Intelligent auxiliary medication reminder method, system, device and medium

Abstract

The present application relates to the technical field of medical equipment and smart alarm clocks, and discloses a smart auxiliary medication reminder method, system, device and medium, wherein the method includes: obtaining a target scheme for a target drug object; determining whether the current time is within the reminder time period of the target scheme, and obtaining a first result; if the first result is yes, obtaining the detection data of the target space corresponding to the target drug object, and determining whether a human body exists according to the detection data, and obtaining a second result; if the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; if the second result is no, performing a medication reminder according to a preset second multi-level reminder strategy. This effectively solves the problem in the prior art that only ringing reminders based on the set time often cannot ensure that the information is accurately transmitted to the user in actual applications, and greatly improves the effectiveness of medication reminders.

Description

Technical Field

[0001] The present application relates to the technical field of medical equipment and smart alarm clocks, and in particular to a smart auxiliary medication reminder method, system, device and medium. Background Art

[0002] In modern society, with the improvement of people's health awareness, the demand for auxiliary medication reminders is growing. However, the current auxiliary medication reminder method has serious shortcomings. It only rings a reminder according to the set time, and the function is too simple and mechanical. This simple auxiliary medication reminder method often cannot ensure that the information is accurately transmitted to the user in actual application. For example, the user may miss taking the medicine because he did not hear the reminder sound, was busy at the time, or the device reminder volume was too low. This not only affects the user's health management, but may also lead to serious consequences in the treatment of some special diseases. In view of these defects of the existing auxiliary medication reminder method, there is an urgent need for a solution that can more effectively convey medication reminder information and reduce medication omissions. Summary of the invention

[0003] Based on this, it is necessary to address the technical problem that the existing technology only rings reminders according to the set time, but in actual applications it is often unable to ensure that the information is accurately transmitted to the user. An intelligent auxiliary medication reminder method, system, device and medium are proposed.

[0004] In a first aspect, a smart auxiliary medication reminder method is provided, the method comprising: Obtain target regimen for target drug object; Determine whether the current time is within the reminder time period of the target plan to obtain a first result; If the first result is yes, acquiring detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result; If the second result is yes, then providing a medication reminder according to the preset first multi-level reminder strategy; If the second result is no, a medication reminder is performed according to a preset second multi-level reminder strategy.

[0005] Furthermore, the step of acquiring detection data of the target space corresponding to the target drug object, and determining whether a human body exists according to the detection data to obtain a second result includes: If the medication type of the target drug object is compulsory medication, a medication reminder is performed according to a preset compulsory medication reminder strategy; If the medication type of the target drug object is non-compulsory medication, the detection data of the target space corresponding to the target drug object is obtained, and whether a human body exists is determined according to the detection data to obtain the second result.

[0006] Further, if the second result is yes, the step of performing a medication reminder according to a preset first multi-level reminder strategy, and if the second result is no, the step of performing a medication reminder according to a preset second multi-level reminder strategy, include: If the second result is yes, a medication reminder based on voice broadcast is performed according to the device configuration of the first multi-level reminder strategy; if the first confirmation information corresponding to the first result is obtained, the target scheme is updated according to the first confirmation information; if the first confirmation information is not obtained within a first time period after the generation time of the second result, a medication reminder based on the client is performed according to the client configuration of the first multi-level reminder strategy; if the second confirmation information corresponding to the first result is obtained, the target scheme is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the non-medication data; if the third confirmation information corresponding to the first result is obtained, the target scheme is updated according to the third confirmation information; If the second result is no, a client-based medication reminder is performed according to the client configuration of the first multi-level reminder strategy; if second confirmation information corresponding to the first result is obtained, the target plan is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the data of not taking medicine; if third confirmation information corresponding to the first result is obtained, the target plan is updated according to the third confirmation information.

[0007] Furthermore, the step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: Performing a medication reminder based on voice broadcast according to the device configuration of the first multi-level reminder strategy, and obtaining the first sound data corresponding to the target space within a preset time period; Extracting sound features according to the first sound data as target features; Determine whether there is a sound feature similar to the target feature in the feature library corresponding to the target solution, to obtain a third result; If the third result is yes, determining the first confirmation information according to the first sound data; If the third result is no, the processing of the first sound data is terminated.

[0008] Further, the step of determining the first confirmation information according to the first sound data includes: Based on the artificial intelligence model, perform intent recognition according to the first sound data to obtain a recognition result; If the conclusion of the recognition result is to confirm taking the medicine, generating the first confirmation information according to the recognition result; If the conclusion of the recognition result is that the medication is not confirmed, a follow-up voice broadcast is performed according to the preset question voice. After the follow-up voice broadcast, second sound data is obtained, and the second sound data is used as the first sound data. The step of jumping to the step of extracting sound features based on the first sound data and continuing to execute as target features is performed.

[0009] Further, the step of updating the target solution according to the first confirmation information includes: updating the future time period in the target regimen according to the first confirmation information and the medication time interval of the target drug object; The step of updating the target solution according to the second confirmation information comprises: updating the future time period in the target plan according to the information generation time of the second confirmation information and the medication time interval of the target drug object; The future time period is the reminder time period after the reminder time period corresponding to the first result in the target solution.

[0010] Furthermore, the method further comprises: If first confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the first confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the first confirmation information; If second confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the second confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the second confirmation information; If third confirmation information corresponding to the first result is obtained, the medication contraindication reminder configuration is updated according to the third confirmation information, a medication contraindication reminder is performed according to the medication contraindication reminder configuration, and the electronic medication record is updated according to the third confirmation information.

[0011] Furthermore, the step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: According to the device configuration of the first multi-level reminder strategy, a voice-broadcast-based medication reminder is performed, and according to the smart home linkage configuration of the first multi-level reminder strategy, the smart home device is controlled, wherein the smart home linkage configuration includes: one or more of a TV control configuration and a lighting control configuration.

[0012] Furthermore, before the step of obtaining the target regimen of the target drug object, the method further includes: Obtaining a regimen update signal of the target drug object; In response to the regimen update signal, acquiring historical medication data and medication standard data of the target drug object; The target regimen for the target drug object is generated based on the historical medication data and the medication standard data.

[0013] Furthermore, the step of determining whether a human body exists according to the detection data to obtain a second result includes: Determine whether there is a human body according to the infrared data in the detection data, and obtain an infrared determination result; Determine whether there is a human body according to the millimeter wave data in the detection data, and obtain a millimeter wave determination result; If both the infrared judgment result and the millimeter wave judgment result are yes, determining that the second result is yes; If either the infrared judgment result or the millimeter wave judgment result is negative, acquiring the camera data in the target space; It is determined whether a human body exists according to the camera data to obtain the second result.

[0014] In a second aspect, a smart auxiliary medication reminder system is provided, the system comprising: a target device, a client and a server, the target device is communicatively connected with the client and the server, and the server is communicatively connected with the client; The target device is configured to implement the steps of the above-mentioned intelligent assisted medication reminder method.

