Wearable diabetes intelligent health monitoring management device and system

Abstract

The invention relates to a wearable diabetes intelligent health monitoring management device and system, and aims to provide continuous and real-time blood glucose monitoring and personalized health management schemes for diabetics. The device comprises a blood glucose sensor, a wireless communication module, a processing unit, a battery module and a wearing support, the blood glucose concentration is measured in real time through a non-intrusive technology, and data are transmitted to intelligent equipment or a cloud platform through the wireless communication module. And the processing unit is used for denoising and filtering the received blood glucose data, and generating personalized health feedback and suggestions by using a data analysis algorithm in combination with information such as historical health data, diet, exercise, medicine use and the like of the patient. The system also has an abnormal blood glucose monitoring function, and when the blood glucose level is abnormal, an alarm is automatically pushed and corresponding health suggestions are provided, such as diet adjustment, exercise recommendation and drug use adjustment. The system supports remote data sharing, and patients and doctors of the patients can carry out remote monitoring and health management through intelligent equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of health care systems, and more particularly, to a wearable intelligent health monitoring and management device and system for diabetes. Background Art

[0002] Diabetes is one of the most influential chronic diseases globally, especially type 2 diabetes, whose incidence has been increasing year by year due to changes in lifestyle and eating habits. The typical symptom of diabetes is abnormal elevation of blood glucose. If not effectively controlled for a long time, it may lead to various complications including retinopathy, nephropathy, nerve damage, etc., seriously threatening the health and quality of life of patients. Therefore, how to effectively monitor and manage the blood glucose level of diabetic patients has become an important topic in global health management.

[0003] Currently, diabetes management mainly relies on the following methods: Traditional blood glucose meter monitoring: Patients use a blood glucose meter to manually measure blood glucose concentration, usually before or after meals, and the data is presented once. Traditional blood glucose meters rely on patients' active testing, but since patients often forget or neglect to detect, it is difficult to comprehensively and continuously monitor blood glucose changes. Moreover, traditional blood glucose meters only provide instantaneous data, unable to show the long-term change trend of blood glucose, nor can they reflect the dynamic situation of blood glucose fluctuations in real time.

[0004] Continuous Glucose Monitoring (CGM) system: The Continuous Glucose Monitoring (CGM) system is a relatively advanced blood glucose monitoring technology at present, which can provide real-time blood glucose change trends. However, current CGM devices still have certain limitations. First, the price of CGM systems is relatively high, making it difficult for ordinary patients to afford, especially in some developing countries, where the popularization rate is low. Second, CGM devices usually need to be installed on or under the skin, requiring patients to undergo invasive operations, which to a certain extent affects the wearing comfort and willingness of patients to use. In addition, there are also some deficiencies in data accuracy, device maintenance, and sensor replacement of CGM devices.

[0005] Smartphones and mobile applications: With the popularization of mobile Internet and smartphones, many diabetic patients have started to use health management applications and smart devices, such as smart bracelets, smart watches, etc., to combine blood glucose monitoring with health management. However, most of these smart devices rely on patients to input data independently, and usually can only provide regular health management suggestions, lacking real-time dynamic monitoring and personalized health interventions. Existing intelligent health management systems often cannot be closely integrated with patients' real-time physiological status and behavioral data (such as blood glucose fluctuations, diet, exercise volume, drug use, etc.), resulting in poor accuracy and timeliness of system suggestions.

[0006] Fragmentation of the health management system: Existing intelligent health management devices and applications often work independently, lacking data integration and cross-platform data sharing capabilities. Patients often need to manually input and monitor multiple different health data (such as diet records, exercise volume, blood glucose levels, medication usage, etc.), and there is no effective correlation and feedback mechanism among these data. This makes personalized treatment and health management of diabetes more difficult.

[0007] Although existing blood glucose monitoring devices and health management systems have made some progress in certain aspects, there are still many deficiencies, mainly manifested in the following aspects: Insufficient real-time and continuity of data: Traditional blood glucose meters can only provide single blood glucose measurements and cannot monitor blood glucose fluctuations in real time; although the CGM system provides continuous monitoring, it still has a certain degree of invasiveness, is not suitable for all patients, and the device cost is relatively high, making it difficult to popularize.

