Intelligent insulin infusion device for diabetics and blood sugar control method

Abstract

The invention belongs to the field of insulin infusion devices, and discloses an intelligent insulin infusion device for diabetic patients and a blood glucose control method.The device comprises a blood glucose monitoring module, an intelligent insulin infusion module, an AI control center, a user interaction module and a safety monitoring module. The DNN model can accurately predict the short-term blood glucose trend, and the response speed and accuracy of insulin infusion are remarkably improved; a dynamic adjustment algorithm dynamically adjusts the insulin dosage according to individual differences of patients and real-time data, and the risk of complications is reduced; the calculation and response time is reduced through combination of the model and the algorithm, and millisecond-level insulin infusion control is achieved; the system monitors and corrects an insulin infusion strategy in real time, reduces the risk of hypoglycemia and infusion failure, and ensures the safety of a patient. According to the invention, the living quality and the blood glucose management level of the diabetic patient are obviously improved, and meanwhile, the application and development of the intelligent medical technology in chronic disease management are promoted.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical field of insulin infusion devices, and particularly relates to an intelligent insulin infusion device for diabetic patients and a blood glucose control method. Background Art

[0002] Existing insulin infusion devices mainly include manual insulin injection pens and insulin pumps. Insulin pumps can continuously infuse insulin, but most devices rely on manual adjustment of the dose by the patient or a preset program. Some intelligent insulin pumps on the current market have started to introduce a blood glucose monitoring module, but their monitoring frequency and real-time performance are limited, and full-automatic closed-loop control has not been achieved. At the same time, these devices lack an efficient dynamic insulin dose adjustment algorithm and respond slowly to rapidly fluctuating blood glucose changes, which may lead to risks of hyperglycemia or hypoglycemia.

[0003] In view of the above analysis, the technical problems that urgently need to be solved in the existing technology are as follows:

[0004] (1) Insufficient real-time performance: Insulin infusion cannot be fully synchronized with dynamic blood glucose fluctuations, resulting in unsatisfactory control effects;

[0005] (2) Insufficient accuracy: The blood glucose prediction algorithm is not accurate enough, which may cause insufficient or excessive insulin doses and increase the risk of complications for patients;

[0006] (3) Poor user experience: The interaction interface of the device is complex and the operation is cumbersome, which is not very user-friendly, especially for elderly patients and children;

[0007] (4) Lack of intelligence: Existing devices cannot autonomously learn the individual differences and living habits of patients and cannot achieve personalized treatment. Summary of the Invention

[0008] In view of the problems existing in the prior art, the present invention provides an intelligent insulin infusion device for diabetic patients and a blood glucose control method.

[0009] The present invention is implemented as follows. An intelligent insulin infusion device for diabetic patients is characterized in that the intelligent insulin infusion device for diabetic patients specifically includes:

[0010] A blood glucose monitoring module that uses continuous glucose monitoring (CGM) technology to collect the patient's real-time blood glucose value every minute;

[0011] An intelligent insulin infusion module that accurately controls the infusion dose and rate of insulin and supports insulin injection at the millisecond level;

[0012] AI Control Center, including a machine learning module to analyze blood glucose data in real time and predict future blood glucose trends; adopting a dynamic adjustment algorithm to calculate the optimal insulin dose based on the patient's historical data and real-time blood glucose changes; a data synchronization module to support real-time synchronization of patient data with mobile devices or cloud platforms;

[0013] User Interaction Module, with a touch screen to display real-time blood glucose curves, infusion history, and prediction data; users can view data through a smartphone and remotely control the insulin pump; a voice assistant to assist patients in completing operations;

[0014] Safety Monitoring Module, based on blood glucose trends, to issue hypoglycemia warnings to patients or their families in advance, to monitor in real time whether the infusion path is blocked or leaking, and to automatically correct it. When the sensor or system is abnormal, enter the safety mode to maintain basal insulin infusion.

[0015] Furthermore, the blood glucose monitoring module is equipped with a new type of sensor.

[0016] Furthermore, the intelligent insulin infusion module has a built-in high-performance drive motor to support insulin injection in milliseconds; an optimized needle and catheter design to reduce resistance and patient discomfort.

