Personalized medication dosage adjusting and doctor seeing reminding method based on intelligent medicine box
By synchronizing and processing data between the smart pillbox and the wearable device, the problem of synchronizing medication behavior with physiological response time is solved, enabling personalized medication dosage adjustment and appointment reminders, thus improving the accuracy and efficiency of medication and appointment plans.
Patent Information
- Application Number
- CN202511011801.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-22
- Publication Date
- 2025-10-31
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing smart pillboxes and wearable devices lack a time synchronization mechanism in the field of medical IoT, which makes it impossible to accurately match medication behavior with physiological response, identify the causal relationship between abnormal reactions after medication and drug overdose, and make it impossible to effectively adjust medication differences and treatment plans between individuals.
By synchronizing data between the smart pillbox and the wearable device, information such as the weight, quantity, and location of the medicine, as well as physiological information such as body temperature, heart rate, and respiratory rate, is obtained. Data alignment and aggregation calculations are performed to generate a medication feature matrix and a treatment plan matrix, adjust the medication dosage, and generate a treatment plan to achieve personalized adjustments.
It improves the accuracy and efficiency of adjusting medication and treatment plans, can identify individual differences in medication and make dynamic adjustments, and ensures medication safety and timely medical treatment.
Smart Images
Figure CN120878035A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of smart pillboxes, specifically to a method for personalized medication dosage adjustment and appointment reminders based on a smart pillbox. Background Technology
[0002] While current smart pillboxes and wearable devices have achieved basic data monitoring (such as drug weight and heart rate) in the field of Internet of Medical Technology (IoMT), there is a general lack of a time synchronization mechanism between pillbox monitoring data (drug weight, quantity, and location) and human physiological data (body temperature, heart rate, and respiratory rate). This results in an inaccurate match between medication behavior and physiological responses. For example, existing technologies cannot identify the causal relationship between "abnormally elevated body temperature within 30 minutes of medication administration" and "drug overdose," making it impossible to effectively adjust individual differences in medication use and treatment plans. Summary of the Invention
[0003] This invention provides a method for personalized medication dosage adjustment and appointment reminders based on a smart pillbox, which at least solves the problems of individual differences in medication and the inability to effectively adjust appointment plans in related technologies.
[0004] According to one embodiment of the present invention, a method for personalized medication dosage adjustment and appointment reminder based on a smart pillbox is provided, comprising: The system acquires monitoring data from a smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug within the smart pillbox. The physiological information includes at least body temperature, heart rate, and / or respiratory rate. The monitoring data and physiological information are compared with a preset medication dosage plan. The medication dosage plan is adjusted according to the first comparison result to obtain a first plan. The first comparison is repeated within a preset time period. The monitoring data and physiological information obtained last within the preset time period are compared with the preset medical treatment plan, and a medical treatment plan is generated based on the results of the second comparison. The medical treatment plan is sent to the target device and the target device is instructed to display the medical treatment plan.
[0005] In one exemplary embodiment, before performing a first comparison between the monitoring data and physiological information and a preset medication dosage regimen, the method further includes: The monitoring data and physiological information are aligned according to the time series. Perform a first aggregation operation on the alignment result to generate a medication feature vector, and construct a medication feature matrix based on the medication feature vector; A second aggregation operation is performed on the preset medication dosage scheme to generate a medication scheme vector, and a medication scheme matrix is constructed based on the medication scheme vector.
[0006] In an exemplary embodiment, the step of performing a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: A first comparison is made between the medication feature vector in the medication feature matrix and the medication plan vector in the medication plan matrix to determine the out-of-bounds value of the medication feature matrix; The total out-of-bounds value of the medication feature matrix is determined based on the out-of-bounds value, and the risk level is determined based on the total out-of-bounds value; The medication dosage scheme is adjusted according to the risk level and the medication feature vector to obtain the first scheme.
[0007] In an exemplary embodiment, the step of performing a second comparison between the last acquired monitoring data and the physiological information within a preset time period and a preset medical treatment plan, and generating a medical treatment plan based on the second comparison result, includes: A second comparison is made between the total value of the out-of-bounds data corresponding to the last obtained monitoring data and physiological information and the risk threshold corresponding to the preset medical treatment plan; If the second comparison result is within the first range, it is determined to be in an abnormal state, and the preset medical treatment plan is adjusted accordingly. If the second comparison result exceeds the first range, an emergency situation is determined, and the preset medical treatment plan is adjusted a second time.
