Intelligent eye medicine box and control method thereof

Abstract

The invention relates to the technical field of intelligent eye medicine boxes, and discloses an intelligent eye medicine box and a control method thereof.The eye medicine box is divided into a plurality of compartments for storing different types of medicine, and a sensor is arranged in each compartment to detect the weight of the medicine in real time; and accurately calculating the residual amount of the medicine by combining the initial weight data of the medicine, the dosage of the medicine currently used by the user, the last time of using the medicine by the user, the weight of the medicine in the last time and the total number of times of using the medicine by the user. Furthermore, the future drug use dosage and use rate are predicted through a machine learning model, the remaining amount is optimized based on the prediction results and the real-time weight data, the optimized remaining amount is obtained, and the optimal remaining amount is calculated according to the optimized remaining amount and the future drug use rate. Specifically, firstly, the accuracy of medicine management is remarkably improved through a smoke and flame identification method, it is ensured that a patient can use medicine on time according to the amount, and the poor treatment effect caused by missed taking or wrong taking is avoided;

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent eye medicine boxes, and in particular to an intelligent eye medicine box and a control method thereof. Background Art

[0002] With the advancement of modern medical technology, more and more drugs require precise management and storage conditions, especially ophthalmic drugs. Ophthalmic drugs such as eye drops, eye gels and eye ointments usually need to be used at specific time intervals and in a specific order to ensure the best therapeutic effect. However, patients need to remember the time, dosage and order of each medication, especially when patients need to use multiple drugs at the same time, which may easily lead to missed or wrong medications. It is difficult for patients to accurately know the remaining amount of the drug, especially during use, the weight of the drug will gradually decrease, and the traditional weighing method is not accurate enough, which can easily lead to drug waste or shortage. Some ophthalmic drugs have strict temperature requirements, and if they are not stored properly, they may affect the effectiveness and safety of the drug. Traditional eye medicine boxes usually do not have temperature control functions and cannot meet these special needs. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a control method of a smart eye medicine box, comprising: Step 1, constructing an eye medicine box with a plurality of compartments by 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines; Step 2, setting storage standards for each type of medicine, and controlling the temperature of the medicine according to the storage standards; Step 3, a sensor is arranged in the compartment of the eye medicine box, and real-time weight data of the medicine stored in the compartment of the eye medicine box is obtained through the sensor; Step 4, obtaining the initial weight data of the drug, the current drug dosage of the user, the current drug use time of the user, and the total number of times the user uses the drug; Step 5, calculate the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the weight of the drug at the user's last drug use time, and the total number of times the user uses the drug; predict the future drug dosage and drug use rate through a machine learning model; optimize the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculate the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

[0004] Furthermore, a storage standard is set for each type of medicine, and the temperature of the medicine is controlled according to the storage standard, including the following steps: Step 21, storing the medicine in the compartment of the eye medicine box at room temperature and refrigerated storage according to the storage standard; Step 22, setting a refrigerator on the eye medicine box, obtaining the current ambient temperature and the temperature of the medicine to be refrigerated and stored, and calculating the temperature change rate; Step 23, adjusting the power of the refrigerator according to the temperature change rate to control the temperature of the medicine.

[0005] Furthermore, the remaining amount of the drug is calculated as follows: ; In the formula, Represents the remaining amount of the drug, Represents the initial weight data of the drug, represents the dosage of the drug used by the user for the i-th time, represents the total number of times the user used the drug up to time t, Represents the last time the user used the drug. The weight of the drug representing the last time the user used the drug, is the adjustment factor.