[0015] In a third aspect, a target device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent assisted medication reminder method when executing the computer program.

[0016] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent assisted medication reminder method are implemented.

[0017] The intelligent auxiliary medication reminder method, system, device and medium of the present application, when it is determined that the current time is within the reminder time period, when it is determined that there is a human body based on the detection data of the target space, the medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that the information may not be fully conveyed due to a single reminder method; when it is determined that the current time is within the reminder time period, when it is determined that there is no human body based on the detection data of the target space, the second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that the ringing reminder is only based on the set time, and in actual applications it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users to better manage their health. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] in: Figure 1 This is an application environment diagram of an intelligent auxiliary medication reminder method in an embodiment; Figure 2 A flowchart of an intelligent auxiliary medication reminder method in one embodiment; Figure 3 A partial flow chart of an intelligent auxiliary medication reminder method in one embodiment; Figure 4 This is a partial flow chart of an intelligent assisted medication reminder method in one embodiment. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The intelligent auxiliary medication reminder method provided in the embodiment of the present application can be applied in the following aspects: Figure 1 In an application environment, the application environment includes: a target device 1, a client 2 and a server 3, the target device 1 is communicatively connected with the client 2 and the server 3, and the server 3 is communicatively connected with the client 2.

[0022] Optionally, the target device 1 is configured to implement the following method steps: obtaining a target regimen for a target drug object; determining whether the current time is within the reminder time period of the target regimen to obtain a first result; if the first result is yes, obtaining detection data of a target space corresponding to the target drug object, and determining whether a human body exists based on the detection data to obtain a second result; if the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; if the second result is no, performing a medication reminder according to a preset second multi-level reminder strategy.

[0023] Optionally, the client 2 is configured to implement the following method steps: obtaining a target regimen for a target drug object; determining whether the current time is within the reminder time period of the target regimen to obtain a first result; if the first result is yes, obtaining detection data of a target space corresponding to the target drug object, and determining whether a human body exists based on the detection data to obtain a second result; if the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; if the second result is no, performing a medication reminder according to a preset second multi-level reminder strategy.

[0024] The intelligent auxiliary medication reminder method of the present application, when it is determined that the current time is within the reminder time period, when it is determined that there is a human body based on the detection data of the target space, the medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that the information may not be fully conveyed due to a single reminder method; when it is determined that the current time is within the reminder time period, when it is determined that there is no human body based on the detection data of the target space, the second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that the ringing reminder is only based on the set time, and in actual applications, it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users to better manage their health.

[0025] The target device 1 may be a smart alarm clock, a smart medicine box, a mobile phone, a smart wearable device (eg, a smart watch, a smart bracelet, a smart watch, etc.).

[0026] The client 2 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server 3 may be implemented by an independent server or a server cluster consisting of multiple servers.

[0027] The present application is described in detail below through specific embodiments.

[0028] See also Figure 2 As shown, Figure 2 A flowchart of the intelligent auxiliary medication reminder method provided in the embodiment of the present application includes the following steps: S1: Obtain the target regimen for the target drug object; The target regimen for target medication subjects is a pre-set reminder medication regimen.

[0029] Optionally, the target scheme describes the associated data of the drug identification and the reminder time period.

[0030] Optionally, the target scheme describes the associated data of the reminder end mark, the drug identifier and the reminder time period. It is understandable that the initial value of the reminder end mark is empty. If the reminder end mark is the end identifier, it means that the reminder time period with the reminder end mark as the end identifier has completed the reminder.

[0031] Optionally, the target scheme describes associated data of a patient ID, a drug ID, and a reminder time period.

[0032] The target drug object includes: at least one drug. It is understandable that when the target drug object includes multiple drugs, the multiple drugs of the target drug object have the same administration requirements.

[0033] The target solution may be manually input by the user, or may be automatically generated by the program file implementing the present application according to the solution generation rules.

[0034] The reminder time period is a time range data.

[0035] The drug identification may be data such as the drug name, drug identification, etc. that uniquely identifies a drug object.

[0036] The patient identification may be data that uniquely identifies a patient, such as a patient name or a patient ID.

[0037] Specifically, the target solution of the target drug object can be obtained from a preset shared storage space, from a third-party application, or from the local storage space of a device implementing the present application.

[0038] S2: Determine whether the current time is within the reminder time period of the target plan, and obtain a first result; Optionally, a preset time interval is used to determine whether the current time is within any reminder time period of the target plan; if the current time is within any reminder time period of the target plan, this means that a medication reminder is required, and thus the first result is determined to be yes; if the current time is not within any reminder time period of the target plan, this means that a medication reminder is not required, and thus the first result is determined to be no.

[0039] Optionally, determine whether the current time is within any reminder time period of the target plan according to a preset time interval; if the current time is within any reminder time period of the target plan, this means that a medication reminder is required, and thus the first result is determined to be yes; if the current time is not within any reminder time period of the target plan whose reminder end mark is not an end mark, this means that a medication reminder is not required, and thus the first result is determined to be no.

[0040] S3: If the first result is yes, obtaining detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result; The target space is the space where the person (patient) taking the target drug object is located. It is understandable that there may be one or more target spaces corresponding to the target drug object.

[0041] The target space can be pre-set, or the space where the target device is located can be used as the target space.

[0042] Specifically, if the first result is yes, it means that a medication reminder is needed to obtain detection data obtained by sensors (for example, infrared sensors, millimeter wave sensors) and / or cameras to detect the target space corresponding to the target drug object; the sensor can be in the target device, or it can be set independently of the target device (in this case, data can be transmitted through the Internet of Things and the program files that implement this application); the camera can be in the target device, or it can be set independently of the target device (in this case, data can be transmitted through the Internet of Things and the program files that implement this application).

[0043] Based on a preset human body analysis method, it is determined whether a human body exists according to the detection data. If a human body exists, the second result is determined to be yes; if a human body does not exist, the second result is determined to be no.

[0044] The infrared sensor detects the human body mainly based on the thermal radiation signal emitted by the human body. At this time, the human body analysis method specifically includes: First, the infrared sensor will obtain the thermal signal data in the target space. The human body temperature is around 36-37℃, so detecting a thermal signal area with a certain area in this temperature range is an important feature. Next, the shape of the thermal signal area is analyzed. The human body outline is roughly elliptical or human-like. If the shape of the thermal signal area is similar, the possibility of the existence of a human body increases. At the same time, the human body is dynamic, and the thermal signal area will move with the movement of the human body. The dynamic changes of the thermal signal area are continuously monitored. If its temperature is stable in the human body temperature range and the movement trajectory conforms to the logic of human movement, such as no sudden jump movement or non-physical deformation movement, it can be judged that a human body exists.