[0008] In an exemplary embodiment, the step of performing a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: Calculate the similarity between the medication feature matrix and the medication regimen matrix; If the difference between the similarity and the preset threshold is greater than a third range, the first medication regimen that is closest to the medication regimen matrix is matched, and the first medication regimen is taken as the first regimen.
[0009] According to another embodiment of the present invention, a personalized medication dosage adjustment and appointment reminder device based on a smart pillbox is provided, comprising: An information acquisition module is used to acquire monitoring data from the smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug within the smart pillbox; the physiological information includes at least body temperature, heart rate, and / or respiratory rate. The comparison and adjustment module is used to make a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjust the medication dosage plan according to the first comparison result to obtain a first plan, and repeat the first comparison within a preset time period. The second comparison module is used to compare the last monitoring data and the physiological information obtained within a preset time period with a preset medical treatment plan, and generate a medical treatment plan based on the second comparison result. The treatment plan display module is used to send the treatment plan to the target device and instruct the target device to display the treatment plan.
[0010] In one exemplary embodiment, the apparatus further includes: The data alignment module is used to align the monitoring data and physiological information according to a time series before making a first comparison with the preset medication dosage plan; The first matrix module is used to perform a first aggregation operation on the alignment processing result, generate a medication feature vector, and construct a medication feature matrix based on the medication feature vector; The second matrix module is used to perform a second aggregation operation on the preset medication dosage scheme to generate a medication scheme vector, and to construct a medication scheme matrix based on the medication scheme vector.
[0011] In an exemplary embodiment, the step of performing a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: A first comparison is made between the medication feature vector in the medication feature matrix and the medication plan vector in the medication plan matrix to determine the out-of-bounds value of the medication feature matrix; The total out-of-bounds value of the medication feature matrix is determined based on the out-of-bounds value, and the risk level is determined based on the total out-of-bounds value; The medication dosage scheme is adjusted according to the risk level and the medication feature vector to obtain the first scheme.
[0012] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.
[0013] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.
[0014] By combining medication use with human physiological conditions, this invention enables multidimensional decision-making for adjusting treatment plans. Therefore, it can solve the problems of individual differences in medication use and the inability to effectively adjust treatment plans, thereby improving the accuracy and efficiency of adjusting medication and treatment plans for individuals. Attached Figure Description
[0015] Figure 1 This is a flowchart of a method for personalized medication dosage adjustment and medical appointment reminder based on a smart pillbox according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a personalized medication dosage adjustment and medical appointment reminder device based on a smart pillbox according to an embodiment of the present invention. Detailed Implementation
[0016] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0017] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0018] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.
[0019] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "coupled" can refer to an electrical connection that enables signal transmission.
[0020] As used herein, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).
[0021] Example 1 This embodiment provides a method for personalized medication dosage adjustment and appointment reminders based on a smart pillbox. Figure 1 This is a flowchart illustrating a personalized medication dosage adjustment and appointment reminder method based on a smart pillbox according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S11: Acquire monitoring data from the smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug within the smart pillbox; the physiological information includes at least body temperature, heart rate, and / or respiratory rate. In this embodiment, sensors are installed in the smart pillbox to detect the user's medication usage and connect to the user's worn human body device via the Internet of Things to obtain the user's physiological response after medication.
[0022] The sensors in the pillbox include (but are not limited to) weight sensors to detect the weight of the medicine, RFID tags to detect and identify the remaining quantity of medicine (or image recognition using a camera). For medicines with fixed placement locations (such as holes for storing medicines), infrared sensors (or weight sensors, position sensors, specific RFID tags, etc.) can also be installed at these locations. When medicine is taken from a certain location, the coordinates of the corresponding location are collected. The wearable device includes (but is not limited to) a wristband, a ventilator (or a breathing sensor), etc. The connection between the pillbox and the wearable device can be a direct communication connection via Bluetooth, NFC, or data, or it can be connected to a mobile device, PC, or gateway via Ethernet, and then the signal is transmitted through the mobile device, PC, or gateway. The acquired data is then stored in a local database or cloud server for easy retrieval.
[0023] It should be noted that, in addition to body temperature, heart rate and / or respiratory rate, physiological information can also include sweating, blood pressure, blood sugar, and weight (detected by an electronic scale) to analyze the body's condition after medication from multiple perspectives.
[0024] In addition, when using a pillbox to connect directly to a wearable device via Bluetooth, a Time Division Multiple Access (TDMA) protocol can be used to allocate communication time slots. ,in, For communication cycle, =200ms, N is the number of devices worn by the human body.