[0008] Lack of personalization and intelligence in health management: Most existing intelligent health management devices and applications rely on patients' independent input, lacking real-time data feedback and intelligent analysis. Even though some devices have data recording functions, most lack intelligent analysis of multiple factors such as blood glucose fluctuations, diet, exercise, and medications, and cannot provide personalized health intervention suggestions according to the specific conditions of patients.

[0009] Difficulty in cross-platform data integration: Most existing intelligent devices and applications cannot achieve cross-device and cross-platform data sharing and integration. Patients often need to switch between multiple applications, manually input various health data, and cannot form a complete health record. The lack of data integration and sharing makes it difficult for patients and doctors to comprehensively evaluate the health status of patients during the diagnosis and treatment process.

[0010] Poor device wearing comfort and patient compliance: Although there are some advanced intelligent health monitoring devices, many devices are uncomfortable to wear. Especially the CGM device requires patients to perform skin punctures, resulting in difficulties in long-term wearing. In addition, the use and maintenance of some devices are complex, and due to the relatively large size or unattractive appearance of the device itself, patients may be reluctant to wear it for a long time, affecting their compliance.

[0011] Lag in monitoring and intervention: Most existing devices can only provide blood glucose monitoring, lacking real-time feedback and personalized suggestions. Patients usually need to rely on doctors to adjust treatment plans based on test results, but due to the lag in remote communication and real-time data analysis, the best intervention opportunities are often missed.

[0012] In summary, there are still obvious deficiencies in the existing technologies in aspects such as blood glucose monitoring, health data management, personalized treatment plans, and patient compliance. There is an urgent need for a more intelligent, convenient, and cost-effective technical solution to improve the health management effect of diabetes and the quality of life of patients.

[0013] For this reason, we propose a wearable intelligent health monitoring and management device and system for diabetes to solve these problems. Summary of the Invention

[0014] The purpose of the present invention is to solve the existing technical problems proposed in the above background technology and provide a wearable intelligent health monitoring and management device for diabetes.

[0015] The above object of the present invention is achieved as follows: A wearable intelligent health monitoring and management device for diabetes, comprising: A blood glucose sensor for real-time measurement of the blood glucose concentration of diabetes patients. The blood glucose sensor adopts a non-invasive sensing technology. The sensor obtains blood glucose data through the skin surface. The sensor is an electrochemical sensor or an optical sensor, capable of accurately and continuously monitoring the blood glucose level; A wireless communication module for transmitting the blood glucose data measured by the blood glucose sensor to an external intelligent device or a cloud platform through a wireless signal. The wireless communication module uses wireless communication technologies such as Bluetooth Low Energy (BLE), Wi-Fi, or NFC for data transmission, thereby enabling the real-time and remote synchronization of data; A processing unit for receiving and processing the data transmitted by the blood glucose sensor. The processing unit includes: A data preprocessing module for removing noise and filtering the blood glucose data to ensure the accuracy of the data; A data analysis module for analyzing the trend of the blood glucose level changing over time and generating personalized health management suggestions based on the patient's health profile (including but not limited to historical blood glucose data, diet, exercise, drug use, etc.); A health feedback module that automatically generates health feedback based on the data analysis results, such as diet, exercise, and drug adjustment suggestions, or issues an alarm prompt when the blood glucose level is abnormal; A battery module for providing power to each component of the device. The battery module is a rechargeable lithium battery or other batteries suitable for low-power devices, capable of supporting the continuous operation of the device and having a long battery life; A wearing bracket for fixing the blood glucose sensor, wireless communication module, processing unit, and battery module on the patient's body. The wearing bracket is an adjustable wristband, waistband, or chest strap, thereby making the device comfortable and firm to wear and suitable for daily use.

[0016] As a preferred technical solution of the present invention, the blood glucose sensor is an electrochemical sensor or an optical sensor, which can non-invasively and real-time measure the blood glucose concentration through the skin surface and has high measurement accuracy and stability.

[0017] As a preferred technical solution of the present invention, the data analysis module in the processing unit analyzes the blood glucose changes of the patient by using the following formula:

[0018] where is the blood glucose level at time t, is the initial blood glucose value, is the influence of diet on the blood glucose level, is the influence of exercise on the blood glucose level, is the influence of drugs on the blood glucose level.

[0019] As a preferred technical solution of the present invention, the data analysis module dynamically adjusts the influence weights of factors such as diet, exercise, and drugs for each patient according to the personalized health record. The calculation formula is:

[0020] where is the diet type and amount at the i-th meal, is the exercise intensity, is the drug usage amount, is the weight coefficient of the influence of each factor on blood glucose.

