Intelligent medicine reminding method and system

Abstract

The application provides a smart medicine reminding method and system; wherein the method comprises: acquiring first data related to medicine reminding, determining a first medicine reminding scheme according to the first data; acquiring second data related to medicine reminding, correcting the first medicine reminding scheme according to the second data to obtain a second medicine reminding scheme; and outputting medicine prompting information according to the second medicine reminding scheme. The application determines an initial medicine reminding scheme according to the acquired first data related to medicine reminding, then acquires other data related to a medicine object, and corrects the initial medicine reminding scheme according to the other data, so that a more personalized medicine reminding scheme is obtained, and the medicine object can obtain better medicine treatment effect.

Description

Technical Field

[0001] This invention relates to the fields of health management and computer technology, and more specifically, to an intelligent medication reminder method, system, electronic device, and computer storage medium. Background Technology

[0002] With the advancement of technology, medication reminder devices have become more widely used. However, existing medication reminder devices generally provide reminders at pre-set fixed times. This method lacks consideration for the individual patient's condition, resulting in poor timed reminder effectiveness and failing to guide patients to take medication at the appropriate time, thus preventing patients from achieving the best treatment results. Summary of the Invention

[0003] In order to at least solve the technical problems existing in the background art, the present invention provides an intelligent medication reminder method, system, electronic device and computer storage medium.

[0004] A first aspect of the present invention provides an intelligent medication reminder method, comprising the following steps:

[0005] Obtain first data related to medication reminders, and determine a first medication reminder plan based on the first data;

[0006] Obtain second data related to medication reminders, and modify the first medication reminder plan based on the second data to obtain a second medication reminder plan;

[0007] Output medication reminder information according to the second medication reminder plan;

[0008] The second data is related to the patients receiving the medication.

[0009] Furthermore, prior to acquiring the first data related to medication reminders, the method also includes:

[0010] Determine whether drug insertion data has been obtained; if so, generate a trigger signal; wherein the trigger signal is used to trigger the acquisition of first data related to medication reminder.

[0011] Further, determining the first medication reminder plan based on the first data includes:

[0012] The first data is parsed to obtain drug attribute data and / or first medication time data;

[0013] The second medication time data is determined based on the drug property data;

[0014] The third medication time data is determined based on the first medication time data and / or the second medication time data;

[0015] The first medication reminder plan is determined based on the third medication time data.

[0016] Furthermore, determining the first medication reminder plan based on the first data further includes:

[0017] Obtain drug usage data, and determine the fourth medication time data based on the drug usage data;

[0018] The first medication reminder scheme is determined based on the third and fourth medication time data.

[0019] Furthermore, the step of modifying the first medication reminder plan based on the second data to obtain a second medication reminder plan includes:

[0020] Based on the second data, determine the motion data of the drug recipient, and evaluate the motion data to obtain a first correction coefficient;

[0021] The first medication reminder scheme is modified according to the first correction coefficient to obtain the second medication reminder scheme.

[0022] Further, the evaluation of the motion data to obtain correction coefficients includes:

[0023] A preset relationship is obtained by looking up the drug attribute data in a table.

[0024] The correction coefficient is determined based on the motion data and the preset relationship.

[0025] Furthermore, the step of modifying the first medication reminder scheme based on the second data to obtain the second medication reminder scheme further includes:

[0026] The medication distance data of the medication recipient is determined based on the second data, and a second correction coefficient is determined based on the movement data, the medication distance data, and the first medication reminder scheme.

[0027] The first medication reminder scheme is modified according to the second correction coefficient to obtain a second medication reminder scheme.

[0028] A second aspect of the present invention provides an intelligent medication reminder system, comprising an acquisition module, a processing module, and a storage module; the processing module is connected to the acquisition module and the storage module.

[0029] The storage module is used to store executable computer program code;

[0030] The acquisition module is used to acquire at least the first data and the second data related to the medication reminder, and transmit them to the processing module;

[0031] The processing module is configured to execute the method described in the preceding one by invoking the executable computer program code in the storage module.

