Management system for oral medicine of stroke patient based on AI smart bracelet

Abstract

The invention discloses a cerebral apoplexy patient oral medicine management system based on an AI smart bracelet, and relates to the technical field of medical management, functional modules of the management system comprise a patient archive management module, a medicine intake prediction module, a dynamic medicine reminding module, a monitoring and early warning module and an illness state analysis and prediction module. According to the management system for oral medicine of the stroke patient based on the AI smart bracelet, dynamic management of oral medicine of the stroke patient is achieved, and the limitation that traditional medicine management lacks individuation and real-time adjustment is overcome. In combination with the individual characteristics of the patient, the medicine taking time is optimized, medicine reminding is dynamically adjusted, and physiological parameters are monitored in real time to reduce the side effect risk of the medicine. Meanwhile, the system provides a scientific basis for early prevention of cerebral apoplexy recurrence by analyzing and predicting a long-term health trend, so that the compliance and curative effect of medicine management are remarkably improved, and the overall health level of a patient is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and specifically to a management system for oral medication of stroke patients based on an AI smart bracelet. Background Art

[0002] Medication management for stroke patients is a crucial part of the treatment process. Its core goal is to ensure that patients take medication on time and in the right amount, while minimizing the risk of drug side effects and interactions. Existing medication management systems usually rely on static reminders and the patient's own compliance, but this model has significant limitations. By introducing the deep integration of AI smart bracelets and hospital information systems, intelligent optimization of the entire process from patient file establishment to medication management can be achieved. Combined with real-time data collection, dynamic analysis and prediction, the system can provide more accurate medication management services based on the patient's personalized characteristics.

[0003] In the prior art, there are the following problems: Lack of dynamic adjustment capabilities: Existing medication reminder systems mostly use fixed time point reminders, which are difficult to dynamically adjust according to the patient's activity status, physiological changes or special situations, which may cause the reminder time to be inconsistent with actual needs.

[0004] Lack of personalization: Most systems are based only on general medication schedules, ignoring individual differences in patients, such as important parameters such as sleep patterns, activity intensity, and heart rate. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a management system for oral medication for stroke patients based on an AI smart bracelet to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a management system for oral medication of stroke patients based on an AI smart bracelet, comprising: S1. Connect the AI ​​smart bracelet with the hospital information system, establish a file of stroke patients, and obtain the daily dosage data of oral medication for patients; S2. Based on the monitoring function of the AI ​​smart bracelet, the patient's physiological data and daily activity behavior patterns are obtained, and a drug intake prediction model is constructed. Combined with the patient's daily oral medication dosage data, the optimal time for the patient to take oral medication is predicted; S3. Based on the predicted optimal time for patients to take oral medications, dynamically adjust the reminder time, and push and remind; S4, monitoring function based on AI smart bracelet, when abnormalities are found, it will immediately issue an early warning to the patient and medical team; S5. The system collects and analyzes the patient's physiological data and medication data over a long period of time based on the AI ​​smart bracelet to identify health trends and predict the risk of stroke recurrence.

[0007] To further optimize the technical solution, in step S1, when establishing the file of the stroke patient, the steps specifically include: Import basic information, medical history, and drug allergy history provided by the patient; Based on the connected hospital information system, the patient's medical records and stroke-related diagnostic information are imported.

[0008] To further optimize the technical solution, in step S2, the drug intake prediction model predicts the optimal medication time based on the patient's physiological data and daily activity behavior patterns, and the model is as follows: ; in, : Best time to take medication; : Data on the patient's daily oral medication dosage; : Weight factor, which measures the impact of each parameter on the optimal medication time; : Sleep parameters, including sleep duration, depth and periodic characteristics; : Activity intensity, based on the amount and type of activity; : Real-time heart rate variation, deviation from baseline heart rate.

[0009] To further optimize the technical solution, in the drug intake prediction model: Sleep function As shown below: ; in, : Daily sleep duration; : Daily sleep quality score; : Average period of past days; Activity Function As shown below: ; in, : Activity intensity index, combining steps and calories burned; : current time; Heart rate function As shown below: : Current heart rate; : Average of historical resting heart rates.

[0010] To further optimize the technical solution, in step S3, a medication reminder algorithm is constructed, the algorithm reminds the patient at the predicted optimal medication time, and dynamically adjusts the reminder time according to the patient's real-time physiological state and activity pattern; When the patient is exercising vigorously, the medication reminder algorithm postpones the push reminder to take the medication to avoid conflict between the medication and the physiological response caused by exercise.