[0045] When the millimeter wave sensor determines whether there is a human body, the human body analysis method specifically includes: mainly using its ability to detect the micro-motion and reflection characteristics of the object, the millimeter wave sensor detects the echo signal, and first analyzes the micro-motion therein. Since the human body has physiological activities such as breathing and heartbeat, it will produce tiny vibrations and movements. By extracting the characteristic parameters reflecting the micro-motion such as frequency shift and phase difference in the echo signal, and comparing them with the predetermined human body micro-motion characteristic model, if they are within a reasonable range, there may be a human body. At the same time, the millimeter wave sensor can image the contour and reflection characteristics of the object, match the obtained object contour image with the preset human body contour template, and determine whether the reflection coefficient is within the normal reflection coefficient range of the human body. If the contour matching degree is high and the reflection coefficient meets the requirements, it can be determined that there is a human body.

[0046] When judging whether there is a human body based on the data captured by the camera, the human body analysis method specifically includes: first, the captured image must be preprocessed, including adjusting the color, contrast, etc. to improve the image quality. Then, target detection algorithms can be used, such as convolutional neural networks (CNNs) based on deep learning, which can learn various characteristic patterns of the human body. Search for areas in the image that match the characteristics of the human body, such as the human body has specific visual features such as shape, proportion and posture, such as the shape of the head and limbs, and the relative position relationship between the various parts of the body. At the same time, it can also be judged in combination with the movement characteristics of the human body. If the object in the image shows a typical movement pattern similar to human walking, standing, bending, etc., the combined analysis results of these characteristics can determine whether there is a human body.

[0047] S4: If the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; Specifically, if the second result is yes, it means that there is a human body in the target space. First, the target space needs to be reminded to take medicine. According to the preset first multi-level reminder strategy, a multi-level reminder mechanism is used to implement the medication reminder.

[0048] Optionally, the first multi-level reminder strategy includes: voice broadcast reminder sub-strategy, application reminder sub-strategy and emergency contact reminder strategy. The voice broadcast reminder sub-strategy is a strategy for implementing medication reminders by voice broadcasting to the target space through the target device. The application reminder sub-strategy is a strategy for implementing medication reminders by using one or more reminder methods such as text message push, vibration, and voice broadcast on the client. The emergency contact reminder strategy is a strategy for implementing medication reminders by contacting emergency contacts through text messages or phone calls.

[0049] S5: If the second result is no, a medication reminder is provided according to a preset second multi-level reminder strategy.

[0050] Specifically, if the second result is no, it means that there is no human body in the target space, and there is no need to remind the target space to take medicine. Therefore, according to the preset second multi-level reminder strategy, a multi-level reminder mechanism is used to remind the target space to take medicine.

[0051] It can be understood that the first multi-level reminder strategy and the second multi-level reminder strategy may be partially the same or completely different.

[0052] Optionally, the second multi-level reminder policy includes: an application reminder sub-policy and an emergency contact reminder policy.

[0053] The specific implementation methods of the application reminder sub-policies of step S51 and S41 may be the same or different. The specific implementation methods of the emergency contact reminder policies of step S51 and S41 may be the same or different.

[0054] In this embodiment, when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body exists, a medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that a single reminder method may cause the information to not be fully conveyed; when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body does not exist, a second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that only ringing reminders are performed based on the set time, and in actual applications, it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users better manage their health.

[0055] In one embodiment, the step of acquiring detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result includes: S311: If the medication type of the target drug object is compulsory medication, a medication reminder is provided according to a preset compulsory medication reminder strategy; Some medicines must be taken on time, otherwise they will cause serious consequences. For example, antihypertensive drugs, blood pressure will fluctuate throughout the day, taking antihypertensive drugs on time helps stabilize blood pressure within the normal range, if not taken on time, blood pressure may suddenly rise, which will put great pressure on the cardiovascular system, long-term irregular medication may lead to long-term instability of blood pressure, and then increase the risk of serious complications such as heart attack, stroke, kidney disease, etc. Therefore, when the target drug object is antihypertensive drugs, the medication type of the target drug object is compulsory medication.

[0056] In the case where the target drug object is not required to take the drug on time, the medication type of the target drug object is non-mandatory medication.

[0057] The medication type of the target drug object may be input by a user, or may be determined by a program file implementing the present application by querying a compulsory medication database based on the target drug object.

[0058] Specifically, if the medication type of the target drug object is compulsory medication, which means that the medication must be taken within the reminder time period, a medication reminder will be performed according to the preset compulsory medication reminder strategy to distinguish it from non-compulsory medication, thereby ensuring that the target drug object who needs to take the medication compulsorily can take it on time.

[0059] The mandatory medication reminder strategy refers to a series of reminder rules and methods for target drugs that must be taken strictly according to the prescribed time and dosage (i.e., drugs with mandatory medication type). Its purpose is to ensure that patients do not miss medication and to ensure the effectiveness and safety of drug treatment. In this strategy, the precise medication time will be preset according to the characteristics of the drug. For example, some drugs for treating heart disease need to be taken every 8 hours, and the system will remind at the corresponding time point. At the same time, multiple reminder methods are used, such as popping up a striking notification on the mobile phone or smart device, and issuing a sound reminder, which continues to ring like an alarm clock until the patient confirms that the reminder has been received. For example, for patients who need to take anticoagulant drugs for a long time to prevent thrombosis, since such drugs may cause serious health risks if missed, the mandatory medication reminder strategy will flash and vibrate the smart medicine box at the two prescribed medication times of 9 am and 9 pm every day, and the mobile phone will also receive voice and text reminders containing detailed information such as the drug name, dosage and precautions for taking. If the patient does not respond within a few minutes, the reminder will be repeated many times, and the patient's emergency contact may be contacted to urge the patient to take the medicine.

[0060] S312: If the medication type of the target drug object is non-compulsory medication, the detection data of the target space corresponding to the target drug object is obtained, and whether a human body exists is determined according to the detection data to obtain the second result.

[0061] Specifically, if the medication type of the target drug object is non-compulsory medication, it means that delaying medication or not taking medication will not have a significant impact. Therefore, the detection data of the target space corresponding to the target drug object is obtained, and whether there is a human body is determined based on the detection data to obtain the second result.

[0062] For drug objects of the mandatory medication type, this embodiment provides reminders based on the preset mandatory medication reminder strategy, which can ensure that patients do not miss medications and improve their medication compliance, thereby ensuring the effectiveness of drug treatment, which is crucial for drugs that must be taken on time to exert their efficacy and control the progression of the disease. For drug objects of the non-mandatory medication type, by obtaining the detection data of the target space to determine whether there is a human body and then obtaining the corresponding results, the subsequent actions can be intelligently determined based on whether there is a person in the target space, avoiding unnecessary reminders and interruptions.