[0032] A third aspect of the present invention provides an electronic device comprising: a memory storing executable program code; a processor coupled to the memory; the processor invoking the executable program code stored in the memory to perform the method as described in any of the preceding claims.

[0033] A fourth aspect of the present invention provides a computer storage medium storing a computer program that, when executed by a processor, performs the method described in any of the preceding claims.

[0034] The present invention first determines an initial medication reminder scheme based on the first data related to medication reminders, and then obtains other data related to the medication user to modify the initial medication reminder scheme, thereby obtaining a more personalized medication reminder scheme, so that the medication user can obtain better medication treatment effect. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating an intelligent medication reminder method disclosed in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the structure of an intelligent medication reminder system disclosed in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0040] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0041] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0042] It should be understood that although the terms first, second, third, etc., may be used to describe ... in the embodiments of this application, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of this application, first ... can also be referred to as second ..., and similarly, second ... can also be referred to as first ....

[0043] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0044] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0045] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] Please see Figure 1 , Figure 1 This is a flowchart illustrating an intelligent medication reminder method disclosed in an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a smart medication reminder method, which includes the following steps:

[0047] Obtain first data related to medication reminders, and determine a first medication reminder plan based on the first data;

[0048] Obtain second data related to medication reminders, and modify the first medication reminder plan based on the second data to obtain a second medication reminder plan;

[0049] Output medication reminder information according to the second medication reminder plan;

[0050] The second data is related to the patients receiving the medication.

[0051] In this embodiment of the invention, referring to the background art, existing medication reminder methods all operate on a timed reminder basis, lacking consideration for the actual situation of the medication user and failing to meet the user's need for optimal medication effect. To address this technical problem, this invention first determines an initial medication reminder scheme based on the acquired first data related to medication reminders, and then acquires other data related to the medication user, thereby revising the initial medication reminder scheme to obtain a more personalized medication reminder scheme, enabling the medication user to achieve better therapeutic effects.

[0052] The solution of this invention can be applied to various terminal devices, such as mobile phones, tablets, computers, alarm clocks, smart wearable devices, etc. These terminal devices can be pre-installed with a medication reminder APP; in addition, it can also be a dedicated medication reminder device, such as a smart pillbox, which is typically equipped with a processor, output device, memory, input device and communicator. The communicator can be used to obtain the aforementioned first data and second data, the processor can generate a medication reminder scheme based on a preset processing program, the memory stores the computer program necessary for medication reminders, and the input device is used to support the user to manually input relevant data.

[0053] Furthermore, prior to acquiring the first data related to medication reminders, the method also includes:

[0054] Determine whether drug insertion data has been obtained; if so, generate a trigger signal; wherein the trigger signal is used to trigger the acquisition of first data related to medication reminder.

[0055] In this embodiment of the invention, when a user uses a smart pillbox with a medication reminder function, the smart pillbox can detect medication insertion data, such as pillbox opening data and empty pill compartment occupancy data, and can trigger a medication reminder scheme for the newly inserted medication. Depending on the type of medication reminder terminal device, the trigger signal can be generated by the smart pillbox or by the aforementioned various terminal devices. In this case, the smart pillbox needs to send the relevant medication insertion data to the terminal device.

[0056] It should be noted that, in the solution of the present invention, the first data related to medication reminders can be published by the prescribing party (doctor or pharmacist) to a designated server. That is, after the doctor or pharmacist designs a medication plan for the patient, he or she will publish the medication plan data to the corresponding server. At this time, the terminal device can request the medication plan data from the designated server based on the trigger signal.

[0057] Further, determining the first medication reminder plan based on the first data includes:

[0058] The first data is parsed to obtain drug attribute data and / or first medication time data;

[0059] The second medication time data is determined based on the drug property data;

[0060] The third medication time data is determined based on the first medication time data and / or the second medication time data;

[0061] The first medication reminder plan is determined based on the third medication time data.