[0006] Furthermore, the future drug dosage and drug usage rate are predicted by a machine learning model, including the following steps: Step 51, obtaining real-time weight data of the medicine stored in the compartment of the eye medicine box, the time when the user uses the medicine, the dosage of the medicine used by the user, the remaining amount of the medicine, and the number of times the user uses the medicine; Step 52, preprocessing the data in step 51 and extracting features, the extracted features include: average number of times the drug is used per day, the dosage of the drug used each time and the average dosage, the user's drug use habits, the drug use rate, the remaining amount of the drug, and the real-time weight data of the drug; Step 53, using the extracted features as a training set to train the machine learning model to obtain a trained machine learning model; using the future drug dosage and usage rate as target variables; Step 54, inputting the remaining amount of the drug calculated in real time, the drug dosage and drug usage time of the previous m times up to the current time t, and the drug usage rate, and the last time the user used the drug into the trained machine learning model to obtain the future drug dosage; Step 55, obtaining a change in the drug use rate based on the drug use rate and the future drug use rate; and obtaining the future drug use rate based on the drug use rate and the change in the drug use rate.

[0007] Furthermore, the formula for optimizing the residual is: ; In the formula, represents the optimization surplus, represents the real-time weight data of the drug at time t, Represents time t to time The first The dosage of the drug used by the user Represents time t to time The total number of times a drug is used by a user.

[0008] Furthermore, the optimal residual is: ; In the formula, represents the optimal surplus, Representing the future moment rate of drug use.

[0009] The formula for calculating the future rate of drug use is: ; In the formula, represents the drug use rate at time t, Represents time t and future time The amount of change in the rate of drug use between the two groups.

[0010] The present invention also provides a smart eye medicine box, comprising the following modules: A construction module: used to construct an eye medicine box with a plurality of compartments by 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines; Temperature control module: connected with the building module, used to set storage standards for each type of medicine and perform temperature control on the medicine according to the storage standards; A real-time weight data acquisition module: connected to the construction module, and used to set a sensor in the compartment of the eye medicine box, and obtain the real-time weight data of the medicine stored in the compartment of the eye medicine box through the sensor; Basic data acquisition module: connected with the real-time weight data acquisition module to obtain the initial weight data of the drug, the current drug dosage of the user, the current drug use time of the user, and the total number of times the user uses the drug; Optimization module: connected to the basic data acquisition module, calculates the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the drug weight when the user last used the drug, and the total number of times the user uses the drug; predicts future drug dosage and drug use rate through a machine learning model; optimizes the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculates the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

[0011] The embodiments of the present invention have the following technical effects: The present invention accurately calculates the remaining amount of the medicine by setting the eye medicine box into several compartments to store different types of medicines, and setting a sensor in each compartment to detect the weight of the medicine in real time, combining the initial weight data of the medicine, the current dosage of the medicine used by the user, the time of the last use of the medicine by the user, the weight of the medicine at the last use, and the total number of times the user uses the medicine. Further, the future dosage and usage rate of the medicine are predicted by a machine learning model, and the remaining amount is optimized based on these prediction results and real-time weight data to obtain the optimized remaining amount, and the optimal remaining amount is calculated based on the optimized remaining amount and the future drug usage rate. Specifically: First, the accuracy of drug management is significantly improved, ensuring that patients can use the medicine on time and in the right amount, avoiding poor treatment effects caused by missed or wrong medication; second, by accurately controlling the storage conditions of the medicine, the validity period of the medicine is extended, and the safety and effectiveness of the medicine are guaranteed; further, the system can make intelligent predictions based on the user's medication habits and historical data, plan drug replenishment in advance, prevent drug shortages or waste, and improve the efficiency of drug management.

[0012] The present invention achieves precise temperature control by calculating the temperature change rate according to the current ambient temperature and the temperature of the medicine that needs to be stored under refrigeration, thereby bringing about significant beneficial effects in many aspects. First, this intelligent temperature control system ensures that the medicine is always stored under the best storage conditions, avoids the failure or deterioration of the medicine due to temperature fluctuations, greatly extends the shelf life of the medicine, and ensures the safety and efficacy of the medicine. Secondly, by real-time monitoring and dynamic adjustment of the temperature change rate, the system can quickly respond to changes in ambient temperature and maintain a constant temperature in the compartment, ensuring the stability of the medicine even when the external ambient temperature fluctuates greatly. Furthermore, the system can flexibly set different temperature control strategies according to the specific storage requirements of different medicines to meet the personalized storage needs of various medicines, and is particularly suitable for temperature-sensitive ophthalmic drugs such as eye drops, eye gels and eye ointments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 is a flow chart of a control method of a smart eye medicine box provided by an embodiment of the present invention; Figure 2It is a structural schematic diagram of a control method of a smart eye medicine box provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an electronic device provided by the implementation of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0016] Figure 1 1 is a flow chart of a control method of a smart eye medicine box provided by an embodiment of the present invention. Figure 1 , including: Step 1: construct an eye medicine box with a plurality of compartments by 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines.