[0063] In one embodiment, if the second result is yes, the step of performing a medication reminder according to a preset first multi-level reminder strategy, and if the second result is no, the step of performing a medication reminder according to a preset second multi-level reminder strategy, include: S41: If the second result is yes, then according to the device configuration of the first multi-level reminder strategy, a medication reminder based on voice broadcast is performed; if the first confirmation information corresponding to the first result is obtained, the target scheme is updated according to the first confirmation information; if the first confirmation information is not obtained within a first time period after the generation time of the second result, then according to the client configuration of the first multi-level reminder strategy, a medication reminder based on the client is performed; if the second confirmation information corresponding to the first result is obtained, the target scheme is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, then according to the emergency configuration of the first multi-level reminder strategy, a medication reminder is performed, and the reminder time period and the target drug object corresponding to the first result are added to the data of not taking medicine; if the third confirmation information corresponding to the first result is obtained, the target scheme is updated according to the third confirmation information; The voice broadcast reminder sub-strategy includes: performing a medication reminder based on voice broadcast according to the device configuration of the first multi-level reminder strategy, and if first confirmation information corresponding to the first result is obtained, updating the target plan according to the first confirmation information.

[0064] Applying the reminder sub-strategy includes: if the first confirmation information is not obtained within a first time period after the generation time of the second result, performing a client-based medication reminder according to the client configuration of the first multi-level reminder strategy; if the second confirmation information corresponding to the first result is obtained, updating the target plan according to the second confirmation information.

[0065] The emergency contact reminder strategy includes: if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the untaken medication data; if the third confirmation information corresponding to the first result is obtained, the target plan is updated according to the third confirmation information.

[0066] Specifically, if the second result is yes, it means that there is a human body in the target space, and a voice broadcast reminder is required for the target space. According to the device configuration of the first multi-level reminder strategy, the target device corresponding to the device configuration is controlled to play voice to perform a voice-based medication reminder. If the first confirmation information corresponding to the first result is obtained within the first time period after the generation time of the second result, it means that the target drug object has taken it after the voice broadcast reminder.

[0067] The updating of the target plan according to the first confirmation information is specifically as follows: setting the reminder end mark of the reminder time period corresponding to the first confirmation information in the target plan (that is, the reminder time period corresponding to the second result) as the end mark, and adjusting the reminder time period in the target plan whose reminder end mark is not the end mark according to the generation time of the first confirmation information.

[0068] The updating of the target plan according to the second confirmation information is specifically: setting the reminder end mark of the reminder time period corresponding to the second confirmation information in the target plan (that is, the reminder time period corresponding to the second result) as the end mark, and adjusting the reminder time period in the target plan whose reminder end mark is not the end mark according to the generation time of the second confirmation information.

[0069] The updating of the target plan according to the third confirmation information is specifically: setting the reminder end mark of the reminder time period corresponding to the third confirmation information in the target plan (that is, the reminder time period corresponding to the second result) as the end mark, and adjusting the reminder time period in the target plan whose reminder end mark is not the end mark according to the generation time of the third confirmation information.

[0070] S51: If the second result is no, a client-based medication reminder is performed according to the client configuration of the first multi-level reminder strategy; if second confirmation information corresponding to the first result is obtained, the target plan is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the data of not taking medicine; if third confirmation information corresponding to the first result is obtained, the target plan is updated according to the third confirmation information.

[0071] Specifically, if the second result is no, it means that there is no human body in the target space, and there is no need to perform voice broadcast reminder for the target space. Therefore, only the reminder sub-strategy and the emergency contact reminder strategy need to be applied.

[0072] In this embodiment, the specific implementation methods of the application reminder sub-policies of step S51 and S41 are the same, and the specific implementation methods of the emergency contact reminder policies of step S51 and S41 are the same.

[0073] In this embodiment, when the second result is yes, the voice broadcast reminder sub-strategy, application reminder sub-strategy and emergency contact reminder strategy are used in sequence to provide medication reminders; when the second result is no, the application reminder sub-strategy and emergency contact reminder strategy are used in sequence to provide medication reminders. The multi-level reminder strategy can convey medication information to users in multiple ways, avoiding the problem of information not being conveyed properly due to a single reminder method, and at the same time, data on non-dosage of medication is recorded for subsequent management and adjustment.

[0074] In one embodiment, the step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: S4111: Performing a medication reminder based on voice broadcast according to the device configuration of the first multi-level reminder strategy, and obtaining first sound data corresponding to the target space within a preset time period; Specifically, according to the device configuration of the first multi-level reminder strategy, a voice-based medication reminder is performed, and the sound data detected by the sound sensor on the target space is obtained within a preset time period, and the sound data is used as the first sound data corresponding to the target space. The sound sensor can be in the target device or can be set independently of the target device (in this case, data can be transmitted through the Internet of Things and the program file that implements this application); S4112: Extracting sound features according to the first sound data as target features; Specifically, first, the first sound data is converted into an analyzable digital signal form, which may involve the sampling and quantization process of the sound. Then, the converted digital signal is preprocessed, such as removing noise interference, to improve the accuracy of subsequent analysis. Next, the preprocessed sound data is analyzed using specific algorithms. These algorithms may include methods based on spectrum analysis, such as fast Fourier transform (FFT), which converts the sound signal from the time domain to the frequency domain to obtain information such as the energy distribution of the sound at different frequencies; it may also include analysis based on time domain features, such as calculating the duration of the sound, the zero-crossing rate, etc. Some pattern recognition techniques can also be used, such as extracting the Mel frequency cepstral coefficient (MFCC) of the sound, which comprehensively considers the characteristics of the human ear's perception of sounds of different frequencies and can well describe the characteristics of the sound.

[0075] Sound features refer to quantitative parameters extracted from sound signals to describe the physical, perceptual or functional characteristics of sound. These features are widely used in speech recognition, voiceprint recognition, sentiment analysis, music information retrieval and other fields.

[0076] S4113: Determine whether there is a sound feature similar to the target feature in the feature library corresponding to the target solution, and obtain a third result; Specifically, first, the sound features in the feature library need to be organized and indexed so that they can be efficiently retrieved and compared. For each sound feature stored in the feature library, the distance metric between it and the target feature is calculated. Distance metrics can be used in a variety of ways, such as Euclidean distance, which calculates the Euclidean distance between the vector of the target feature and the vector of each sound feature in the feature library. The smaller the distance, the higher the similarity; or cosine similarity, which measures the similarity by calculating the cosine value of the angle between the vector of the target feature and the vector of each sound feature in the feature library. The closer the cosine value is to 1, the higher the similarity. In the process of calculating distance or similarity, it is necessary to match the features according to predetermined rules. For example, if the sound feature is a multidimensional vector, it is necessary to ensure that each dimension is compared according to the same standard. Then, the similarity is determined according to the set threshold. If the calculated distance is less than a specific distance threshold, or the similarity is higher than the set similarity threshold, it can be considered that there are sound features similar to the target feature in the feature library; conversely, if all calculation results do not meet the threshold conditions, it is considered that there are no similar sound features.