[0025] Step S12: The monitoring data and physiological information are compared with the preset medication dosage plan, and the medication dosage plan is adjusted according to the first comparison result to obtain the first plan, and the first comparison is repeated within a preset time period. In this embodiment, the user's medication situation is determined by the changes in the quantity of medicine in the medicine box and the physiological changes of the human body, and the medication plan is adjusted accordingly to achieve personalized dynamic adjustment of medication.
[0026] Specifically, it mainly includes the following steps: Step S121: Align the monitoring data and the physiological information according to the time series. Step S122: Perform a first aggregation operation on the alignment processing result to generate a medication feature vector, and construct a medication feature matrix based on the medication feature vector; Step S123: Perform a second aggregation operation on the preset medication dosage scheme to generate a medication scheme vector, and construct a medication scheme matrix based on the medication scheme vector.
[0027] Step S124: Perform a first comparison between the medication feature vector in the medication feature matrix and the medication plan vector in the medication plan matrix to determine the out-of-bounds value of the medication feature matrix; Step S125: Determine the total out-of-bounds value of the medication feature matrix based on the out-of-bounds value, and determine the risk level based on the total out-of-bounds value; Step S126: Adjust the medication dosage scheme according to the risk level and the medication feature vector to obtain the first scheme.
[0028] In this embodiment, time series alignment is used to establish a baseline for medication administration, ensuring that the user's medication status corresponds to their physiological condition. During time alignment, all data needs to be converted to a uniform time granularity (e.g., per minute / hour), and missing time points are filled using interpolation (linear / spline) or forward padding to maintain temporal continuity. Simultaneously, using medication administration time as an anchor point, the data is aligned to a fixed window before / after medication administration (e.g., from 1 hour before to 4 hours after administration, adjusted according to the specific medication). Subsequently, key features are extracted from the time series and aggregated to form a high-dimensional vector. Specifically, this may involve calculating the rate of change in drug weight, quantity, and storage location, as well as the rate of change in body temperature, heart rate, and respiratory rate, ultimately generating a medication feature vector. Where t represents the dimension of the corresponding medication box monitoring data and physiological data; similarly, the medication regimen is dose-coded (e.g., a dose of 1 every four hours, a change in drug weight of 1, a change in quantity of 1, a change in location of 0, a corresponding temperature change of 0.0055, a heart rate change rate of 0.07, a respiratory rate change rate of 0.15, etc.), and then the corresponding aggregation operation is performed to obtain the corresponding medication regimen vector. , This represents the dimension in the medication regimen. After obtaining the relevant vectors, the corresponding matrices are constructed. Generally, the preset regimen is set according to the conventional medication regimen. However, considering individual differences, it is necessary to compare the differences between individual medication situations and the actual regimen. Specifically, the difference between the actual regimen and the preset regimen is determined by calculating the out-of-bounds value after mapping. This method can intuitively determine the difference between the two regimens and improve the accuracy of the judgment.
[0029] Specific examples are as follows: Medicine box data sheet Timestamp Weight (g) Quantity (pieces) Storage location (grid number) 2024-07-14T10:00 100 20 1 2024-07-14T11:00 95 19 1 Physiological data table Timestamp Body temperature (°C) Heart rate (beats / minute) Respiratory rate (breaths / minute) 2024-07-14T10:00 36.5 72 16 2024-07-14T10:30 36.6 73 17 2024-07-14T11:00 36.7 74 18 Aggregate calculation: Rate of change in weight:
[0030] Rate of change in quantity:
[0031] Rate of change of position:
[0032] Body temperature change rate:
[0033] Heart rate variability:
[0034] Rate of change of respiratory rate:
[0035] Therefore, the drug use feature vector Similarly, medication regimen vector Then, the medication feature matrix T and the treatment plan matrix L are constructed respectively, and the difference between the two matrices is calculated; at this point, for each parameter: if > If the value exceeds the upper bound, it exceeds the lower bound; otherwise, it exceeds the lower bound. The total out-of-bounds value A is then calculated, and this value is used to determine the risk level of the user's physiological characteristics.
[0036] If A < 25, the risk is low and no adjustment is needed; if 25 < A < 40, the risk is medium and the dosage or frequency needs to be adjusted, such as increasing the dosage by 5 mg or shortening the dosing interval; if 40 < A, the risk is high and emergency intervention is needed, such as immediately locking the medicine box and notifying the doctor, and so on.