[0017] Step 2, setting storage standards for each type of medicine and performing temperature control on the medicine according to the storage standards.

[0018] Step 21, storing the medicine in the compartment of the eye medicine box at room temperature and refrigerated storage according to storage standards.

[0019] Step 22, a refrigerator is installed on the eye medicine box, the current ambient temperature and the temperature of the medicine to be stored in a refrigerated state are obtained, and the temperature change rate is calculated.

[0020] Step 23, adjusting the power of the refrigerator according to the temperature change rate to control the temperature of the medicine.

[0021] Step 3, a sensor is arranged in the compartment of the eye medicine box, and real-time weight data of the medicine stored in the compartment of the eye medicine box is obtained through the sensor.

[0022] A sensor is arranged in each compartment of the smart eye medicine box. The sensor is preferably a weight sensor, which detects the weight of the medicine in the compartment in real time, so that real-time weight data can be obtained through the sensor.

[0023] Step 4, obtaining the initial weight data of the drug, the current drug dosage used by the user, the current drug use time of the user, and the total number of times the user uses the drug.

[0024] The real-time weight data obtained may refer to the real-time weight data obtained by the sensor when the user puts the medicine back into the compartment after taking it. Therefore, the current drug dosage used by the user can be obtained through the real-time weight data monitored by the sensor. The initial weight data is when the user places the medicine in the compartment for the first time, and the sensor records the real-time weight data at that time as the initial weight data. The sensor detects the weight change after the medicine is taken out for the current time, and calculates the current drug dosage used by the user. The drug weight data collected by the sensor determines the timestamp of each time the medicine is taken out and put back, which serves as the basis for the current drug use time of the user. The number of weight data changes is obtained from the sensor, so that the number of times the user takes out the medicine each time can be counted, and the total number of times the user uses the medicine can be accumulated.

[0025] Step 5, calculate the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the weight of the drug at the user's last drug use time, and the total number of times the user uses the drug; predict the future drug dosage and drug use rate through a machine learning model; optimize the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculate the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

[0026] ; In the formula, Represents the remaining amount of the drug, Represents the initial weight data of the drug, represents the dosage of the drug used by the user for the i-th time, represents the total number of times the user used the drug up to time t, Represents the last time the user used the drug. The weight of the drug representing the last time the user used the drug, is the adjustment factor.

[0027] Predicting future drug dosage and drug usage rate through machine learning models includes the following steps: Step 51, obtaining real-time weight data of the medicine stored in the compartment of the eye medicine box, the time when the user uses the medicine, the dosage of the medicine used by the user, the remaining amount of the medicine, and the number of times the user uses the medicine.

[0028] Step 52, preprocessing the data in step 51 and performing feature extraction, the extracted features include: the average number of times the drug is used per day, the dosage of the drug used each time and the average dosage, the user's drug use habits, the drug use rate, the remaining amount of the drug, and the real-time weight data of the drug.

[0029] Step 53, using the extracted features as a training set to train the machine learning model to obtain a trained machine learning model; and using the future drug dosage and usage rate as target variables.

[0030] Step 54, the remaining amount of the drug calculated in real time, the drug dosage and drug usage time of the previous m times up to the current time t, the drug usage rate, and the last time the user used the drug are input into the trained machine learning model to obtain the future drug dosage.

[0031] Step 55, obtaining a change in the drug use rate based on the drug use rate and the future drug use rate; and obtaining the future drug use rate based on the drug use rate and the change in the drug use rate.