[0077] It can be understood that if there is a sound feature similar to the target feature in the feature library corresponding to the target scheme, the third result is determined to be yes; if there is no sound feature similar to the target feature in the feature library corresponding to the target scheme, the third result is determined to be no.

[0078] S4114: If the third result is yes, determining the first confirmation information according to the first sound data; Specifically, if the third result is yes, the first sound data is converted into text, and the text is subjected to intent recognition. If the intent recognition result is that the medicine has been taken, the first confirmation information is determined.

[0079] The first confirmation information expresses information confirming that the medicine has been taken.

[0080] S4115: If the third result is no, then end the processing of the first sound data.

[0081] Specifically, if the third result is no, it means that the first sound data is not emitted by the person corresponding to the feature library corresponding to the target solution, and it is impossible to confirm whether the medicine has been taken, and the processing of the first sound data is terminated.

[0082] In this embodiment, the first confirmation information can be determined based on the first sound data only when there is a sound feature similar to the target feature in the feature library corresponding to the target solution. When there is no sound feature similar to the target feature in the feature library corresponding to the target solution, the first confirmation information is not extracted from the first sound data, thereby reducing erroneous results caused by misidentification. In terms of security, only sound data that meets similar features in the feature library can be used to determine the first confirmation information, which effectively prevents illegal or unauthorized personnel in the target space from obtaining relevant permissions or providing false information through means such as sound simulation.

[0083] In one embodiment, the step of determining the first confirmation information according to the first sound data includes: S41141: Based on the artificial intelligence model, perform intent recognition according to the first sound data to obtain a recognition result; Specifically, first, the pre-processed sound data is converted into text data using speech recognition technology. In this process, the speech features in the sound are mapped to the corresponding text according to the pre-trained speech model. Then, the obtained text data is input into the natural language processing (NLP) model. This NLP model is usually pre-trained with a large amount of text data, such as a neural network model pre-trained with a large-scale corpus. In the NLP model (that is, the artificial intelligence model), the text data is lexically analyzed to identify words, parts of speech and other information; then syntactic analysis is performed to determine the structure of the sentence; and semantic analysis is performed to understand the meaning of the text. Based on the results of the semantic analysis, it is matched with pre-defined intent templates or knowledge bases. These intent templates contain common intent categories and their corresponding feature patterns, and the knowledge base contains information related to various domain knowledge. Finally, the most suitable intent category is determined based on the matching results. This category is the recognition result obtained by intent recognition of the first sound data.

[0084] S41142: If the conclusion of the recognition result is to confirm taking the medicine, generating the first confirmation information according to the recognition result; Specifically, if the conclusion of the recognition result is to confirm taking the medicine, the conclusion in the recognition result and the actual time of taking the medicine are used as the first confirmation information.

[0085] Optionally, the actual medication time can also be extracted from the text data corresponding to the first sound data, and the recognition result and the generation time of the first sound data are associated to obtain the first confirmation information. First, a keyword library related to medication time is constructed, including words such as "morning", "afternoon", "evening", "what time", "hour", "before meal", "after meal" and other words indicating time and common expressions associated with medication time. Then, the converted text data is scanned word by word, and when a word in the keyword library is found, the surrounding word combination is analyzed. For example, if "morning" appears and is followed by the number "9", it may mean that the actual medication time is 9 am; if there is a combination of "after meal" and "half an hour", it is necessary to further find the meal time mentioned nearby, so as to calculate the approximate medication time. If there is a clear time format expression in the text data, such as "10:30", it can be directly determined as the actual medication time. At the same time, some special expressions, such as "about" and "about" and other fuzzy words, should be considered to accurately infer the range of the actual medication time.

[0086] S41143: If the conclusion of the recognition result is that the medication is not confirmed, a follow-up voice broadcast is performed according to the preset question voice. After the follow-up voice broadcast, second sound data is obtained, and the second sound data is used as the first sound data. The step of jumping to the step of extracting sound features based on the first sound data and continuing to execute as the target feature is performed.

[0087] Specifically, if the conclusion of the recognition result is that the medication is not confirmed, a follow-up voice broadcast is performed according to the preset question voice. After the follow-up voice broadcast, the sound data obtained by the sound sensor on the target space after the follow-up voice broadcast is obtained, and the sound data is used as the second sound data; the second sound data is used as the first sound data, and the process jumps to the step of extracting sound features according to the first sound data as target features to continue execution, and then jumps to step S4112 to continue execution.

[0088] In this embodiment, when the conclusion of the recognition result is confirmation of medication taking, the first confirmation information is generated according to the recognition result. When the conclusion of the recognition result is unconfirmed medication taking, the user is reminded to re-answer through a follow-up voice broadcast, and the first confirmation information is re-determined through the sound data of the user's re-answer, thereby avoiding ambiguous answers and improving the accuracy of medication information confirmation. By confirming the user's answers multiple times, the possibility of erroneous recording of medication information due to misjudgment or unclear answers is reduced, thereby providing a more accurate and reliable data basis for subsequent medical analysis, health management, etc.

[0089] In one embodiment, the step of updating the target solution according to the first confirmation information includes: S4121: updating the future time period in the target plan according to the first confirmation information and the medication time interval of the target drug object; Specifically, first, the actual medication time of the first confirmation information is obtained. This time point is a key time mark for accurately recording the user's confirmation of medication or reconfirmation of medication. At the same time, the medication time interval of the target drug object is clarified. This interval is pre-set and determined based on factors such as the pharmacological properties of the drug and treatment requirements. For example, a certain drug may need to be taken every 6 hours. Then, taking the actual medication time of the first confirmation information as the starting point, the next time point when the medication should be taken is calculated according to the medication time interval of the target drug object. If the medication time interval is 6 hours and the actual medication time is 9 am, then the next medication time is 3 pm. Then, adjust the future time period in the target plan. The target plan may include a series of time arrangements for drug taking. The calculated next medication time and the subsequent time points based on this medication time interval are updated to the future time period part of the target plan, replacing the previous time arrangement that may be inaccurate or outdated.

[0090] The step of updating the target solution according to the second confirmation information comprises: S4122: updating the future time period in the target plan according to the information generation time of the second confirmation information and the medication time interval of the target drug object; The future time period is the reminder time period after the reminder time period corresponding to the first result in the target solution.

[0091] Specifically, the specific implementation method steps of "updating the future time period in the target regimen according to the information generation time of the second confirmation information and the medication time interval of the target drug object" are the same as the specific implementation method steps of "updating the future time period in the target regimen according to the first confirmation information and the medication time interval of the target drug object".