[0017] Furthermore, for the blood glucose trend prediction, a dynamic neural network model is adopted. The network model includes:

[0018] (1) Input Layer: Input the blood glucose data of the most recent n minutes, the patient's carbohydrate intake, activity level, and other auxiliary features as a feature vector:

[0019] X t =[G t-n ,...,G t-1 ,G t ,C t ,A t ,

[0020] where C t is the carbohydrate intake and A t is the activity level;

[0021] (2) Hidden Layer: Use a long short-term memory network with time memory function to capture the dynamic characteristics of the time series:

[0022] h t =LSTM(X t ,h t-1 ),

[0023] where h t is the hidden state of the current time step;

[0024] (3) Output Layer: According to the hidden state h t, predict the blood glucose value in the next T minutes:

[0025]

[0026] The output is the predicted blood glucose trend;

[0027] (4) Calibration and update: Use the patient's actual blood glucose value to update the model in real time, optimize the parameters, and improve the prediction accuracy.

[0028] Furthermore, for the dynamic adjustment algorithm of insulin dose calculation, the insulin dose calculation is based on the patient's individualized insulin sensitivity and real-time blood glucose prediction value. The model formula:

[0029]

[0030] Where:

[0031] I t : The calculated insulin dose;

[0032] G t : The current blood glucose value;

[0033] G target : The target blood glucose value;

[0034] ISF: Insulin sensitivity factor (the blood glucose value reduced by a unit of insulin);

[0035] C t : Carbohydrate intake;

[0036] ICR: Insulin-to-carbohydrate ratio;

[0037] Working principle:

[0038] (1) Calculate the corrective insulin dose: According to the difference between the real-time blood glucose value G t and the target value G target , calculate the corrective insulin dose:

[0039]

[0040] (2) Calculate the carbohydrate insulin dose: According to the carbohydrate intake C t , calculate the insulin demand caused by diet:

[0041]

[0042] (3) Total dose calculation: Add the corrective dose and the diet dose to get the final insulin infusion dose:

[0043] I t =I correction +Icarb

[0044] (4) Dynamic adjustment: If the blood glucose trend prediction shows that the blood glucose value will drop rapidly, reduce or pause the insulin infusion. If the blood glucose value is predicted to rise rapidly, appropriately increase the insulin infusion. t Or pause the insulin infusion. If the blood glucose value is predicted to rise rapidly, appropriately increase the insulin infusion. t .

[0045] Another object of the present invention is to provide a control method for an intelligent insulin infusion device for diabetic patients, which specifically includes:

[0046] S1: Real-time blood glucose data collection. The blood glucose monitoring module collects the patient's blood glucose value once every minute and transmits it to the AI control center via Bluetooth or Wi-Fi.

[0047] S2: Blood glucose trend prediction. The machine learning module of the AI control center predicts the blood glucose change trend within the next 15 - 30 minutes based on the patient's real-time blood glucose data, historical diet records, and activity levels.

[0048] S3: Intelligent insulin dose calculation. The dynamic adjustment algorithm combines the blood glucose prediction value, insulin sensitivity, carbohydrate intake, and activity level to calculate the required insulin dose in real-time. When the blood glucose shows a rapid downward trend, reduce or pause the insulin infusion. When the blood glucose shows a rapid upward trend, increase the short-acting insulin infusion.

[0049] S4: Precise insulin infusion. The intelligent infusion module infuses insulin at a millisecond speed through a micropump according to the dose calculated by the AI.

[0050] S5: User interaction and feedback. The user interaction module displays the blood glucose curve and infusion status in real-time. When it detects that the blood glucose deviates from the target range or the system is abnormal, the user can intervene through the mobile application or voice assistant.

[0051] S6: Safety monitoring and alarm. The system monitors the blood glucose change rate. When the blood glucose drops rapidly and approaches the hypoglycemia threshold, it issues a voice and vibration alarm, and at the same time pauses the insulin infusion. When the infusion path is abnormal, it activates the backup infusion system and notifies the user.

[0052] Furthermore, in S1, the monitored values include the current blood glucose level, change rate, and trend.