[0062] In this embodiment of the invention, the medication plan data published by the doctor or pharmacist to the designated server generally includes drug attribute data and first medication time data. The first medication time data refers to the medication time recommended by the doctor or pharmacist. Simultaneously, the corresponding drug instructions can be retrieved based on the drug attribute data to extract the manufacturer's recommended medication time, especially when the first medication time data is not included in the medication plan data. Furthermore, when both the first and second medication time data are available, they can be optimized or merged according to preset rules to obtain the most suitable third medication time data, thereby generating an initial first medication reminder plan.

[0063] Furthermore, determining the first medication reminder plan based on the first data further includes:

[0064] Obtain drug usage data, and determine the fourth medication time data based on the drug usage data;

[0065] The first medication reminder scheme is determined based on the third and fourth medication time data.

[0066] In this embodiment of the invention, in reality, the patient may not strictly adhere to the recommended medication timing, and there may be significant time delays due to various factors. To address this, the invention further acquires and analyzes medication usage data detected by the smart pillbox (e.g., pillbox opening data, weight reduction data of a designated pill compartment, etc.) to obtain the patient's usage signal for the designated drug (this signal can be for initial medication or intermediate medication). Using this signal as a baseline, supplemented by the aforementioned third medication time data, a first medication reminder scheme can be determined (or redefined). This setup allows the patient to maintain the optimal medication interval as much as possible, thereby ensuring the duration of time the drug concentration in the body remains within the effective range, resulting in better therapeutic effects.

[0067] In this embodiment, the third medication time data may refer to the medication time interval, while the fourth medication time data may refer to the most recent medication time.

[0068] Furthermore, the step of modifying the first medication reminder plan based on the second data to obtain a second medication reminder plan includes:

[0069] Based on the second data, determine the motion data of the drug recipient, and evaluate the motion data to obtain a first correction coefficient;

[0070] The first medication reminder scheme is modified according to the first correction coefficient to obtain the second medication reminder scheme.

[0071] In this embodiment of the invention, exercise can significantly affect the concentration of certain drugs in the blood and the absorption efficiency of others. For example, if exercise occurs immediately after taking a certain drug, a large amount of blood will flow to the limbs and other moving parts, reducing blood flow to the gastrointestinal tract. This results in less blood delivering the drug, slower or incomplete absorption, and consequently, poor drug efficacy. Therefore, this invention considers the exercise status of the drug user simultaneously. Specifically, the exercise data of the drug user is determined and evaluated based on second data (to determine the exercise intensity), and then an appropriate correction coefficient is calculated. Based on this, the first medication reminder scheme can be modified, for example, by appropriately advancing or delaying the medication reminder time.

[0072] The second data is mainly used to reflect the exercise intensity of the drug user. It can be obtained through location data analysis or directly based on various wearable devices.

[0073] Further, the evaluation of the motion data to obtain correction coefficients includes:

[0074] A preset relationship is obtained by looking up the drug attribute data in a table.

[0075] The correction coefficient is determined based on the motion data and the preset relationship.

[0076] In this embodiment of the invention, different drugs are affected by exercise to varying degrees. For example, drug A is largely unaffected by exercise; drug B is absorbed quickly through the gastrointestinal tract, and exercise can lead to excessive excretion of the drug, thus prematurely lowering the drug concentration in the blood; drug C is absorbed slowly through the gastrointestinal tract, and exercise can accelerate its entry into the bloodstream, causing a premature increase in blood drug concentration, but without significantly affecting the excretion rate of drug C. Therefore, this invention establishes a preset relationship lookup table for different drugs. By looking up the table, the correspondence between the exercise intensity (obtained through evaluation of exercise data) and the correction coefficient for a particular drug can be quickly determined, thereby accurately determining the correction coefficient to adjust the first medication reminder plan. Obviously, the correction coefficient can be positive, zero, or negative, and both positive and negative values ​​have corresponding magnitudes, which also correspond to the exercise state.

[0077] Furthermore, the step of modifying the first medication reminder scheme based on the second data to obtain the second medication reminder scheme further includes:

[0078] The medication distance data of the medication recipient is determined based on the second data, and a second correction coefficient is determined based on the movement data, the medication distance data, and the first medication reminder scheme.