[0092] This embodiment updates the future time period in the target plan according to the first confirmation information and the medication time interval of the target drug object, and updates the future time period in the target plan according to the information generation time of the second confirmation information and the medication time interval of the target drug object, thereby realizing dynamic updating of the target plan. This method enables the target plan to be adjusted in time according to changes in relevant information, improves its adaptability to the target drug taking schedule, and provides more flexible and accurate plan support for related matters such as medication management.

[0093] In one embodiment, the method further comprises: S4131: if first confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the first confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the first confirmation information; Specifically, the first confirmation information is obtained, and then the medication contraindication reminder configuration is updated according to the content of the first confirmation information. After the update is completed, the medication contraindication reminder work is performed according to the new medication contraindication reminder configuration. For example, the target device plays the medication contraindication information corresponding to the target drug object, displays the medication contraindication information corresponding to the target drug object in the client, and sends the medication contraindication information corresponding to the target drug object to the designated mobile phone number. At the same time, the data in the first confirmation information is used again to update the electronic medication record, which may include updating the medication object, time and other related information to ensure that the electronic medication record can accurately reflect the latest medication situation.

[0094] S4132: if second confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the second confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the second confirmation information; S4133: If third confirmation information corresponding to the first result is obtained, update the medication contraindication reminder configuration according to the third confirmation information, perform medication contraindication reminder according to the medication contraindication reminder configuration, and update the electronic medication record according to the third confirmation information.

[0095] It can be understood that the specific implementation steps of S4132, S4133, and S4131 are the same.

[0096] This embodiment realizes multi-source information integration and dynamic optimization of reminder configuration by updating the medication contraindication reminder configuration according to confirmation information (that is, the first confirmation information, the second confirmation information, and the third confirmation information), performing medication contraindication reminders according to the medication contraindication reminder configuration, and updating the electronic medication record according to the first confirmation information. The updated medication contraindication reminder configuration in this way can more accurately perform medication contraindication reminders, effectively avoid erroneous reminders or missed reminders due to incomplete or inaccurate information, and greatly improve the accuracy and reliability of reminders. At the same time, using the first confirmation information to update the electronic medication record helps to ensure the timeliness and completeness of the medication record, so that the record of the medication process can better reflect the actual situation, provide more comprehensive and accurate data support for medical and health management, and also facilitate medical staff or patients themselves to review, analyze and adjust the medication plan and other operations.

[0097] In one embodiment, the step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: S4141: Performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy, and controlling smart home devices according to the smart home linkage configuration of the first multi-level reminder strategy, wherein the smart home linkage configuration includes: one or more of a TV control configuration and a lighting control configuration.

[0098] Smart home linkage configuration describes the association between device identification and configuration data. Device identification can be data such as device name, device ID, etc. that uniquely identifies a smart device (for example, smart TV, smart lamp, smart curtain).

[0099] The TV control configuration includes: a TV identification and configuration data, wherein the configuration data is any one of controlling the TV to be turned off and controlling the TV volume to be turned down to a first value. The TV identification is data that uniquely identifies a TV, such as a TV name, a TV ID, etc.

[0100] The lighting control configuration includes: a lighting fixture identification and configuration data, wherein the configuration data is any one of the configuration of increasing the light brightness by a second value and the light flashing. The lighting fixture identification is data that uniquely identifies a lighting fixture, such as a lighting fixture name, lighting fixture ID, etc.

[0101] This embodiment performs a voice-based medication reminder by configuring the device according to the first multi-level reminder strategy, and controls the smart home device according to the smart home linkage configuration of the first multi-level reminder strategy. The change of the state of the smart home device can attract the user's attention, such as flashing lights, turning down the TV volume, etc. This helps to adjust the user's attention changes, allowing the user to focus on taking medicine from possible distractions, so as to better follow the medication plan, improve medication compliance and enhance the effectiveness of the entire reminder system.

[0102] See also Figure 3 As shown, in one embodiment, before the step of obtaining the target regimen of the target drug object, the step further includes: S11: Acquire a regimen update signal of the target drug object; The regimen update signal is a signal to start reminding the update of the medication regimen.

[0103] Specifically, the regimen update signal of the target drug object input by the user can be obtained, the regimen update signal of the target drug object sent by a third-party application can be obtained, or the regimen update signal of the target drug object actively triggered by the program implementing the present application according to a preset time list can be obtained.

[0104] S12: In response to the regimen update signal, acquiring historical medication data and medication standard data of the target drug object; Specifically, when the regimen update signal is received, the historical medication data and medication standard data of the target drug object are obtained from a preset storage space.

[0105] Historical medication data describes the associated data between drug objects and medication time.

[0106] Medication standard data describes the associated data of drug objects and medication time requirements.

[0107] S13: Generate the target regimen for the target drug object according to the historical medication data and the medication standard data.

[0108] Optionally, by matching the target drug object's standard medication data with historical medication records, a rule engine or machine learning model (such as time series clustering analysis of medication habits) can be used to dynamically generate a personalized medication plan. For example, the standard time point can be adjusted to "30 minutes ± 15 minutes after breakfast" based on the patient's daily routine, and redundant warnings can be added based on the probability of missed doses (such as double triggering 10 minutes in advance + 30 minutes behind).

[0109] Optionally, first, use a preset regular expression to parse the historical medication data and medication standard data to extract key information and perform feature extraction. Then calculate the adjustment data based on the extracted features, including calculating the medication time adjustment data, such as the deviation and fluctuation range between the actual medication time and the standard medication time. Finally, adjust the basic reminder medication plan for the target drug object based on these adjustment data, adjust the reminder time according to the deviation and fluctuation range of the medication time, and use the adjusted plan as the target plan.

[0110] Optionally, the step of generating the target regimen for the target drug object based on the historical medication data and the standard medication data includes: generating a medication reminder regimen for the target drug object based on the historical medication data and the standard medication data as a pending regimen, obtaining user feedback information based on the updated pending regimen, and ending the process if the conclusion of the user feedback information is agreement; if the conclusion of the user feedback information is disagreement, updating the pending regimen based on the regimen modification result of the user feedback information, and using the updated pending regimen as the target regimen for the target drug object.

[0111] Optionally, the step of generating the target regimen for the target drug object based on the historical medication data and the standard medication data includes: acquiring historical activity data of the target drug object, and generating the target regimen for the target drug object based on the historical activity data, the historical medication data and the standard medication data.

[0112] Optionally, the specific implementation steps of generating the target solution for the target drug object based on the historical activity data, the historical medication data and the medication standard data are: first, aligning the historical activity data, the historical medication data and the medication standard data, and constructing time series features (such as a daily activity-medication association matrix); second, using a clustering algorithm (such as DBSCAN) to identify patient behavior patterns and medication compliance rules, combining a rule engine (such as CQL-based medication conflict detection) and a reinforcement learning model (such as a PPO algorithm to optimize reminder timing), dynamically generating a flexible solution that adapts to the patient's living habits (such as adjusting the standard "daily medication at 8:00 / 20:00" to "1 hour ± 20 minutes after waking up" and "fixed event trigger before bedtime"), and verifying the robustness of the solution in terms of missed dose risk, peak efficacy, etc. through Monte Carlo simulation.