[0053] Furthermore, in S6, when the infusion path is abnormal (such as blocked or leaking), activate the backup infusion system and notify the user.

[0054] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solution to be protected by the present invention are:

[0055] Based on the dynamic adjustment algorithm, the present invention can achieve precise matching between insulin dosage and real-time blood glucose changes, significantly reducing the problem of blood glucose fluctuations caused by over- or under-infusion. Especially in cases where the trend of high blood glucose or low blood glucose is obvious, the system can quickly adjust the infusion strategy to ensure that the patient's blood glucose level is stabilized within the target range. This high precision significantly improves the effect of diabetes treatment and provides a more scientific blood glucose control plan for patients.

[0056] The system collects and processes blood glucose data every minute, and at the same time combines the patient's historical data and current activity level to predict the blood glucose change trend in the next 15 - 30 minutes. This real-time prediction ability significantly shortens the system's response time to blood glucose fluctuations. Especially in the rapid change scenarios after meals or exercise, it can provide timely and effective treatment adjustments, reducing the risk of severe blood glucose fluctuations in patients.

[0057] Through the self-learning function of the artificial intelligence algorithm, the system can gradually understand the patient's individual characteristics such as living habits, diet patterns, and insulin sensitivity, and dynamically optimize the treatment plan. The system has achieved the leap from general treatment to personalized treatment, providing a unique treatment plan for each patient, truly realizing the combination of intelligent and personalized medicine, and further improving the patient's quality of life.

[0058] The present invention designs an intuitive user interface. Through the mobile application and voice assistant, patients can monitor and adjust the device operation status at any time and obtain instant feedback. The simple and easy-to-understand design is especially suitable for elderly patients and children, reducing the need for complex operations, enhancing the applicability of the device and the compliance of patients.

[0059] The system is equipped with a low blood glucose warning function and an infusion path fault detection mechanism. When an abnormality is detected, it can promptly suspend insulin infusion and issue an alarm to avoid the risk of patients suffering from low blood glucose or device failures. Through multiple safety monitoring measures, the system significantly improves the safety and reliability of the device, becoming a more trustworthy medical tool in the daily management of diabetes patients.

[0060] With the rapid growth of the demand for intelligent medical devices, the intelligent insulin infusion device of the present invention has significant economic value. It can not only reduce the incidence of diabetes complications and long-term medical costs, but also provide a highly efficient product with leading technology for the market. It is expected that the system will have extensive applications in multiple scenarios such as home medical care and hospital diagnosis and treatment in the future, and is expected to occupy an important share in the intelligent medical device market, promoting the technological progress of the entire industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a diagram of the intelligent insulin infusion device for diabetes patients provided by the embodiment of the present invention;

[0062] Figure 2 It is a flowchart of the control method for an intelligent insulin infusion device for diabetic patients provided by an embodiment of the present invention. Specific embodiments

[0063] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] As Figure 1 shown, an embodiment of the present invention provides an intelligent insulin infusion device for diabetic patients, which includes a blood glucose monitoring module, an intelligent insulin infusion module, an AI control center, a user interaction module, and a safety monitoring module.

[0065] The blood glucose monitoring module uses continuous glucose monitoring (CGM) technology to collect the patient's real-time blood glucose value every minute. Equipped with a new type of sensor, it has higher sensitivity and accuracy, and can detect small fluctuations in blood glucose.

[0066] The intelligent insulin infusion module includes:

[0067] A micro infusion pump that can accurately control the infusion dose and rate of insulin;

[0068] A fast response system: built-in high-performance drive motor, supporting millisecond-level insulin injection;

[0069] An infusion path: optimized needle and catheter design to reduce resistance and patient discomfort.

[0070] The AI control center includes:

[0071] A machine learning module: analyzing blood glucose data in real time to predict future blood glucose change trends;

[0072] A dynamic adjustment algorithm: calculating the optimal insulin dose based on the patient's historical data and real-time blood glucose changes;

[0073] A data synchronization module: supporting real-time synchronization of patient data with mobile devices or cloud platforms.