[0011] To further optimize this technical solution, the drug reminder algorithm is as follows: ; in, : Actual medication reminder time, i.e. the final calculated reminder time; : The predicted optimal time to take medication, i.e. the output of the drug intake prediction model; : Adjust according to the time of activity status; : Time adjustment according to physiological parameters; : Adjust the timing according to patient needs and environmental events.

[0012] To further optimize this technical solution, in the drug reminder algorithm: Time adjustment of activity status As shown below: ; in, : Real-time activity intensity index; : Activity intensity threshold, exceeding this value is considered strenuous activity; : Activity intensity adjustment factor, which measures the effect of activity intensity on the adjustment of medication time; Time of physiological parameters The adjustments are as follows: ; in, : Real-time heart rate; : Resting heart rate baseline value; : Real-time blood pressure; : Safe upper limit of blood pressure; : Physiological parameter adjustment coefficient, used to adjust the impact of heart rate and blood pressure on reminder time; Timing of patient needs and environmental events As shown below: ; in, : Including patients actively planning to delay medication, or the need for delay caused by the environment; : Adjustment factor used to quantify the impact of patient needs and environmental events on reminder time.

[0013] To further optimize the technical solution, in step S4, the AI ​​smart bracelet monitors the patient's physiological data in real time to identify early signs of drug side effects, and systematically analyzes changes in key indicators such as heart rate, blood pressure, and body temperature. After issuing an early warning, the impact of drug side effects on the patient's health is reduced.

[0014] To further optimize the technical solution, in step S5, the system is based on AI and uses a machine learning algorithm to predict the risk of stroke recurrence according to the patient's physiological data changes combined with medication data, and formulate preventive measures in advance. Through the accumulation and analysis of long-term data, a comprehensive health management plan is provided for patients.

[0015] To further optimize the technical solution, the functional modules of the management system include a patient file management module, a drug intake prediction module, a dynamic drug reminder module, a monitoring and early warning module, and a disease analysis and prediction module; Patient file management module, used to establish files of stroke patients and obtain daily oral medication dosage data; The drug intake prediction module predicts the optimal time for patients to take oral medication based on the drug intake prediction model; Dynamic medication reminder module, based on medication reminder algorithm, dynamically adjusts reminder time, pushes and reminds; The monitoring and early warning module sends an early warning to patients and medical teams when abnormalities are detected; The disease analysis and prediction module is used to identify health trends and predict the risk of stroke recurrence.

[0016] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a management system for oral medication for stroke patients based on an AI smart bracelet as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a management system for oral medication for stroke patients based on an AI smart bracelet as described in the first aspect of the present invention are implemented.

[0018] Compared with the prior art, the present invention provides a management system for oral medication of stroke patients based on an AI smart bracelet, which has the following beneficial effects: This oral medication management system for stroke patients based on AI smart bracelets realizes dynamic management of oral medications for stroke patients, overcoming the limitations of traditional medication management that lacks personalization and real-time adjustment. It combines the individual characteristics of patients to optimize medication time, dynamically adjusts medication reminders, and monitors physiological parameters in real time to reduce the risk of drug side effects. At the same time, the system provides a scientific basis for the early prevention of stroke recurrence through analysis and prediction of long-term health trends, thereby significantly improving the compliance and efficacy of medication management and improving the overall health level of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a management system for oral medication for stroke patients based on an AI smart bracelet proposed by the present invention; Figure 2 This is a schematic diagram of the functional modules of a management system for oral medication for stroke patients based on an AI smart bracelet proposed in the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Embodiment 1

[0024] Reference Figure 1 , which is the first embodiment of the present invention, provides a management system for oral medication of stroke patients based on an AI smart bracelet, including: S1. Connect the AI ​​smart bracelet with the hospital information system, establish the files of stroke patients, and obtain the daily dosage data of oral medication.

[0025] In this embodiment, when establishing a file of a stroke patient, the following steps are specifically included: Import basic information, medical history, and drug allergy history provided by the patient; Based on the connected hospital information system, the patient's medical records and stroke-related diagnostic information are imported.