[0113] Specifically, historical activity data can be obtained through a third-party system (such as a door lock system). The historical activity data describes the associated data of human body identification, entry time, and exit time.

[0114] Optionally, a trend prediction model is used to predict a person's time at home based on historical activity data, and the target regimen for the target drug object is generated based on the predicted time at home, the historical medication data and the medication standard data.

[0115] This embodiment uses historical medication data to analyze past medication habits, patterns, and possible problems. Combined with the associated data in the medication standard data, a more realistic, scientific, and reasonable target solution can be developed. The target solution generated in this way takes into account both the characteristics of the drug itself (as reflected by the association between the drug object and medication time in the medication standard data) and the individual's medication history, thereby improving the accuracy and effectiveness of the medication solution, helping to better exert the therapeutic effect of the drug, and also improving the patient's adaptability and compliance to the medication solution.

[0116] See also Figure 4 As shown, in one embodiment, the step of determining whether a human body is present according to the detection data to obtain a second result includes: S321: judging whether there is a human body according to the infrared data in the detection data, and obtaining an infrared judgment result; Specifically, infrared sensors detect the human body mainly based on the thermal radiation signals emitted by the human body. First, the infrared sensor will obtain the thermal signal data in the target space. The human body temperature is around 36-37℃, so detecting a thermal signal area with a certain area in this temperature range is an important feature to obtain infrared data. Next, the shape of the thermal signal area is analyzed based on the infrared data. The human body outline is roughly elliptical or human-like. If the shape of the thermal signal area is similar, the possibility of the existence of a human body increases. At the same time, the human body is dynamic, and the thermal signal area will move with the movement of the human body. The dynamic changes of the thermal signal area are continuously monitored. If its temperature is stable in the human body temperature range and the movement trajectory conforms to the logic of human movement, such as no sudden jump movement or non-physical deformation movement, it can be judged that a human body exists.

[0117] If it is determined that a human body exists according to the infrared data in the detection data, the infrared judgment result is determined to be yes; if it is determined that a human body does not exist according to the infrared data in the detection data, the infrared judgment result is determined to be no.

[0118] S322: judging whether there is a human body according to the millimeter wave data in the detection data, and obtaining a millimeter wave judgment result; Specifically, when the millimeter wave sensor determines whether there is a human body, it mainly uses its ability to detect the micro-motion and reflection characteristics of the object. The millimeter wave sensor detects the echo signal (that is, millimeter wave data). First, the micro-motion is analyzed. Since the human body has physiological activities such as breathing and heartbeat, it will produce tiny vibrations and movements. By extracting the characteristic parameters reflecting the micro-motion such as frequency shift and phase difference in the echo signal, and comparing them with the predetermined human body micro-motion characteristic model, if it is within a reasonable range, there may be a human body. At the same time, the millimeter wave sensor can image the contour and reflection characteristics of the object, match the obtained object contour image with the preset human body contour template, and determine whether the reflection coefficient is within the normal reflection coefficient range of the human body. If the contour matching degree is high and the reflection coefficient meets the requirements, it can be determined that there is a human body.

[0119] If it is determined that a human body exists according to the millimeter wave data in the detection data, the millimeter wave judgment result is determined to be yes; if it is determined that a human body does not exist according to the millimeter wave data in the detection data, the millimeter wave judgment result is determined to be no.

[0120] S323: If both the infrared judgment result and the millimeter wave judgment result are yes, determining that the second result is yes; S324: If either the infrared judgment result or the millimeter wave judgment result is negative, acquiring the camera data in the target space; S325: Determine whether a human body exists according to the camera data to obtain the second result.

[0121] Specifically, when judging whether a human body exists based on the data captured by the camera (that is, the video data, which can be an image or a video), the captured image must first be preprocessed, including adjusting the color, contrast, etc. to improve the image quality. Then, target detection algorithms can be used, such as convolutional neural networks (CNNs) based on deep learning, which can learn various characteristic patterns of the human body. Search for areas in the image that match the characteristics of the human body, such as the human body has specific visual features such as shape, proportion, and posture, such as the shape of the head and limbs, and the relative position relationship between the various parts of the body. At the same time, it can also be judged in combination with the movement characteristics of the human body. If the object in the image shows a typical movement pattern similar to human walking, standing, bending, etc., the combined analysis results of these features can determine whether a human body exists.

[0122] First, this embodiment refers to two different detection methods, infrared and millimeter wave, at the same time. Only when the judgment results of both are yes, the second result is determined to be yes. This dual judgment mechanism improves the accuracy and reliability of the judgment and reduces the misjudgment that may occur in a single detection method. Secondly, when either infrared or millimeter wave is judged as no, the presence of a human body is further judged based on the camera data to obtain the second result. This supplementary judgment method makes the entire detection more complete and comprehensive. When there is a disagreement between different detection methods or a single detection method fails, there are still other bases to accurately judge whether a human body exists, thereby adapting to more complex environments and detection needs, and improving the effectiveness and adaptability of the overall detection.

[0123] In one embodiment, a smart auxiliary medication reminder system is provided, the system comprising: the system comprising: a target device 1, a client 2 and a server 3, the target device 1 is communicatively connected with the client 2 and the server 3, the server 3 is communicatively connected with the client 2; The target device 1 is configured to implement any of the steps of the above-mentioned intelligent auxiliary medication reminder method.

[0124] In this embodiment, when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body exists, a medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that a single reminder method may cause the information to not be fully conveyed; when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body does not exist, a second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that only ringing reminders are performed based on the set time, and in actual applications, it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users better manage their health.

[0125] In one embodiment, a target device is provided, the target device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain target regimen for target drug object; Determine whether the current time is within the reminder time period of the target plan to obtain a first result; If the first result is yes, acquiring detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result; If the second result is yes, then providing a medication reminder according to the preset first multi-level reminder strategy; If the second result is no, a medication reminder is performed according to a preset second multi-level reminder strategy.

[0126] In this embodiment, when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body exists, a medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that a single reminder method may cause the information to not be fully conveyed; when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body does not exist, a second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that only ringing reminders are performed based on the set time, and in actual applications, it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users better manage their health.

[0127] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: Obtain target regimen for target drug object; Determine whether the current time is within the reminder time period of the target plan to obtain a first result; If the first result is yes, acquiring detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result; If the second result is yes, then providing a medication reminder according to the preset first multi-level reminder strategy; If the second result is no, a medication reminder is performed according to a preset second multi-level reminder strategy.