[0021] As a preferred technical solution of the present invention, when the health feedback module detects an abnormal blood glucose level, it pushes an alarm to the user's smart device through the wireless communication module and provides instant health advice according to the patient's blood glucose condition.

[0022] As a preferred technical solution of the present invention, the alarm reminds the user by means of sound, vibration, or notification push, etc., and displays the current blood glucose trend, historical data analysis, and health adjustment suggestions through a mobile application.

[0023] As a preferred technical solution of the present invention, the wireless communication module supports pairing and data synchronization with devices such as smartphones, smart watches, and tablets, realizes real-time remote monitoring and management, and supports remote interaction and data sharing between users and medical staff.

[0024] As a preferred technical solution of the present invention, the battery module is of an integrated design and has an intelligent charging function, which can automatically monitor the battery power and remind the user to charge when the power is low.

[0025] The present invention also provides an intelligent health management system, including: a wearable diabetes intelligent health monitoring management device; A mobile application that receives data from the blood glucose sensor and displays real-time blood glucose information, and users can provide feedback through the application; The cloud platform is used to store and analyze patients' historical health data, generate health records, and provide users with personalized health advice. The cloud platform introduces machine learning and deepseek big models. According to the patient's basic vital signs, height, weight, waist circumference, physique, blood sugar, diet, exercise, mood, heart rate, the system is trained with a certain algorithm and gives personalized guidance according to the patient's real-time situation.

[0026] As a preferred technical solution of the present invention, the data analysis module uses the following derivation formula to predict the changes in the patient's future blood sugar level:

[0027] in, For the future moment Predicted blood sugar levels, is the rate of change of current blood glucose level, and t is the predicted time interval.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The wearable diabetes intelligent health monitoring management device and system of the present invention overcomes the limitations of traditional blood glucose meters and existing continuous blood glucose monitoring systems by monitoring blood glucose levels in real time and integrating multiple health data analysis functions. The device can continuously and in real time monitor the patient's blood glucose changes without the need for manual testing by the patient, thereby significantly improving the frequency and accuracy of blood glucose monitoring. By analyzing blood glucose data through intelligent algorithms and combining factors such as the patient's diet, exercise, and medication use, the system can provide personalized health advice to help patients adjust treatment plans in a timely manner, avoid the risk of high or low blood glucose, and effectively reduce the occurrence of complications.

[0029] In addition, the system of the present invention has good user compliance and wearing comfort, solving the problems of inconvenience for patients to wear and difficulty in equipment maintenance in existing equipment. Through the wireless communication module connected to smart devices (such as smart phones, smart watches), patients can view real-time blood sugar data and health feedback at any time, and interact through mobile applications, ensuring continuous data updates and timely feedback. This comprehensive health management program not only improves the daily management efficiency of diabetic patients, but also provides patients with more intelligent and convenient treatment support, ultimately improving the quality of life of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 is the overall logic diagram of a wearable diabetes intelligent health monitoring and management device in an embodiment of the present invention; Figure 2 is the system block diagram of an intelligent health management system in an embodiment of the present invention. Specific Embodiments

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0033] The following will Figure 1 and Figure 2 be used to describe the specific embodiments of the present invention in detail.

[0034] The wearable diabetes intelligent health monitoring and management device and system of the present invention integrate functions such as blood glucose monitoring, health data analysis, and personalized feedback, realizing real-time monitoring and intelligent health management of diabetic patients. Next, the embodiments will be described in detail in combination with the technical content in the claims, and specific embodiments will be listed, including the technical implementation process, calculation process, and typical application scenarios.

[0035] I. Overall System Architecture The wearable diabetes intelligent health monitoring and management device of the present invention includes the following main modules: Blood glucose sensor: Non-invasively measures blood glucose concentration through the skin surface, and uses an electrochemical sensor or an optical sensor to monitor blood glucose levels in real time.

[0036] Wireless communication module: Adopts Bluetooth Low Energy (BLE) or Wi-Fi technology to transmit data to intelligent devices or cloud platforms, realizing data synchronization and remote monitoring.

[0037] Processing unit: Responsible for data preprocessing, analysis, and generating health feedback. It includes a data preprocessing module, a data analysis module, a health feedback module, etc.