[0026] Through the seamless connection between the AI ​​smart bracelet and the hospital information system, the patient's medical records and stroke-related diagnostic information can be automatically imported, providing a personalized reference basis for subsequent drug management. The establishment of patient files is the starting point of the entire system, ensuring that the management process is scientific and accurate.

[0027] S2. Based on the monitoring function of the AI ​​smart bracelet, the patient's physiological data and daily activity behavior patterns are obtained, and a drug intake prediction model is constructed. Combined with the patient's daily oral medication dosage data, the optimal time for the patient to take oral medication is predicted. This process combines drug management with the patient's physiological rhythm to ensure that the timing of medication is highly matched with the patient's physiological state, thereby significantly improving drug compliance and efficacy. It fully reflects the innovative application of AI technology in personalized medicine.

[0028] In this embodiment, the drug intake prediction model predicts the optimal medication time based on the patient's physiological data and daily activity behavior patterns. The model is as follows: ; in, : Best time to take medication (relative to the time point of the day, unit: hours); : Data on the patient's daily oral medication dosage; : Weight factor, which measures the impact of each parameter on the optimal medication time (obtained through historical data training); : Sleep parameters, including sleep duration, depth and periodic characteristics; : Activity intensity, based on the amount of activity (e.g., number of steps, energy expenditure) and type of activity (e.g., vigorous exercise, sedentary); : Real-time heart rate variation, deviation from baseline heart rate.

[0029] Furthermore, in the drug intake prediction model: Sleep function As shown below: ; in, : Daily sleep duration (unit: hours); : Daily sleep quality score (range: 0-1); : The average period of the past days (default is 7 days).

[0030] Activity Function As shown below: ; in, : Activity intensity index, combining steps and calories burned; : Current time (unit: hours).

[0031] Heart rate function As shown below: : Current heart rate; : Average of historical resting heart rates.

[0032] When this model is used, Collect data in real time through AI smart bracelet: Sleep data (such as , ).

[0033] Activity data (such as steps, activity type, energy consumption, used to calculate ).

[0034] Heart rate data (resting heart rate and real-time heart rate ).

[0035] After the data is preprocessed (such as denoising and normalization), the corresponding parameters of the input model are , , .

[0036] Model weight training: Weight The value of is learned through historical data, and an optimization algorithm (such as genetic algorithm or Bayesian optimization) is used to improve patient medication compliance and treatment efficacy.

[0037] After the model training is completed, the weights are dynamically adjusted according to the individual patient conditions.

[0038] Best time to take medication: Calculated according to the model based on the real-time data of the day.

[0039] Medication reminder and dynamic adjustment: Initial medication time and the model predicts Remind the patient.

[0040] If real-time physiological data changes significantly, the system will update And dynamically adjust reminders.

[0041] S3. Based on the predicted optimal time for patients to take oral medications, dynamically adjust reminder times, and perform push and reminder functions.

[0042] In this embodiment, a medication reminder algorithm is constructed, which reminds the patient at the predicted optimal medication time and dynamically adjusts the reminder time according to the patient's real-time physiological state and activity pattern; When the patient is exercising vigorously, the medication reminder algorithm postpones the push reminder of medication to avoid conflicts between medication and the physiological response caused by exercise. This dynamic adjustment function makes medication management more flexible and adaptable.

[0043] Furthermore, the medication reminder algorithm is as follows: ; in, : Actual medication reminder time, that is, the final calculated reminder time (final calculated reminder time, unit: hour); : The predicted optimal time to take medication, i.e. the output of the drug intake prediction model; : Adjust according to the time of activity status; : Time adjustment according to physiological parameters; : Adjust the timing according to patient needs and environmental events.

[0044] Furthermore, in the medication reminder algorithm: Time adjustment of activity status As shown below: ; in, : Real-time activity intensity index (calculated based on steps, energy consumption, etc.); : Activity intensity threshold, exceeding this value is considered strenuous activity; : Activity intensity adjustment factor, which measures the effect of activity intensity on the adjustment of medication time; Time of physiological parameters The adjustments are as follows: ; in, : Real-time heart rate; : Resting heart rate baseline value; : Real-time blood pressure; : Safety upper limit of blood pressure; : Physiological parameter adjustment coefficient, used to adjust the impact of heart rate and blood pressure on reminder time; Timing of patient needs and environmental events As shown below: ; in, : Including patients actively planning to delay medication, or the need for delay caused by the environment; : Adjustment factors, such as adjustment to the schedule based on patient interim reports, are used to quantify the impact of patient needs and environmental events on reminder times.