[0037] Step S13: The last monitoring data and physiological information obtained within the preset time period are compared with the preset medical treatment plan for the second time, and a medical treatment plan is generated based on the results of the second comparison. In this embodiment, by comparing monitoring data and physiological data with the medical treatment plan, the medical treatment plan is adaptively and dynamically adjusted according to individual differences, thereby promoting the user's recovery.
[0038] Specifically, it includes the following steps: Step S131: Perform a second comparison between the total value of the out-of-bounds data corresponding to the last obtained monitoring data and the physiological information and the risk threshold corresponding to the preset medical treatment plan; Step S132: When the second comparison result is within the first range, it is determined that the situation is abnormal, and the preset medical treatment plan is adjusted first. Step S133: When the second comparison result exceeds the first range, an emergency situation is determined, and the preset medical treatment plan is adjusted for the second time.
[0039] In this embodiment, the first range is generally considered abnormal (e.g., the aforementioned medium risk). The first adjustment at this time includes advancing the consultation time (e.g., 24 hours earlier), conducting muscle-building-specific examinations (e.g., blood drug concentration, liver and kidney function), generating medical records and medication data, etc. If it exceeds the first range, it is considered to be in an emergency state (e.g., the aforementioned high risk). At this time, the emergency procedure is immediately triggered (e.g., instructing the mobile communication device to make an emergency call and automatically broadcasting the pre-stored address and other information through the artificial intelligence voice system), immediately stopping the current medication plan, continuously monitoring physiological information (recording vital signs every 15 minutes), etc. At the same time, all adjustment plans need to be pushed through the Clinical Decision Support System (CDSS) and the doctor's confirmation time is recorded. Then, the total value of exceeding the limit is recalculated within 48 hours after the adjustment to verify the effectiveness of the intervention.
[0040] Step S14: Send the medical treatment plan to the target device and instruct the target device to display the medical treatment plan.
[0041] In this embodiment, after a new medical treatment plan is generated, a medical treatment reminder is generated in the calendar program of the target device (which may be a mobile terminal such as a mobile phone or tablet) and displayed to prompt the user to seek medical treatment.
[0042] By combining medication use with the individual's physiological condition through the above steps, multidimensional decision-making on treatment plans can be made, solving the problems of individual differences in medication use and the inability to effectively adjust treatment plans, and improving the accuracy and efficiency of adjusting individual medication and treatment plans.
[0043] Example 2 The difference from Example 1 is that the step of comparing the monitoring data and physiological information with a preset medication dosage plan, and adjusting the medication dosage plan according to the first comparison result to obtain a first plan includes: Step S127: Calculate the similarity between the medication feature matrix and the medication regimen matrix; Step S128: If the difference between the similarity and the preset threshold is greater than the third range, match the first medication plan that is closest to the medication plan matrix and use the first medication plan as the first plan.
[0044] In this embodiment, in addition to directly changing the medication regimen, a new medication regimen can also be obtained by regimen matching. Specifically, when the difference is greater than the third range, a candidate matrix with adjusted parameters relative to the medication feature matrix is first matched, and then the similarity between the candidate matrix and the medication regimen matrix is calculated, and this is used as the first regimen.
[0045] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0046] This embodiment also provides a personalized medication dosage adjustment and appointment reminder device based on a smart pillbox. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0047] Figure 2 This is a structural block diagram of a personalized medication dosage adjustment and appointment reminder device based on a smart pillbox according to an embodiment of the present invention. Figure 2 As shown, the device includes: The information acquisition module 21 is used to acquire monitoring data from the smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug in the smart pillbox; the physiological information includes at least body temperature, heart rate, and / or respiratory rate. The comparison and adjustment module 22 is used to make a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjust the medication dosage plan according to the first comparison result to obtain a first plan, and repeat the first comparison within a preset time period. The second comparison module 23 is used to compare the last monitoring data and the physiological information obtained within a preset time period with a preset medical treatment plan, and generate a medical treatment plan based on the second comparison result. The treatment plan display module 24 is used to send the treatment plan to the target device and instruct the target device to display the treatment plan.
