstrual period time in the basic information. Among them, when constructing and / or updating the shared database (400), the server (200) preferentially selects the data information with complete pregnancy period data.

6. The system according to any one of claims 1 to 5, characterized in that, The server (200) can group all the data in the shared database (400) based on a preset group division rule. Among them, the preset group division rule includes one or more combinations of several factors included in the basic information. The factor type and / or quantity of the group division rule are determined according to the data volume covered by the shared database (400), and the grouping method can be adjusted based on a dynamic adjustment mechanism.

7. The system according to any one of claims 1 to 6, characterized in that, The data information stored in each subgroup of the shared database (400) of the server (200) is the analysis data of several users who meet the same group division rule at different time nodes or periods. The analysis data is a multi-dimensional blood glucose comprehensive evaluation index calculated based on the average blood glucose index, blood glucose fluctuation factor, postprandial peak gradient, and nocturnal blood glucose load related to blood glucose.

8. The system according to any one of claims 1 to 7, characterized in that The server (200) can perform statistics on all the analysis data stored in each subgroup of the shared database (400) to obtain the statistical data set of each subgroup, and generate a curve of the change of the corresponding multi-dimensional blood glucose comprehensive evaluation index over time for each user in each subgroup, so as to form a corresponding reference statistical chart for each subgroup. Among them, the server (200) can perform spatio-temporal clustering analysis on the statistical data set and / or reference statistical chart of each subgroup to obtain the reference interval boundary value and / or continuous reference curve.

9. The system according to any one of claims 1 to 8, characterized in that, When the user first uses the user terminal (100), the user's basic information is uploaded to the server (200), so that the server (200) can determine the corresponding subgroup based on the user's basic information and the preset group division rule. Among them, the server (200) can generate a hash code based on the user's basic information, and determine the target subgroup by comparing the Hamming distance with the pre-stored subgroup feature code.

10. The system according to any one of claims 1 to 9, characterized in that, The user terminal (100) and / or the server (200) can analyze the current blood glucose management situation of the patient by calculating the percentile rank of the user's current multi-dimensional blood glucose comprehensive evaluation index. Among them, the user terminal (100) and / or the server (200) can directly or indirectly determine the warning level based on the percentile rank of one or more multi-dimensional blood glucose comprehensive evaluation indexes with the help of other auxiliary functions.

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