[0045] Based on this model, it includes: First reminder: Press Remind the patient; Dynamic adjustment: If the real-time data before the reminder (such as activity intensity, heart rate) changes significantly, the system recalculates in real time And adjust the reminder.

[0046] Combine activities, heart rate, blood pressure and special situations to flexibly adjust medication reminder times, avoiding the limitations of traditional static reminders. Combine multiple physiological data and patient behaviors to achieve more accurate personalized reminders. Monitor and recalculate reminder times in real time to ensure that reminders adapt to the patient's immediate status. Through compliance and efficacy feedback, continuously optimize algorithm parameters and improve the intelligence level of reminders.

[0047] S4. The monitoring function based on the AI ​​smart bracelet can immediately issue an early warning to the patient and the medical team when abnormalities are detected.

[0048] In this embodiment, the AI ​​smart bracelet monitors the patient's physiological data in real time to identify early signs of drug side effects. The system analyzes changes in key indicators such as heart rate, blood pressure, and body temperature, and after issuing an early warning, it reduces the impact of drug side effects on the patient's health. For example, when certain physiological indicators exceed the normal range, the system will immediately issue an early warning to notify the patient and the medical team. This function effectively reduces the potential harm of drug side effects to patient health through real-time data analysis and a rapid response mechanism, and provides timely intervention recommendations.

[0049] S5. The system collects and analyzes the patient's physiological data and medication data over a long period of time based on the AI ​​smart bracelet to identify health trends and predict the risk of stroke recurrence. The system can predict possible health problems in the future (such as the risk of stroke recurrence) and formulate personalized preventive measures in advance. It emphasizes the importance of long-term data accumulation and trend analysis, aiming to provide patients with a comprehensive health management plan and help the medical team better formulate treatment strategies.

[0050] In this embodiment, the system is based on AI and uses machine learning algorithms to predict the risk of stroke recurrence based on changes in the patient's physiological data combined with medication data, and to formulate preventive measures in advance. Through the accumulation and analysis of long-term data, a comprehensive health management plan is provided for patients. Embodiment 2

[0051] Reference Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a functional module of a management system for oral medication of stroke patients based on an AI smart bracelet, and the functional modules of the management system include a patient file management module, a medication intake prediction module, a dynamic medication reminder module, a monitoring and early warning module, and a condition analysis and prediction module.

[0052] The patient file management module is used to establish files for stroke patients and obtain daily oral medication dosage data.

[0053] The drug intake prediction module predicts the optimal time for patients to take oral medication based on the drug intake prediction model to improve medication compliance and efficacy.

[0054] The dynamic medication reminder module dynamically adjusts the reminder time based on the medication reminder algorithm, and pushes and reminds patients. It evaluates the patient's activity intensity, physiological state, special situational needs, etc. in real time, dynamically optimizes the reminder time, and improves the flexibility and scientificity of medication management. Reminder notifications are sent through smart bracelets or mobile phone APPs.

[0055] The monitoring and early warning module will issue early warnings to patients and medical teams when abnormalities are found. It will automatically send alerts to patients and medical teams to reduce the potential harm of drug side effects to patients' health.

[0056] The disease analysis and prediction module is used to identify health trends and predict the risk of stroke recurrence. It collects patients' long-term physiological data and drug management records, analyzes health trends, and identifies potential risks. It predicts the risk of stroke recurrence through machine learning algorithms and provides early prevention advice to patients. Embodiment 3

[0057] This embodiment also provides a computer device, which is suitable for a management system for oral medication for stroke patients based on an AI smart bracelet, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a management system for oral medication for stroke patients based on an AI smart bracelet as proposed in the above embodiment.

[0058] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a management system for oral medication for stroke patients based on an AI smart bracelet as proposed in the above embodiment is implemented.