[0079] The first medication reminder scheme is modified according to the second correction coefficient to obtain a second medication reminder scheme.

[0080] In this embodiment of the invention, the medication or smart pillbox may not be readily available to the user. If the previously determined medication reminder scheme is followed, the user may not be able to take the medication in time, even if they receive the reminder, or may forget to take it due to the long passage of time since the reminder. To address this, the invention further determines the distance between the user and the medication / smart pillbox based on second data, and then analyzes the user's motion data (location, speed, direction, route, etc.) to determine the approximate time interval for their arrival at the medication or smart pillbox. By comparing this with the first medication reminder scheme, a reasonable second correction coefficient can be determined to decide how to make a second correction to the first medication reminder scheme.

[0081] Specifically, if it is predicted that the medication recipient can arrive within or ahead of the designated medication period of the first medication reminder plan, the second correction coefficient can be set to 1, meaning that the first medication reminder plan will not be modified. Conversely, the second correction coefficient can be set according to the degree of delay in arrival, so as to achieve medication reminders before or after the predicted arrival time of the medication recipient. This can both remind the medication recipient to take the medication in a timely manner and avoid reminding them too early, which could lead to a higher probability of forgetting to take the medication.

[0082] The object of the second modification in this embodiment can be the initial first medication reminder scheme or the first medication reminder scheme after the first modification, and there is no specific limitation.

[0083] An improved version of this embodiment is also provided, as follows:

[0084] The motion data is analyzed to obtain a stress coefficient, and the degree of matching between the stress coefficient and the drug attribute data is calculated.

[0085] If the matching degree is greater than a preset threshold, then medication reminder information is output according to the second medication reminder scheme; wherein, the second medication reminder scheme includes a first reminder time;

[0086] Otherwise, the first medication reminder scheme, which has been modified in the second modification, is modified in the third modification to obtain a new second medication reminder scheme, and medication reminder information is output according to the new second medication reminder scheme; wherein, the second medication reminder scheme includes at least a second reminder time, and the second reminder time is earlier than the first reminder time.

[0087] In this improved implementation plan, medications can be roughly divided into two categories: emergency (e.g., cardiovascular drugs like nitroglycerin) and routine (e.g., routine anti-inflammatory drugs). By analyzing the matching degree between the urgency coefficient and the medication attribute data, the psychological urgency of the user regarding the medication to be reused can be determined, thereby enabling the development of a more reasonable medication reminder plan. An example is given below:

[0088] Analysis of the motion data revealed that the patient frequently exhibited behaviors such as speeding and failing to slow down on curves / intersections, initially indicating a high degree of urgency for medication use. Further analysis of the medication attribute data stored in the smart pillbox was conducted to determine if it was an emergency medication, thus verifying the urgency. If so, it indicates a strong correlation between the patient's abnormal motion behavior and the urgency of medication use; otherwise, it suggests simply abnormal motion (e.g., inherent aggressive driving habits).

[0089] For the former situation, the second medication reminder plan, determined as described above, can be implemented after the user has arrived at the location of the medication (or is within a certain distance). This reminder method only reminds the user upon arrival, rather than during the journey when the user is in a state of psychological urgency to retrieve the medication, thus avoiding potential traffic hazards.

[0090] In the latter case, the second medication reminder plan determined by the aforementioned method can be further revised, i.e., a third revision, to implement the reminder at an earlier time before the medication recipient arrives at the location (or is within a certain range) of the medication. Furthermore, the new second medication reminder plan obtained after the third revision can simultaneously include, for example, a first reminder time and a second reminder time, that is, reminding the medication recipient, who is not psychologically anxious, both during the journey to pick up the medication and upon arrival, thereby reducing the probability of them forgetting to take their medication.

[0091] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an intelligent medication reminder system disclosed in an embodiment of the present invention. Figure 2 As shown, an intelligent medication reminder system according to an embodiment of the present invention includes an acquisition module (101), a processing module (102), and a storage module (103); the processing module (102) is connected to the acquisition module (101) and the storage module (103);

[0092] The storage module (103) is used to store executable computer program code;

[0093] The acquisition module (101) is used to acquire at least the first data and the second data related to the medication reminder, and transmit them to the processing module (102);

[0094] The processing module (102) is configured to execute the method described in the preceding one by invoking the executable computer program code in the storage module (103).