[0032] ; In the formula, represents the optimization surplus, represents the real-time weight data of the drug at time t, Represents time t to time The first The dosage of the drug used by the user Represents time t to time The total number of times a drug is used by a user.

[0033] ; In the formula, represents the optimal surplus, Representing the future moment rate of drug use.

[0034] ; In the formula, represents the drug use rate at time t, Represents time t and future time The amount of change in the rate of drug use between the two groups.

[0035] like Figure 2 As shown, this embodiment also provides a smart eye medicine box, which is used to execute the above-mentioned control method of a smart eye medicine box, including the following modules: Building module: used to build an eye medicine box with a plurality of compartments through 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines.

[0036] A magnetic card patch or QR code can be attached to the drug packaging, and each patch or QR code is uniquely matched with the compartment of the eye medicine box to ensure that the drug is placed in the correct compartment. For example, the user manually enters the name of the drug through a mobile phone APP or applet and associates it with the patch or QR code. The user can manually enter the dosage for each use (such as 10 mg each time) and set the number of daily medications (such as 3 times a day). The user can also manually enter the initial weight or quantity of the drug, and the system will automatically update the remaining amount of the drug based on the data from the real-time weight sensor.

[0037] In another embodiment, relevant identification marks (such as labels, color codes, etc.) are placed on the drug packaging to help users quickly find the corresponding drug.

[0038] In another embodiment, the user can also manually enter the opening time of the medicine through the APP or mini program. After entering, the system will automatically calculate the expiration date of the medicine and set a reminder. The system automatically calculates the expiration date of the medicine based on the type of medicine and the opening time. For example, some eye drops are valid for 30 days after opening, and the system will issue a reminder on the 30th day. Through the APP or mini program, the system will send a notification when the medicine is about to expire, reminding the user to change the medicine in time. Users can choose to receive push notifications, text messages, or email reminders.

[0039] In another embodiment, the user can manually input the usage of the drug (such as eye drops, oral administration, etc.) through the APP or applet, and choose whether to need a timed reminder, manually input the dosage used each time (such as 10 mg each time), and set the number of daily medications (such as 3 times a day). The user can also set the daily medication time period (such as 8 am to 10 pm), and the system will remind the user to take the medicine on time during this time period. The start date and end date of the medication can be set (such as from November 1 to November 30), and the system will provide reminders within the date range. Specifically, when the medication time set by the user is reached, the eye medicine box can remind the user to take the medicine on time in a variety of ways. First, the eye medicine box can sound a bell or voice broadcast to clearly inform the user of the name of the medicine to be used, such as "levofloxacin eye drops at this point". In addition, in order to further improve the effect of the reminder, the eye medicine box can also be designed to automatically pop up the corresponding medicine, or flash the compartment where the medicine is stored, providing simultaneous sound and light stimulation. This multi-sensory reminder method can effectively ensure that users will not miss the medication time, especially for users with visual or hearing impairments, providing a more user-friendly experience. In this way, the working status module can not only monitor the working status of the eye medicine box in real time, but also help users better manage medication use through intelligent reminder functions.

[0040] Temperature control module: connected with the building module, used to set storage standards for each type of medicine and perform temperature control on the medicine according to the storage standards.

[0041] The temperature control module of this embodiment has a refrigeration function, which allows medicines that need to be stored at low temperatures and medicines at room temperature to be stored separately in the same eye medicine box, and can also control the temperature of the low-temperature medicines.

[0042] Temperature control module, including the following sub-modules: Storage submodule: used for storing the medicine in the compartment of the eye medicine box at room temperature or refrigerated storage according to storage standards.

[0043] Temperature change rate calculation submodule: used to set a refrigerator on the eye medicine box, obtain the current ambient temperature and the temperature of the medicine that needs to be refrigerated and stored, and calculate the temperature change rate.

[0044] Power adjustment submodule: used to adjust the power of the refrigerator according to the temperature change rate and control the temperature of the medicine.