[0048] In an optional embodiment, the apparatus further includes: The data alignment module is used to align the monitoring data and physiological information according to a time series before making a first comparison with the preset medication dosage plan; The first matrix module performs a first aggregation operation on the alignment results to generate a medication feature vector. Each unit exists physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0049] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for personalized medication dosage adjustment and appointment reminders based on a smart pillbox, characterized in that, include: The system acquires monitoring data from a smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug within the smart pillbox. The physiological information includes at least body temperature, heart rate, and / or respiratory rate. The monitoring data and physiological information are compared with a preset medication dosage plan. The medication dosage plan is adjusted according to the first comparison result to obtain a first plan. The first comparison is repeated within a preset time period. The monitoring data and physiological information obtained last within the preset time period are compared with the preset medical treatment plan, and a medical treatment plan is generated based on the results of the second comparison. The medical treatment plan is sent to the target device and the target device is instructed to display the medical treatment plan.
2. The method according to claim 1, characterized in that, Before performing a first comparison between the monitoring data and physiological information and a preset medication dosage regimen, the method further includes: The monitoring data and physiological information are aligned according to the time series. Perform a first aggregation operation on the alignment result to generate a medication feature vector, and construct a medication feature matrix based on the medication feature vector; A second aggregation operation is performed on the preset medication dosage scheme to generate a medication scheme vector, and a medication scheme matrix is constructed based on the medication scheme vector.
3. The method according to claim 2, characterized in that, The step of comparing the monitoring data and physiological information with a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: A first comparison is made between the medication feature vector in the medication feature matrix and the medication plan vector in the medication plan matrix to determine the out-of-bounds value of the medication feature matrix; The total out-of-bounds value of the medication feature matrix is determined based on the out-of-bounds value, and the risk level is determined based on the total out-of-bounds value; The medication dosage scheme is adjusted according to the risk level and the medication feature vector to obtain the first scheme.
4. The method according to claim 3, characterized in that, The step of performing a second comparison between the last monitoring data and the physiological information obtained within a preset time period and a preset medical treatment plan, and generating a medical treatment plan based on the second comparison result, includes: A second comparison is made between the total value of the out-of-bounds data corresponding to the last obtained monitoring data and physiological information and the risk threshold corresponding to the preset medical treatment plan; If the second comparison result is within the first range, it is determined to be in an abnormal state, and the preset medical treatment plan is adjusted accordingly. If the second comparison result exceeds the first range, an emergency situation is determined, and the preset medical treatment plan is adjusted a second time.
5. The method according to claim 2, characterized in that, The step of comparing the monitoring data and physiological information with a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: Calculate the similarity between the medication feature matrix and the medication regimen matrix; If the difference between the similarity and the preset threshold is greater than a third range, the first medication regimen that is closest to the medication regimen matrix is matched, and the first medication regimen is taken as the first regimen.
6. A personalized medication dosage adjustment and appointment reminder device based on a smart pillbox, characterized in that, include: An information acquisition module is used to acquire monitoring data from the smart pillbox and physiological information from a wearable device connected to the smart pillbox. The monitoring data includes the weight, quantity, and / or storage location of the target drug within the smart pillbox; the physiological information includes at least body temperature, heart rate, and / or respiratory rate. The comparison and adjustment module is used to make a first comparison between the monitoring data and physiological information and a preset medication dosage plan, and adjust the medication dosage plan according to the first comparison result to obtain a first plan, and repeat the first comparison within a preset time period. The second comparison module is used to compare the last monitoring data and the physiological information obtained within a preset time period with a preset medical treatment plan, and generate a medical treatment plan based on the second comparison result. The treatment plan display module is used to send the treatment plan to the target device and instruct the target device to display the treatment plan.
7. The apparatus according to claim 6, characterized in that, The device further includes: The data alignment module is used to align the monitoring data and physiological information according to a time series before making a first comparison with the preset medication dosage plan; The first matrix module is used to perform a first aggregation operation on the alignment processing result, generate a medication feature vector, and construct a medication feature matrix based on the medication feature vector; The second matrix module is used to perform a second aggregation operation on the preset medication dosage scheme to generate a medication scheme vector, and to construct a medication scheme matrix based on the medication scheme vector.
8. The apparatus according to claim 7, characterized in that, The step of comparing the monitoring data and physiological information with a preset medication dosage plan, and adjusting the medication dosage plan based on the first comparison result to obtain a first plan includes: A first comparison is made between the medication feature vector in the medication feature matrix and the medication plan vector in the medication plan matrix to determine the out-of-bounds value of the medication feature matrix; The total out-of-bounds value of the medication feature matrix is determined based on the out-of-bounds value, and the risk level is determined based on the total out-of-bounds value; The medication dosage scheme is adjusted according to the risk level and the medication feature vector to obtain the first scheme.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 5 when executed.
10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 5.