[0128] In this embodiment, when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body exists, a medication reminder is performed according to the first multi-level reminder strategy. This multi-level reminder strategy can convey medication information to the user in a variety of ways, avoiding the problem that a single reminder method may cause the information to not be fully conveyed; when it is determined that the current time is within the reminder time period, and when it is determined based on the detection data of the target space that a human body does not exist, a second multi-level reminder strategy is adopted, which means that regardless of whether the user is in the target space, an adapted reminder method can be obtained. This effectively solves the problem in the prior art that only ringing reminders are performed based on the set time, and in actual applications, it is often impossible to ensure that the information is accurately conveyed to the user, greatly improves the effectiveness of medication reminders, reduces the occurrence of missed medications, and helps users better manage their health.

[0129] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0132] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent auxiliary medication reminder method, characterized in that: The method comprises: Obtain target regimen for target drug object; Determine whether the current time is within the reminder time period of the target plan to obtain a first result; If the first result is yes, acquiring detection data of the target space corresponding to the target drug object, determining whether a human body exists according to the detection data, and obtaining a second result; If the second result is yes, then providing a medication reminder according to the preset first multi-level reminder strategy; If the second result is no, a medication reminder is performed according to a preset second multi-level reminder strategy.

2. The intelligent medication reminder method according to claim 1, characterized in that: The step of acquiring detection data of the target space corresponding to the target drug object, and determining whether a human body exists according to the detection data to obtain a second result includes: If the medication type of the target drug object is compulsory medication, a medication reminder is performed according to a preset compulsory medication reminder strategy; If the medication type of the target drug object is non-compulsory medication, the detection data of the target space corresponding to the target drug object is obtained, and whether a human body exists is determined according to the detection data to obtain the second result.

3. The intelligent medication reminder method according to claim 1, characterized in that: If the second result is yes, the step of performing a medication reminder according to a preset first multi-level reminder strategy, and if the second result is no, the step of performing a medication reminder according to a preset second multi-level reminder strategy, include: If the second result is yes, a medication reminder based on voice broadcast is performed according to the device configuration of the first multi-level reminder strategy; if the first confirmation information corresponding to the first result is obtained, the target scheme is updated according to the first confirmation information; if the first confirmation information is not obtained within a first time period after the generation time of the second result, a medication reminder based on the client is performed according to the client configuration of the first multi-level reminder strategy; if the second confirmation information corresponding to the first result is obtained, the target scheme is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the non-medication data; if the third confirmation information corresponding to the first result is obtained, the target scheme is updated according to the third confirmation information; If the second result is no, a client-based medication reminder is performed according to the client configuration of the first multi-level reminder strategy; if second confirmation information corresponding to the first result is obtained, the target plan is updated according to the second confirmation information; if the second confirmation information is not obtained within a second time period after the generation time of the second result, a medication reminder is performed according to the emergency configuration of the first multi-level reminder strategy, and the reminder time period and the target drug object corresponding to the first result are added to the data of not taking medicine; if third confirmation information corresponding to the first result is obtained, the target plan is updated according to the third confirmation information.

4. The intelligent auxiliary medication reminder method according to claim 3, characterized in that: The step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: Performing a medication reminder based on voice broadcast according to the device configuration of the first multi-level reminder strategy, and obtaining the first sound data corresponding to the target space within a preset time period; Extracting sound features according to the first sound data as target features; Determine whether there is a sound feature similar to the target feature in the feature library corresponding to the target solution, to obtain a third result; If the third result is yes, determining the first confirmation information according to the first sound data; If the third result is no, the processing of the first sound data is terminated.

5. The intelligent auxiliary medication reminder method according to claim 4, characterized in that: The step of determining the first confirmation information according to the first sound data comprises: Based on the artificial intelligence model, perform intent recognition according to the first sound data to obtain a recognition result; If the conclusion of the recognition result is to confirm taking the medicine, generating the first confirmation information according to the recognition result; If the conclusion of the recognition result is that the medication is not confirmed, a follow-up voice broadcast is performed according to the preset question voice. After the follow-up voice broadcast, second sound data is obtained, and the second sound data is used as the first sound data. The step of jumping to the step of extracting sound features based on the first sound data and continuing to execute as target features is performed.

6. The intelligent auxiliary medication reminder method according to claim 3, characterized in that: The step of updating the target solution according to the first confirmation information comprises: updating the future time period in the target regimen according to the first confirmation information and the medication time interval of the target drug object; The step of updating the target solution according to the second confirmation information comprises: updating the future time period in the target plan according to the information generation time of the second confirmation information and the medication time interval of the target drug object; The future time period is the reminder time period after the reminder time period corresponding to the first result in the target solution.

7. The intelligent auxiliary medication reminder method according to claim 3, characterized in that: The method further comprises: If first confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the first confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the first confirmation information; If second confirmation information corresponding to the first result is obtained, updating the medication contraindication reminder configuration according to the second confirmation information, performing medication contraindication reminder according to the medication contraindication reminder configuration, and updating the electronic medication record according to the second confirmation information; If third confirmation information corresponding to the first result is obtained, the medication contraindication reminder configuration is updated according to the third confirmation information, a medication contraindication reminder is performed according to the medication contraindication reminder configuration, and the electronic medication record is updated according to the third confirmation information.

8. The intelligent auxiliary medication reminder method according to claim 3, characterized in that: The step of performing a voice-based medication reminder according to the device configuration of the first multi-level reminder strategy includes: According to the device configuration of the first multi-level reminder strategy, a voice-broadcast-based medication reminder is performed, and according to the smart home linkage configuration of the first multi-level reminder strategy, the smart home device is controlled, wherein the smart home linkage configuration includes: one or more of a TV control configuration and a lighting control configuration.

9. The intelligent auxiliary medication reminder method according to claim 1, characterized in that: Before the step of obtaining the target regimen of the target drug object, the method further includes: Obtaining a regimen update signal of the target drug object; In response to the regimen update signal, acquiring historical medication data and medication standard data of the target drug object; The target regimen for the target drug object is generated based on the historical medication data and the medication standard data.

10. The intelligent auxiliary medication reminder method according to claim 1, characterized in that: The step of determining whether a human body exists according to the detection data to obtain a second result comprises: Determine whether there is a human body according to the infrared data in the detection data, and obtain an infrared determination result; Determine whether there is a human body according to the millimeter wave data in the detection data, and obtain a millimeter wave determination result; If both the infrared judgment result and the millimeter wave judgment result are yes, determining that the second result is yes; If either the infrared judgment result or the millimeter wave judgment result is negative, acquiring the camera data in the target space; It is determined whether a human body exists according to the camera data to obtain the second result.

11. An intelligent auxiliary medication reminder system, characterized in that: The system comprises: a target device, a client and a server, wherein the target device is connected to the client and the server in communication, and the server is connected to the client in communication; The target device is configured to implement the steps of the intelligent assisted medication reminder method according to any one of claims 1 to 10.

12. A target device, characterized in that: The target device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent assisted medication reminder method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent assisted medication reminder method according to any one of claims 1 to 10 are implemented.

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