[0059] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0060] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0061] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0062] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0063] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A management system for oral medication for stroke patients based on an AI smart bracelet, characterized in that: include: S1. Connect the AI ​​smart bracelet with the hospital information system, establish a file of stroke patients, and obtain the daily dosage data of oral medication for patients; S2. Based on the monitoring function of the AI ​​smart bracelet, the patient's physiological data and daily activity behavior patterns are obtained, and a drug intake prediction model is constructed. Combined with the patient's daily oral medication dosage data, the optimal time for the patient to take oral medication is predicted; S3. Based on the predicted optimal time for patients to take oral medications, dynamically adjust the reminder time, and push and remind; S4, monitoring function based on AI smart bracelet, when abnormalities are found, it will immediately issue an early warning to the patient and medical team; S5. The system collects and analyzes the patient's physiological data and medication data over a long period of time based on the AI ​​smart bracelet to identify health trends and predict the risk of stroke recurrence.

2. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In the step S1, when establishing the file of the stroke patient, it specifically includes: Import basic information, medical history, and drug allergy history provided by the patient; Based on the connected hospital information system, the patient's medical records and stroke-related diagnostic information are imported.

3. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In step S2, the drug intake prediction model predicts the optimal medication time based on the patient's physiological data and daily activity behavior patterns. The model is as follows: ； in, : Best time to take medication; : Data on the patient's daily oral medication dosage; : Weight factor, which measures the impact of each parameter on the optimal medication time; : Sleep parameters, including sleep duration, depth and periodic characteristics; : Activity intensity, based on the amount and type of activity; : Real-time heart rate variation, deviation from baseline heart rate.

4. According to claim 3, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In the drug intake prediction model: Sleep function As shown below: ； in, : Daily sleep duration; : Daily sleep quality score; : Average period of past days; Activity Function As shown below: ； in, : Activity intensity index, combining steps and calories burned; : current time; Heart rate function As shown below: : Current heart rate; : Average of historical resting heart rates.

5. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In step S3, a medication reminder algorithm is constructed, the algorithm reminds the patient at the predicted optimal medication time, and dynamically adjusts the reminder time according to the patient's real-time physiological state and activity pattern; When the patient is exercising vigorously, the medication reminder algorithm postpones the push reminder to take the medication to avoid conflict between the medication and the physiological response caused by exercise.

6. According to claim 5, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: The medication reminder algorithm is as follows: ； in, : Actual medication reminder time, i.e. the final calculated reminder time; : The predicted optimal time to take medication, i.e. the output of the drug intake prediction model; : Adjust according to the time of activity status; : Time adjustment according to physiological parameters; : Adjust the timing according to patient needs and environmental events.

7. According to claim 6, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In the medication reminder algorithm: Time adjustment of activity status As shown below: ； in, : Real-time activity intensity index; : Activity intensity threshold, exceeding this value is considered strenuous activity; : Activity intensity adjustment factor, which measures the effect of activity intensity on the adjustment of medication time; Time of physiological parameters The adjustments are as follows: ； in, : Real-time heart rate; : Resting heart rate baseline value; : Real-time blood pressure; : Safe upper limit of blood pressure; : Physiological parameter adjustment coefficient, used to adjust the impact of heart rate and blood pressure on reminder time; Timing of patient needs and environmental events As shown below: ； in, : Including patients actively planning to delay medication, or the need for delay caused by the environment; : Adjustment factor used to quantify the impact of patient needs and environmental events on reminder time.

8. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In step S4, the AI ​​smart bracelet monitors the patient's physiological data in real time to identify early signs of drug side effects, and systematically analyzes changes in key indicators such as heart rate, blood pressure, and body temperature. After issuing an early warning, it reduces the impact of drug side effects on the patient's health.

9. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: In step S5, the system is based on AI and uses machine learning algorithms to predict the risk of stroke recurrence according to the patient's physiological data changes combined with medication data, and formulate preventive measures in advance. Through the accumulation and analysis of long-term data, a comprehensive health management plan is provided for patients.

10. According to claim 1, a management system for oral medication for stroke patients based on an AI smart bracelet is characterized in that: The functional modules of the management system include patient file management module, drug intake prediction module, dynamic drug reminder module, monitoring and early warning module, and disease analysis and prediction module; Patient file management module, used to establish files of stroke patients and obtain daily oral medication dosage data; The drug intake prediction module predicts the optimal time for patients to take oral medication based on the drug intake prediction model; Dynamic medication reminder module, based on medication reminder algorithm, dynamically adjusts reminder time, pushes and reminds; The monitoring and early warning module sends an early warning to patients and medical teams when abnormalities are detected; The disease analysis and prediction module is used to identify health trends and predict the risk of stroke recurrence.

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