[0038] Battery module: Provides power for the device and uses efficient and long-lasting batteries to support long-term use.

[0039] Wearing bracket: Suitable for daily wear by patients, enabling components such as blood glucose sensors to operate stably. II. Specific implementation methods

[0040] 1. Blood glucose monitoring and data collection: The blood glucose sensor detects the blood glucose concentration in real time through a micro-sensor on the skin surface. The working principle of the sensor can adopt electrochemical sensing or optical sensing technology. The electrochemical sensor detects the blood glucose concentration through sweat or capillaries on the skin surface, while the optical sensor measures the absorption of blood glucose in the skin layer through near-infrared technology.

[0041] Whenever the sensor measures blood glucose data, the data will be transmitted to a smart device or cloud platform through a wireless communication module (such as a BLE module) for subsequent analysis by the processing unit.

[0042] 2. Data preprocessing and denoising: After the data preprocessing module in the processing unit receives the blood glucose data, it first denoises and filters the data to eliminate the noise that may be generated during the measurement process. For example, a low-pass filter is used to smooth the original blood glucose data, removing short-term fluctuations and retaining long-term trend changes.

[0043] Denoising formula:

[0044] Among them, is the smoothed blood glucose data, is the original blood glucose measurement value, N is the filter window size, and a time window of 5 - 10 minutes is usually selected.

[0045] 3. Data analysis module: The data analysis module analyzes based on factors such as historical blood glucose data, the patient's diet, exercise, and medications, using the following formula:

[0046] Among them, is the blood glucose level at time t, is the initial blood glucose value, is the impact of diet on the blood glucose level, is the impact of exercise on the blood glucose level, is the impact of medications on the blood glucose level. This formula calculates the change trend of the blood glucose level by performing a weighted sum of the feedback on the patient's living habits, diet, exercise amount, and medication usage.

[0047] 4. Personalized Health Feedback: Through data analysis, the system automatically generates personalized health suggestions based on the changing trend of blood glucose levels and the patient's health profile. For example, if the analysis results show that the patient's blood glucose level has been continuously high for a certain period, the system may recommend adjusting the diet or increasing physical activity, or even reminding the patient to adjust the medication according to the doctor's prescription.

[0048] Health advice push formula:

[0049] Where is the diet type and amount at the i-th meal, is the exercise intensity, is the dosage of the medication, is the weight coefficient of the impact of each factor on blood glucose; 5. Abnormality Monitoring and Alarm: : Impact coefficients of diet, exercise, and medication. Through this formula, the system calculates health feedback based on the patient's personalized data, making the suggestions accurate and effective.

[0050] When blood glucose data is abnormal, the health feedback module automatically sends an alarm. The alarm is pushed to the patient through a mobile application and can also be used to remind the patient's relatives or doctor via text message, email, etc. The system will prompt the patient whether immediate medication adjustment or other actions are needed. III. Embodiments

[0051] Embodiment 1: Standard Diet and Exercise Management The patient in this embodiment is a type 2 diabetes patient who usually monitors blood glucose through a wearable diabetes intelligent health monitoring and management device. The patient's blood glucose level usually rises after meals, especially after consuming high-sugar foods. The system analyzes the real-time monitoring of the patient's blood glucose changes in combination with factors such as diet and exercise, and automatically provides personalized diet and exercise adjustment suggestions for the patient.

[0052] Blood glucose data collection: After the patient wears the device, the blood glucose sensor monitors the patient's blood glucose concentration in real time through electrochemical sensing technology. When the patient eats, the device automatically records the change in blood glucose level.

[0053] Suppose the patient eats a high-sugar meal containing 50 grams of sugar, and the post-meal blood glucose recorded by the blood glucose sensor is 200 mg / dL, approximately equal to 11.1 mmol / L.

[0054] Data preprocessing: The blood glucose data is transmitted to the intelligent device through a wireless communication module.

[0055] The data preprocessing module removes noise and filters the original data, thereby enhancing the stability and accuracy of the data. For example, the moving average method is used to smooth the data and avoid the influence of short-term fluctuations on the analysis results.

[0056] Suppose the blood glucose data of a patient after a meal is as follows: , and the data after filtering is .