[0045] Real-time weight data acquisition module: connected to the building module, used to set a sensor in the compartment of the eye medicine box, and obtain the real-time weight data of the medicine stored in the compartment of the eye medicine box through the sensor.

[0046] A sensor is arranged in each compartment of the smart eye medicine box. The sensor is preferably a weight sensor, which detects the weight of the medicine in the compartment in real time, so that real-time weight data can be obtained through the sensor.

[0047] Basic data acquisition module: connected with the real-time weight data acquisition module to obtain the initial weight data of the drug, the current drug dosage used by the user, the current drug use time of the user, and the total number of times the user uses the drug.

[0048] The real-time weight data obtained may refer to the real-time weight data obtained by the sensor when the user puts the medicine back into the compartment after taking it. Therefore, the current dosage of the medicine used by the user can be obtained through the real-time weight data monitored by the sensor.

[0049] The basic data acquisition module includes the following sub-modules: Initial weight data acquisition submodule: used to obtain the initial weight data of the drug.

[0050] Initial weight data When the user places the medicine in the compartment for the first time, the sensor records the real-time weight data at that time as the initial weight data.

[0051] User medication dosage acquisition submodule: used to obtain the user's current medication dosage.

[0052] The sensor detects the weight change after the medicine is taken out for the last time and calculates the dosage of the medicine used by the user for the last time.

[0053] User medication time acquisition submodule: used to acquire the user medication time.

[0054] The drug weight data collected by the sensor is used to determine the timestamp of each time the drug is taken out and put back, which serves as the basis for the user's current drug use time.

[0055] The module for obtaining the total number of times a user uses drugs is used to obtain the total number of times a user uses drugs.

[0056] The number of times the weight data changes is obtained from the sensor, so that the number of times the user takes out the medicine each time can be counted, and the total number of times the user uses the medicine can be accumulated.

[0057] In another embodiment, through the APP or applet, the system can synchronize the working status of the medicine box in real time, including whether any medicine has been taken out, put back, and whether the dosage has changed. The specific functions are as follows: Removal reminder: When the user takes out the medicine from the compartment, the APP will immediately display a reminder to inform the user that the medicine has been taken out, and display the time and dosage of the removal; Put back reminder: When the user puts the medicine back into the compartment, the APP will display a reminder to inform the user that the medicine has been put back and confirm the remaining amount of the medicine; Dosage change reminder: If the amount of medicine taken out by the user does not match the expected amount (such as more or less), the system will issue a warning to remind the user to confirm the actual dosage used.

[0058] In another embodiment, this embodiment can also determine whether the user takes the medicine on time based on the medication time and frequency set by the user. If the user does not take the medicine on time, the system will send a reminder notification to ensure that the user does not miss the medication time. If the user frequently takes out or puts back the medicine, or the dosage fluctuates greatly, the system will detect abnormal behavior and remind the user or his guardian through the APP or mini program to ensure the safety of medication.

[0059] Optimization module: connected to the basic data acquisition module, calculates the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the drug weight when the user last used the drug, and the total number of times the user uses the drug; predicts future drug dosage and drug use rate through a machine learning model; optimizes the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculates the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

[0060] The optimization module includes the following submodules: The remaining amount of medicine calculation submodule is used to calculate the remaining amount of medicine according to the initial weight data of the medicine, the current dosage of the medicine used by the user, the time when the user last used the medicine, the weight of the medicine when the user last used the medicine, and the total number of times the user used the medicine.

[0061] Machine learning models: used to predict future drug dosage and drug usage rates.

[0062] Optimization remainder calculation submodule: used to calculate the optimization remainder.

[0063] Optimal remaining quantity calculation submodule: used to calculate the optimal remaining quantity.

[0064] The machine learning model consists of the following sub-units: Preprocessing and feature extraction subunit: used to preprocess and extract features of real-time weight data, user's drug use time, user's drug dosage, remaining amount of drug, and number of times user uses drug. The extracted features include: average number of times drug is used per day, dosage of drug used each time and average dosage, user's drug use habits, drug use rate, remaining amount of drug, and real-time weight data of drug.