[0095] The specific functions of the intelligent medication reminder system in this embodiment are the same as those in the above embodiments. Since the system in this embodiment adopts all the technical solutions of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be described in detail here.

[0096] Please see Figure 3 , Figure 3 This invention discloses an electronic device comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the method described in the foregoing embodiments.

[0097] This invention also discloses a computer storage medium storing a computer program, which is executed by a processor to perform the methods described in the foregoing embodiments.

[0098] The apparatus / system according to embodiments of this disclosure may include a processor, memory for storing and executing program data, permanent memory such as a disk drive, a communication port for processing communication with external devices, and a user interface device, etc. The method is implemented as a software module or may be stored on a computer-readable recording medium as computer-readable code or program instructions executable by a processor. Examples of computer-readable recording media may include magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disk, hard disk, etc.), optical reading media (e.g., CD-ROM, DVD, etc.). The computer-readable recording medium may be distributed across computer systems connected to a network, and the computer-readable code may be stored and executed in a distributed manner. The medium may be computer-readable, stored in memory, and executed by a processor.

[0099] Embodiments of this disclosure can be designated as functional block components and various processing operations. Functional blocks can be implemented as various numbers of hardware and / or software components that perform specific functions. For example, embodiments of this disclosure can implement direct circuit components, such as memories, processing circuits, logic circuits, lookup tables, etc., that can perform various functions under the control of one or more microprocessors or other control devices. Components of this disclosure can be implemented by software programming or software components. Similarly, embodiments of this disclosure can include various algorithms implemented by combinations of data structures, procedures, routines, or other programming components, and can be implemented by programming or scripting languages ​​(such as C, C++, Java, assembler, etc.). Functional aspects can be implemented by algorithms executed by one or more processors. Furthermore, embodiments of this disclosure can implement related techniques for electronic environment setup, signal processing, and / or data processing. Terms such as “mechanism,” “element,” “unit,” etc., can be used broadly and are not limited to mechanical and physical components. These terms can refer to a series of software routines associated with processors, etc.

[0100] Specific embodiments are described in this disclosure as examples, and the scope of the embodiments is not limited thereto.

[0101] While embodiments of this disclosure have been described, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of this disclosure as defined by the appended claims. Therefore, the above embodiments of this disclosure should be interpreted as exemplary and are not limiting in any way. For example, each component described as a single unit may be executed in a distributed manner, and similarly, components described as distributed may be executed in a combined manner.

[0102] All examples or example terms (e.g., etc.) used in the embodiments of this disclosure are for the purpose of describing embodiments of this disclosure and are not intended to limit the scope of embodiments of this disclosure.

[0103] Furthermore, unless otherwise explicitly stated, expressions such as “necessary” or “important” associated with certain components do not necessarily indicate that the components are absolutely necessary.

[0104] Those skilled in the art will understand that embodiments of this disclosure may be implemented in modified forms without departing from the spirit and scope of this disclosure.

[0105] Because this disclosure allows for various changes to the embodiments thereof, it is not limited to the specific embodiments described herein, and it will be understood that all changes, equivalents, and alternatives that do not depart from the spirit and scope of this disclosure are included herein. Therefore, the embodiments of this disclosure described herein should be understood as illustrative in all respects and should not be construed as limiting.

[0106] Furthermore, terms such as "unit" and "module" refer to a unit that can be implemented as hardware or software or a combination of hardware and software to process at least one function or operation. "Unit" and "module" can be stored in a storage medium to be addressed and can be implemented as a program that can be executed by a processor. For example, "unit" and "module" can refer to components such as software components, object-oriented software components, class components, and task components, and can include processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables.