[0057] Data analysis: The data analysis module uses the formula: to calculate the blood glucose change, where: (The impact of diet on blood glucose. The impact of 50 grams of sugar on blood glucose is 40 mg / dL, approximately equal to 2.22 mmol / L); (Suppose the patient walks fast for 30 minutes. The impact of exercise on blood glucose is -15 mg / dL); (The patient is not currently using medications). Based on the above data, the system calculates that the trend of the patient's blood glucose level 1 hour after the meal is:

[0058] Health feedback generation: The system generates health feedback based on the blood glucose change trend. Since it is predicted that the blood glucose 1 hour after the meal reaches 255 mg / dL, approximately equal to 14.16 mmol / L, which is significantly beyond the normal range (the normal range is generally 80 - 140 mg / dL).

[0059] The system advises the patient: Do more exercise after the meal (such as walking for 40 minutes) to help lower the blood glucose level; Adjust the diet structure, reduce foods with high sugar content, and recommend foods with higher fiber.

[0060] Health advice push: The system pushes notifications through the smartphone to remind the patient to take actions. After receiving the notification, the patient views the advice and decides to increase the exercise amount.

[0061] Data update: Suppose the patient walks for 40 minutes as advised. After exercise, the blood glucose level improves. Finally, the blood glucose level 2 hours after the meal drops to 170 mg / dL, approximately equal to 9.44 mmol / L. The health advice push is updated to maintain the current diet and exercise habits.

[0062] Results and effects: According to the system's advice, the patient successfully adjusted the diet and exercise plan, avoiding excessive post-meal blood glucose elevation. Through continuous monitoring and feedback, the patient can manage their diabetes more precisely, improving the quality of life and reducing the risk of long-term complications.

[0063] Example 2: Medication Use Adjustment and Hypoglycemia Monitoring The patient in this embodiment is a type 1 diabetic patient who uses insulin to control blood sugar. Recently, due to less physical activity, the patient took more insulin, resulting in an excessive drop in blood sugar. This example shows how the intelligent health monitoring system monitors blood sugar changes, gives early warnings in a timely manner, and helps the patient adjust the dosage of the medicine. Blood sugar data collection: After the patient wears the device, the blood sugar sensor monitors the blood sugar concentration in real time through optical sensing technology. Suppose that 1 hour after the patient uses insulin, the blood sugar drops rapidly to 50 mg / dL, which is approximately equal to 2.77 mmol / L. The data recorded by the blood sugar sensor is transmitted to the smart phone through the wireless communication module.

[0064] Data preprocessing: Since the patient's blood sugar drops rapidly, the data preprocessing module denoises and filters the blood sugar data. Suppose the original data is: (The blood sugar drops continuously), and the data after filtering is: . The data analysis module calculates the blood sugar change trend and detects hypoglycemic events. Based on factors such as historical blood sugar data, diet, and exercise, the system calculates that: Among them, the drug effect is: (The effect of insulin on blood sugar) The system calculates through the formula that the current hypoglycemic trend continues, and predicts that the patient's blood sugar will drop to 40 mg / dL within 30 minutes, which is approximately equal to 2.22 mmol / L, exceeding the hypoglycemic warning value.

[0065] Data analysis and hypoglycemic warning: The patient receives a hypoglycemic alarm through the intelligent device and immediately ingests a sugary drink according to the system's suggestion. The intelligent device also displays the current blood sugar level and the amount of sugar recommended for ingestion.

[0066] Health feedback generation: The system recognizes the hypoglycemic trend and automatically pushes a warning message; Health advice push: Remind the patient to immediately ingest sugary foods (such as sugary drinks, fruit juices, etc.); Remind the patient to reduce the insulin dosage or communicate with the doctor to adjust the medicine.

[0067] Data update and drug adjustment: After the system receives the feedback data of the patient, it updates the health advice, calculates the new dosage of the medicine, and reminds the patient to conduct regular blood sugar monitoring.

[0068] Suppose the patient reduces the insulin dosage according to the suggestion and monitors that the blood sugar gradually returns to the normal level. The system continues to provide long-term monitoring and adjustment suggestions.

[0069] Results and effects: Through the real-time data analysis and feedback of the intelligent health monitoring system, patients can timely identify the hypoglycemia risk and take measures to avoid the serious consequences of hypoglycemia. The personalized medication adjustment suggestions of the system help patients reduce the risk of overuse of medications and promote long-term health management.