[0065] Training set acquisition subunit: used to use the extracted features as training sets to train the machine learning model and obtain a trained machine learning model; and use the future drug dosage and usage rate as target variables.

[0066] Future drug dosage prediction subunit: used to predict future drug dosage.

[0067] Future drug use rate prediction subunit: used to predict the future drug use rate.

[0068] In this subunit, the change in drug use rate is obtained based on the drug use rate and the future drug use rate; and the future drug use rate is obtained based on the drug use rate and the change in drug use rate.

[0069] ; In the formula, represents the drug use rate at time t, Represents time t and future time The amount of change in the rate of drug use between the two groups.

[0070] ; In the formula, Represents the remaining amount of the drug, Represents the initial weight data of the drug, represents the dosage of the drug used by the user for the i-th time, represents the total number of times the user used the drug up to time t, Represents the last time the user used the drug. The weight of the drug representing the last time the user used the drug, is the adjustment factor.

[0071] ; In the formula, represents the optimization surplus, represents the real-time weight data of the drug at time t, Represents time t to time The first The dosage of the drug used by the user Represents time t to time The total number of times a drug is used by a user.

[0072] ; In the formula, represents the optimal surplus, Representing the future moment rate of drug use.

[0073] In another embodiment, the module also has a sequential interval prompt function for multiple combined medications. Specifically, this function is intended to help users set reasonable medication intervals according to different types of drugs (such as eye drops, eye gels, and eye ointments). The system can automatically arrange the order and time intervals of medication according to preset rules, for example: 10 minutes between eye drops, and 15 minutes between eye drops and gels or ointments. Users can also manually adjust these intervals according to the doctor's advice. For example, if a user needs to use three drugs at 12 noon, two of which are aqueous solutions and one is an ointment, the system will remind the user to use the first aqueous solution at 11:50, remind the user to use the second aqueous solution at 12 o'clock, and finally remind the user to use the third ointment at 12:15. In this way, the optimization module can not only intelligently manage the order and intervals of drug use, but also ensure that users follow the correct medication process to avoid drug interactions or reduced efficacy due to improper intervals.

[0074] Figure 3 Schematic diagram of the structure of an electronic device provided by the present invention. Figure 3 As shown, the electronic device 500 includes one or more processors 501 and a memory 502 .

[0075] The processor 501 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 500 to perform desired functions.

[0076] The memory 502 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 501 may run the program instructions to implement a smart eye medicine box and its control method and / or other desired functions of any embodiment of the present application described above. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0077] In one example, the electronic device 500 may further include: an input device 503 and an output device 504, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 503 may include, for example, a keyboard, a mouse, etc. The output device 504 may output various information to the outside, including early warning prompt information, braking force, etc. The output device 504 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0078] Of course, to simplify, Figure 3 Only some of the components related to the present application in the electronic device 500 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 500 may also include any other appropriate components.

[0079] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of a smart eye medicine box and its control method provided in any embodiment of the present application.

[0080] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0081] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of a smart eye medicine box and a control method thereof provided in any embodiment of the present application.

[0082] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0083] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0084] It should also be noted that the orientations or positional relationships indicated by the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A control method for an intelligent eye medicine box, characterized in that: include: Step 1, constructing an eye medicine box with a plurality of compartments by 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines; Step 2, setting storage standards for each type of medicine, and controlling the temperature of the medicine according to the storage standards; Step 3, a sensor is arranged in the compartment of the eye medicine box, and real-time weight data of the medicine stored in the compartment of the eye medicine box is obtained through the sensor; Step 4, obtaining the initial weight data of the drug, the current drug dosage of the user, the current drug use time of the user, and the total number of times the user uses the drug; Step 5, calculate the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the weight of the drug at the user's last drug use time, and the total number of times the user uses the drug; predict the future drug dosage and drug use rate through a machine learning model; optimize the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculate the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

2. The control method of the smart eye medicine box according to claim 1, characterized in that: Setting storage standards for each type of medication and temperature controlling the medication according to the storage standards includes the following steps: Step 21, storing the medicine in the compartment of the eye medicine box at room temperature and refrigerated storage according to the storage standard; Step 22, setting a refrigerator on the eye medicine box, obtaining the current ambient temperature and the temperature of the medicine to be refrigerated and stored, and calculating the temperature change rate; Step 23, adjusting the power of the refrigerator according to the temperature change rate to control the temperature of the medicine.