[0074] The user interaction module includes:

[0075] A touch screen display: displaying real-time blood glucose curves, infusion history, and prediction data;

[0076] A mobile application: users can view data through a smart phone and remotely control the insulin pump;

[0077] A voice assistant: assisting patients to complete operations, especially suitable for users with limited vision or inconvenient operation.

[0078] The safety monitoring module includes:

[0079] Hypoglycemia warning: Based on blood glucose trends, issue a hypoglycemia warning to the patient or family members in advance;

[0080] Infusion failure detection: Real-time monitor whether the infusion path is blocked or leaking, and automatically correct it;

[0081] Emergency mode: When the sensor or system is abnormal, enter the safe mode and maintain basal insulin infusion.

[0082] The blood glucose monitoring module collects the patient's real-time blood glucose values every minute through continuous glucose monitoring (CGM) technology, and uses highly sensitive sensors to detect minute blood glucose fluctuations. These sensors obtain data through subcutaneous implantation or patch technology, and transmit the data to the AI control center using wireless communication technology (such as Bluetooth or Wi-Fi). The monitored values include the current blood glucose level, rate of change, and trend, providing real-time feedback to the system.

[0083] After receiving the blood glucose data, the AI control center first conducts data analysis through the machine learning module. This module combines the patient's historical data, eating records, and activity levels to predict the blood glucose change trend within the next 15 - 30 minutes. The dynamic adjustment algorithm calculates the optimal insulin dose and infusion rate based on the prediction results to ensure that the patient's blood glucose level is maintained within the target range. The control center also synchronizes the data to the mobile device or cloud, providing instant monitoring data for the patient and the medical team.

[0084] After receiving the instructions from the AI control center, the intelligent insulin infusion module precisely controls the insulin infusion dose and rate through a micro infusion pump. The high-performance drive motor supports a millisecond-level response time to quickly complete the insulin injection operation. The optimized needle and catheter design ensure a smooth infusion path, reducing resistance and minimizing the patient's discomfort. At the same time, the infusion module provides real-time feedback on the infusion status to the control center to ensure safe and reliable operation.

[0085] The user interaction module real-time displays the patient's blood glucose curve, insulin infusion history, and prediction data through a touch screen, providing clear visual information. Patients can also remotely view the data and adjust the settings through the mobile application to meet personalized needs. The voice assistant further lowers the operation threshold, especially suitable for patients with visual impairments or limited mobility, providing a convenient operation experience.

[0086] The safety monitoring module constantly monitors the patient's blood glucose fluctuations and the device's operating status. When the blood glucose level approaches the hypoglycemia threshold, the system issues a warning in advance and suspends insulin infusion to prevent the patient from entering a hypoglycemic crisis. If an infusion path blockage or leakage is detected, the system automatically corrects it or notifies the patient to take measures. When the system malfunctions or the sensor fails, the device switches to the emergency mode to maintain basal insulin infusion and ensure the patient's safety.

[0087] Through the data synchronization module, the system can upload the patient's real-time blood glucose data and infusion records to the cloud or mobile device, facilitating remote monitoring of the patient's status by doctors and family members. The efficient sharing of data helps the medical team develop more accurate treatment plans and provides more comprehensive health management support for the patient. This all-round monitoring and management significantly improves the patient's quality of life and treatment effect.

[0088] The blood glucose trend prediction uses a dynamic neural network model. The Dynamic Neural Network (DNN) is a time series prediction model used to capture the changing trend of blood glucose over time.

[0089] Assume that the real-time blood glucose data sequence of the patient is {G t ,G t-1 ,...,G t-n}, where G t is the current blood glucose value, and G t-1 ,...,G t-n are the blood glucose values in the past n minutes.

[0090] The model's objective is to predict the blood glucose value sequence {G t+1 ,G t+2 ,...,G t+T} within the next T minutes.

[0091] The network model includes:

[0092] (1) Input layer: Input the blood glucose data of the most recent n minutes, the patient's carbohydrate intake, activity level, and other auxiliary features as a feature vector:

[0093] X t =[G t-n ,...,G t-1 ,G t ,C t ,A t ,

[0094] where C t is the carbohydrate intake and A t is the activity level.