[0070] Summary: These two embodiments demonstrate the specific applications of the wearable intelligent health monitoring and management device for diabetes in daily life. Embodiment 1 focuses on the adjustment of diet and exercise, while Embodiment 2 shows the medication adjustment and hypoglycemia monitoring. In each embodiment, the system helps diabetic patients effectively manage blood sugar, avoid health risks, and optimize treatment plans through real-time monitoring, data analysis, and personalized feedback.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A wearable diabetes intelligent health monitoring and management device, characterized in that: include: A blood glucose sensor is used to measure the blood glucose concentration of a diabetic patient in real time. The blood glucose sensor adopts non-invasive sensing technology. The sensor obtains blood glucose data through the skin surface. The sensor is an electrochemical sensor or an optical sensor. A wireless communication module, used to transmit the blood sugar data measured by the blood sugar sensor to an external smart device or a cloud platform via wireless signals, wherein the wireless communication module uses Bluetooth low energy, Wi-Fi or NFC wireless communication technology for data transmission; A processing unit, used to receive and process data transmitted by the blood glucose sensor, the processing unit comprising: A data preprocessing module, used to remove noise and filter blood glucose data; Data analysis module, which is used to analyze the trend of blood sugar levels over time and generate personalized health management suggestions based on the patient's health records; Health feedback module, which automatically generates health feedback based on data analysis results, such as diet, exercise and medication adjustment suggestions, or issues alarm prompts when blood sugar levels are abnormal; A battery module, used to provide power to various components of the device, wherein the battery module is a rechargeable lithium battery or other battery suitable for low-power devices; The wearing bracket is used to fix the blood glucose sensor, wireless communication module, processing unit and battery module on the patient. The wearing bracket is an adjustable wristband, waist belt or chest belt.

2. The wearable diabetes intelligent health monitoring and management device according to claim 1 is characterized in that: The blood glucose sensor is an electrochemical sensor or an optical sensor, which measures blood glucose concentration in real time and non-invasively through the skin surface.

3. The wearable diabetes intelligent health monitoring and management device according to claim 1, characterized in that: The data analysis module in the processing unit analyzes the patient's blood sugar changes using the following formula: in, is the blood glucose level at time t, is the initial blood glucose value, The effect of diet on blood sugar levels, The effect of exercise on blood sugar levels. The effect of the drug on blood sugar levels.

4. The wearable diabetes intelligent health monitoring and management device according to claim 3 is characterized in that: The data analysis module dynamically adjusts the weight of each patient's diet, exercise, and medication factors based on the personalized health profile. The calculation formula is: in, is the type and amount of food consumed during the i-th meal, For exercise intensity, The amount of drug used, is the weight coefficient of each factor affecting blood sugar.

5. The wearable diabetes intelligent health monitoring and management device according to claim 1, characterized in that: When the health feedback module detects abnormal blood sugar levels, it pushes an alarm to the user's smart device through the wireless communication module and provides immediate health advice based on the patient's blood sugar status.

6. The wearable diabetes intelligent health monitoring and management device according to claim 5, characterized in that: The alarm reminds the user through sound, vibration or notification push, and displays the current blood sugar trend, historical data analysis and health adjustment suggestions through the mobile application.

7. The wearable diabetes intelligent health monitoring and management device according to claim 1, characterized in that: The wireless communication module supports pairing and data synchronization with smart phones, smart watches, and tablet devices, realizes real-time remote monitoring and management, and supports remote interaction and data sharing between users and medical staff.

8. The wearable diabetes intelligent health monitoring and management device according to claim 1, characterized in that: The battery module is an integrated design and has an intelligent charging function, which can automatically monitor the battery power and remind the user to charge when the battery is low.

9. An intelligent health management system comprising the wearable diabetes intelligent health monitoring management device according to any one of claims 1 to 8, characterized in that ； A mobile application that receives data from the blood glucose sensor and displays real-time blood glucose information, and users can provide feedback through the application; The cloud platform is used to store and analyze patients' historical health data, generate health records, and provide users with personalized health advice. The cloud platform introduces machine learning and deepseek big models. Based on the patient's basic vital signs, height, weight, waist circumference, physique, blood sugar, diet, exercise, mood, heart rate, the system is trained with certain algorithms and gives personalized guidance based on the patient's real-time situation.

10. The intelligent health management system according to claim 9, characterized in that: The data analysis module predicts the patient's future changes in blood glucose levels using the following derivation formula: in, For the future moment Predicted blood sugar levels, is the rate of change of current blood glucose level, and t is the predicted time interval.

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