3. The control method of the intelligent eye medicine box according to claim 1, characterized in that: The remaining amount of the drug is calculated as: ； In the formula, Represents the remaining amount of the drug, Represents the initial weight data of the drug, represents the dosage of the drug used by the user for the i-th time, represents the total number of times the user used the drug up to time t, Represents the last time the user used the drug. The weight of the drug representing the last time the user used the drug, is the adjustment factor.

4. The control method of the smart eye medicine box according to claim 1, characterized in that: Predicting future drug dosage and drug usage rate through machine learning models includes the following steps: Step 51, obtaining real-time weight data of the medicine stored in the compartment of the eye medicine box, the time when the user uses the medicine, the dosage of the medicine used by the user, the remaining amount of the medicine, and the number of times the user uses the medicine; Step 52, preprocessing the data in step 51 and extracting features, the extracted features include: average number of times the drug is used per day, the dosage of the drug used each time and the average dosage, the user's drug use habits, the drug use rate, the remaining amount of the drug, and the real-time weight data of the drug; Step 53, using the extracted features as a training set to train the machine learning model to obtain a trained machine learning model; using the future drug dosage and usage rate as target variables; Step 54, inputting the remaining amount of the drug calculated in real time, the drug dosage and drug usage time of the previous m times up to the current time t, and the drug usage rate, and the last time the user used the drug into the trained machine learning model to obtain the future drug dosage; Step 55, obtaining a change in the drug use rate based on the drug use rate and the future drug use rate; and obtaining the future drug use rate based on the drug use rate and the change in the drug use rate.

5. The control method of the smart eye medicine box according to claim 1, characterized in that: The formula for optimizing the residual is: ； In the formula, represents the optimization surplus, represents the real-time weight data of the drug at time t, Represents time t to time The first The dosage of the drug used by the user Represents time t to time The total number of times a drug is used by a user.

6. The control method of the intelligent eye medicine box according to claim 5, characterized in that: The optimal surplus is: ； In the formula, represents the optimal surplus, Representing the future moment rate of drug use.

7. The control method of the intelligent eye medicine box according to claim 6, characterized in that: The formula for calculating the future rate of drug use is: ； In the formula, represents the drug use rate at time t, Represents time t and future time The amount of change in the rate of drug use between the two groups.

8. A smart eye medicine box, used to execute the control method of a smart eye medicine box according to any one of claims 1 to 7 above, characterized in that: Includes the following modules: A construction module: used to construct an eye medicine box with a plurality of compartments by 3D printing technology, wherein the compartments of the eye medicine box are used to store different types of medicines; Temperature control module: connected with the building module, used to set storage standards for each type of medicine and perform temperature control on the medicine according to the storage standards; A real-time weight data acquisition module: connected to the construction module, and used to set a sensor in the compartment of the eye medicine box, and obtain the real-time weight data of the medicine stored in the compartment of the eye medicine box through the sensor; Basic data acquisition module: connected with the real-time weight data acquisition module to obtain the initial weight data of the drug, the current drug dosage of the user, the current drug use time of the user, and the total number of times the user uses the drug; Optimization module: connected to the basic data acquisition module, calculates the remaining amount of the drug based on the initial weight data of the drug, the user's current drug dosage, the user's last drug use time, the drug weight when the user last used the drug, and the total number of times the user uses the drug; predicts future drug dosage and drug use rate through a machine learning model; optimizes the remaining amount based on the predicted future drug dosage and real-time weight data to obtain the optimized remaining amount; calculates the optimal remaining amount based on the optimized remaining amount and the future drug use rate.

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