[0107] In this disclosure, the statement "A may include one of a1, a2, and a3" can broadly indicate that examples that can be included in element A include a1, a2, or a3. This statement should not be construed as limiting the meaning of examples included in element A to a1, a2, and a3. Therefore, as examples included in element A, elements other than a1, a2, and a3 should not be interpreted as excluding elements. Furthermore, this statement indicates that element A may include a1, a2, or a3. This statement does not imply that the elements included in element A must be selected from a specific set of features. That is, this statement should not be construed restrictively as indicating that a1, a2, or a3 must be selected from a set that includes a1, a2, and a3 to be included in element A.

[0108] Furthermore, in this disclosure, the expression "at least one of a1, a2 and / or a3" means one of "a1", "a2", "a3", "a1 and a2", "a1 and a3", "a2 and a3", and "a1, a2 and a3". Therefore, it should be noted that unless explicitly described as "at least one of a1, at least one of a2, and at least one of a3", the expression "at least one of a1, a2 and / or a3" should not be interpreted as "at least one of a1", "at least one of a2", and "at least one of a3".

Claims

1. A smart medication reminder method, characterized in that, Includes the following steps: Obtain first data related to medication reminders, and determine a first medication reminder plan based on the first data; the first data is the medication plan data formulated by the drug prescribing agent; Obtain second data related to medication reminders, and modify the first medication reminder plan based on the second data to obtain a second medication reminder plan; Output medication reminder information according to the second medication reminder plan; The second data is related to the patients receiving the medication; The step of modifying the first medication reminder plan based on the second data to obtain a second medication reminder plan includes: The exercise data of the drug user is determined based on the second data, the exercise data is evaluated to obtain the exercise intensity, a preset relationship is obtained by looking up the drug attribute data, and a first correction coefficient is determined based on the exercise intensity and the preset relationship. The first medication reminder scheme is modified according to the first correction coefficient, that is, the medication reminder time is appropriately advanced or delayed, to obtain the second medication reminder scheme; The step of modifying the first medication reminder plan based on the second data to obtain a second medication reminder plan further includes: Based on the second data, the medication distance data of the medication recipient, i.e., the distance between the medication recipient and the medication / smart pillbox, is determined. Based on the movement data and the medication distance data, it is predicted that the medication recipient will arrive at or ahead of the designated medication period of the first medication reminder scheme. If so, the second correction coefficient is set to 1, that is, no second correction is made to the first medication reminder scheme. Otherwise, the second correction coefficient is set according to the degree of delayed arrival time, so as to provide medication reminders before or after the predicted arrival time of the medication recipient. The first medication reminder scheme is modified according to the second correction coefficient to obtain a second medication reminder scheme.

2. The intelligent medication reminder method according to claim 1, characterized in that: Prior to acquiring the first data related to medication reminders, the method further includes: Determine whether drug insertion data has been obtained; if so, generate a trigger signal; wherein the trigger signal is used to trigger the acquisition of first data related to medication reminder.

3. The intelligent medication reminder method according to claim 1, characterized in that: The step of determining the first medication reminder plan based on the first data includes: The first data is parsed to obtain drug attribute data and / or first medication time data; The second medication time data is determined based on the drug property data; The third medication time data is determined based on the first medication time data and / or the second medication time data; The first medication reminder plan is determined based on the third medication time data.

4. The intelligent medication reminder method according to claim 3, characterized in that: The step of determining the first medication reminder plan based on the first data further includes: Obtain drug usage data, and determine the fourth medication time data based on the drug usage data; The first medication reminder scheme is determined based on the third and fourth medication time data.

5. An intelligent medication reminder system, comprising an acquisition module, a processing module, and a storage module; the processing module is connected to the acquisition module and the storage module; The storage module is used to store executable computer program code; The acquisition module is used to acquire at least the first data and the second data related to the medication reminder, and transmit them to the processing module; Its features are: The processing module is configured to execute the method as described in any one of claims 1-4 by calling the executable computer program code in the storage module.

6. An electronic device, comprising: Memory containing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to perform the method as described in any one of claims 1-4.

7. A computer storage medium storing a computer program, characterized in that: The computer program is executed by the processor to perform the method as described in any one of claims 1-4.

Images