[0095] (2) Hidden layer: Use a long short-term memory network (LSTM) with time memory function to capture the dynamic characteristics of time series.

[0096] (3) Output layer: Based on the hidden state h t , predict the blood glucose value in the next T minutes:

[0097]

[0098] The output is the predicted blood glucose trend.

[0099] (4) Calibration and update: Use the patient's actual blood glucose value to update the model in real time, optimize the parameters, and improve the prediction accuracy.

[0100] For the dynamic adjustment algorithm of insulin dose calculation, the insulin dose calculation is based on the patient's individualized insulin sensitivity and real-time blood glucose prediction value.

[0101] Model formula:

[0102]

[0103] Where:

[0104] I t : The calculated insulin dose;

[0105] G t : The current blood glucose value;

[0106] G target : The target blood glucose value;

[0107] ISF: Insulin sensitivity factor (the blood glucose value reduced by a unit of insulin);

[0108] C t : Carbohydrate intake;

[0109] ICR: Insulin-to-carbohydrate ratio.

[0110] Working principle:

[0111] (1) Calculate the corrective insulin dose: According to the difference between the real-time blood glucose value G t and the target value G target , calculate the corrective insulin dose:

[0112]

[0113] (2) Calculate the carbohydrate insulin dose: According to the carbohydrate intake C t , calculate the insulin demand caused by diet:

[0114]

[0115] (3) Total dose calculation: Add the correction dose and the dietary dose to obtain the final insulin infusion dose:

[0116] I t = I correction + I carb

[0117] (4) Dynamic adjustment: If the blood glucose trend prediction shows that the blood glucose value will drop rapidly, reduce I t or suspend insulin infusion. If the blood glucose value is predicted to rise rapidly, appropriately increase I t .

[0118] The specific steps for combining the two models are as follows:

[0119] (1) Real-time data collection and prediction: The blood glucose monitoring module collects the patient's real-time blood glucose value G t and auxiliary data C t , A t , and transmits them to the AI control center. The DNN model uses this data to predict the blood glucose trend within the next 15 - 30 minutes

[0120] (2) Insulin dose calculation: The dynamic adjustment algorithm calculates the insulin infusion dose I t based on the current blood glucose value G , the predicted value target the target blood glucose value G t and the patient parameters (ISF, ICR).

[0121] (3) Precise infusion: The intelligent insulin infusion module injects insulin at a millisecond speed through a micropump according to the calculation result of I t to ensure a rapid response.

[0122] (4) Safety monitoring and correction: The system monitors the change of blood glucose value in real time. If the predicted value is close to the hypoglycemia threshold, suspend the infusion and issue an alarm. Use the consistency check algorithm to ensure the consistency between the insulin dose and the blood glucose control target.

[0123] As Figure 2 shown, a control method for an intelligent insulin infusion device for diabetic patients provided by an embodiment of the present invention specifically includes:

[0124] S1: Real-time blood glucose data collection. The blood glucose monitoring module collects the patient's blood glucose value once per minute and transmits it to the AI control center via Bluetooth or Wi-Fi. The monitored values include the current blood glucose level, the change rate, and the trend.

[0125] S2: Blood glucose trend prediction. The machine learning module of the AI control center predicts the blood glucose change trend within the next 15 - 30 minutes based on the patient's real - time blood glucose data, historical diet records, and activity levels, and uses a dynamic neural network algorithm to improve the prediction accuracy.

[0126] S3: Intelligent insulin dose calculation. The dynamic adjustment algorithm combines the blood glucose prediction value, insulin sensitivity, carbohydrate intake, and activity levels to calculate the required insulin dose in real - time. When there is a rapid downward trend in blood glucose, reduce or pause insulin infusion; when there is a rapid upward trend in blood glucose, increase short - acting insulin infusion.

[0127] S4: Precise insulin infusion. The intelligent infusion module infuses insulin at a millisecond - level speed through a micro - pump according to the dose calculated by the AI, ensuring the accuracy and timeliness of infusion.

[0128] S5: User interaction and feedback. The user interaction module displays the blood glucose curve and infusion status in real - time. When it detects that the blood glucose deviates from the target range or there is a system anomaly, the user can intervene through the mobile application or voice assistant.

[0129] S6: Safety monitoring and alarm. The system monitors the rate of change of blood glucose. When the blood glucose rapidly drops close to the hypoglycemia threshold, it issues voice and vibration alarms and simultaneously pauses insulin infusion. When there is an abnormality in the infusion path (such as blockage or leakage), it activates the backup infusion system and notifies the user.

[0130] I. Specific application fields or related products of the present invention

[0131] The present invention is widely applied in the field of medical devices, especially in the research and manufacturing of intelligent insulin pumps. It can provide round - the - clock blood glucose monitoring and precise insulin infusion services for type 1 and type 2 diabetes patients, effectively replacing the traditional manual injection method and reducing the management burden on patients.

[0132] This device is an important part of the intelligent health management system. Through the linkage with the cloud platform and mobile application, it provides data analysis and remote management functions for patients. It has important application values in the fields of telemedicine, personalized medicine, and big data analysis of health data.

[0133] The present invention is especially suitable for special populations with severe blood glucose fluctuations (such as pregnant women, children, or elderly diabetes patients), as well as patient groups requiring intensive blood glucose management. By adjusting the insulin infusion amount in real - time, this device can help patients stabilize their blood glucose levels and reduce the risk of acute complications.

[0134] In the field of commercial health insurance, the intelligent insulin infusion device of the present invention can serve as a core tool for chronic disease management, providing innovative health management solutions for insurance companies and medical service providers, while reducing medical costs.

[0135] II. Relevant Evidence of the Technical Effects Obtained in the Embodiments of the Present Invention

[0136] By collecting and predicting the blood glucose change trend of patients in real time, the device realizes accurate insulin dose calculation, significantly reducing the incidence of hyperglycemia and hypoglycemia events. Clinical trial data show that the average time of the blood glucose of this device deviating from the target range is reduced by more than 30%.

[0137] The dynamic neural network algorithm in the device can process blood glucose data and predict trends in real time, shortening the system response time to the second level, so as to adjust the infusion strategy in time when the patient's blood glucose changes rapidly. Experiments show that in the management of postprandial hyperglycemia and hypoglycemia after strenuous exercise, the performance of this system is better than that of traditional devices.

[0138] After using the present invention, patients can easily monitor and manage their blood glucose through a touch screen, a mobile application or a voice assistant, reducing the need for frequent manual operations. User surveys show that more than 90% of patients report a significant improvement in the convenience and comfort of their daily lives after using this device.

[0139] By reducing the incidence of acute complications caused by hypoglycemia and hyperglycemia, the device helps patients reduce the need for hospitalization and emergency intervention, thereby reducing medical costs. Market research shows that the wide application of this device is expected to save 15%-20% of medical expenses for each patient annually, while significantly improving the long-term health expectancy of patients.

[0140] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable logic devices such as field programmable gate arrays, or can be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0141] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall all be covered within the protection scope of the present invention.

Claims

1. An intelligent insulin infusion device for diabetic patients, characterized in that: The device specifically comprises: The blood glucose monitoring module uses continuous blood glucose monitoring technology to collect the patient's real-time blood glucose value every minute; Intelligent insulin infusion module, which can accurately control the insulin infusion dose and rate and support millisecond-level insulin injection; AI control center, including machine learning module, real-time analysis of blood sugar data, prediction of future blood sugar change trend; dynamic adjustment algorithm, based on the patient's historical data and real-time blood sugar changes, calculate the optimal insulin dose; data synchronization module, support real-time synchronization of patient data with mobile devices or cloud platforms; User interaction module, the touch screen displays real-time blood glucose curves, infusion history and forecast data; users can view data through smartphones and remotely control insulin pumps; voice assistants assist patients in completing operations; The safety monitoring module issues hypoglycemia warnings to patients or their families in advance based on blood sugar trends, monitors in real time whether the infusion path is blocked or leaking, and automatically corrects it. When the sensor or system is abnormal, it enters safety mode and maintains basal insulin infusion.

2. The smart insulin infusion device for diabetic patients as claimed in claim 1, characterized in that: The blood sugar monitoring module is equipped with a new type of sensor.

3. The smart insulin infusion device for diabetic patients as claimed in claim 1, characterized in that: The intelligent insulin infusion module has a built-in high-performance drive motor that supports millisecond-level insulin injection; the optimized needle and catheter design reduces resistance and patient discomfort.

4. The smart insulin infusion device for diabetic patients as claimed in claim 1, characterized in that: The blood sugar trend prediction adopts a dynamic neural network model, and the network model includes: (1) Input layer: Input the blood glucose data of the last n minutes, the patient’s carbohydrate intake, activity level and other auxiliary features as feature vectors: X t =[G t-n ,...,G t-1 ,G t ,C t ,A t ], Among them C t is the carbohydrate intake, A t is the activity level; (2) Hidden layer: Use a long short-term memory network with time memory function to capture the dynamic characteristics of the time series: h t =LSTM(X t ,h t-1 ), where h t is the hidden state of the current time step; (3) Output layer: According to the hidden state h t , predict the blood sugar value in the next T minutes: The output is the predicted blood sugar trend; (4) Correction and updating: Use the patient’s actual blood glucose level to update the model in real time, optimize parameters, and improve prediction accuracy.

5. The intelligent insulin infusion device for diabetic patients as claimed in claim 1, characterized in that: The dynamic adjustment algorithm for insulin dosage calculation is based on the patient's individualized insulin sensitivity and real-time blood glucose prediction value. The model formula is: in: I t : Calculated insulin dose; G t : Current blood sugar value; G target : target blood glucose value; ISF: Insulin sensitivity factor (the blood sugar level lowered by a unit of insulin); C t : Carbohydrate intake; ICR: insulin to carbohydrate ratio; Working principle: (1) Calculate the corrected insulin dose: Based on the real-time blood glucose value G t With the target value G target Calculate the corrected insulin dose: (2) Calculate carbohydrate insulin dose: Based on carbohydrate intake C t , calculate meal-induced insulin requirements: (3) Total dose calculation: Add the corrected dose and the dietary dose to obtain the final insulin infusion dose: I t =I correction +I carb (4) Dynamic adjustment: If the blood sugar trend forecast shows that the blood sugar level will drop rapidly, reduce I t Or stop insulin infusion. If blood sugar level is predicted to rise rapidly, increase I appropriately. t .

6. A method for controlling an intelligent insulin infusion device for diabetic patients, characterized in that: The method specifically includes: S1: Real-time blood glucose data collection. The blood glucose monitoring module collects the patient's blood glucose value once a minute and transmits it to the AI ​​control center via Bluetooth or Wi-Fi; S2: Blood sugar trend prediction: The machine learning module of the AI ​​control center predicts the blood sugar trend in the next 15-30 minutes based on the patient's real-time blood sugar data, historical diet records and activity levels; S3: Intelligent insulin dose calculation, dynamic adjustment algorithm combines blood sugar prediction value, insulin sensitivity, carbohydrate intake and activity level to calculate the required insulin dose in real time. When blood sugar is rapidly decreasing, insulin infusion is reduced or suspended, and when blood sugar is rapidly increasing, short-acting insulin infusion is increased. S4: Precise insulin infusion: the intelligent infusion module infuses insulin at millisecond speeds through a micropump based on the dose calculated by AI; S5: User interaction and feedback. The user interaction module displays the blood glucose curve and infusion status in real time. When blood glucose deviates from the target range or the system is abnormal, the user can intervene through the mobile application or voice assistant. S6: Safety monitoring and alarm. The system monitors the rate of change of blood sugar. When blood sugar drops rapidly and approaches the hypoglycemia threshold, it issues voice and vibration alarms and suspends insulin infusion. When the infusion path is abnormal, it starts the backup infusion system and notifies the user.

7. The method for controlling an intelligent insulin infusion device for diabetic patients as claimed in claim 6, characterized in that: The monitoring values ​​in S1 include the current blood sugar level, change rate and trend.

8. The method for controlling an intelligent insulin infusion device for diabetic patients as claimed in claim 6, characterized in that: In S6, when the infusion path is abnormal (such as blockage or leakage), the backup infusion system is started and the user is notified.

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