Outpatient medicine taking method and system based on self-adaptive optimization supervision algorithm

Abstract

The invention provides an outpatient medicine taking method and system based on a self-adaptive optimization supervision algorithm, and the method comprises the steps: searching a medicine taking prescription of an outpatient from a medical advice database, and writing the prescription information in the medicine taking prescription into a preset prescription dynamic priority scoring model; calculating a priority score Pi of the outpatient through the prescription dynamic priority scoring model, and recommending a medicine taking channel matched with the priority score Pi; the medicine taking channel of the outpatient is sent to the large LED screen of the pharmacy to be displayed. The prescription dynamic priority scoring model can dynamically calculate the medicine taking priority of the outpatient in combination with prescription information and recommend the medicine taking channel, so that accurate distribution of the medicine taking process can be realized through collaborative optimization of dynamic priority scoring and a machine learning classifier, the medicine taking efficiency is improved, and the waiting time of the patient is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of outpatient medication dispensing, and in particular to an outpatient medication dispensing method based on an adaptive optimization supervision algorithm, an outpatient medication dispensing system based on the adaptive optimization supervision algorithm, an electronic device and a computer-readable storage medium. Background Art

[0002] After seeing an outpatient, patients may not be able to pick up their medications on the same day due to scheduling issues at the hospital or the patient, requiring them to return to the hospital for pickup. After a doctor issues a prescription, it is stored in a database by date. After the pharmacist reads the patient's ID, they can retrieve the prescription. However, with the increasing number of patients seeking medical treatment, pharmacists often have to search and retrieve their prescriptions, which takes a long time and delays other patients' medication pickup.

[0003] Therefore, in view of the above situation, the efficiency of dispensing medicines in pharmacies is very low. This type of patients picking up medicines will delay the time for normal patients to pick up medicines, and at the same time waste the effective workload of pharmacy doctors. Summary of the Invention

[0004] In order to solve the technical problems existing in the prior art, the present invention provides the following technical solutions: In one aspect, a method for outpatient medication collection based on an adaptive optimization supervision algorithm is provided, the method being implemented by an electronic device and comprising: S1. Obtain and verify the identity information of outpatients; S2. Based on the verified identity information, retrieve the outpatient's prescription from the medical order database, and write the prescription information in the prescription into a preset dynamic prescription priority scoring model; S3, calculate the priority score P of outpatients through the dynamic priority scoring model of prescriptions i and recommends the i Matching medicine collection channel; S4. Send the outpatient patient's medication collection channel to the pharmacy LED screen for display.

[0005] Preferably, the method for verifying the identity information of the outpatient includes: Obtaining the three-modal identity information of outpatients: face + medical insurance card + mobile phone code; Verify the outpatient patient's three-modal identity information: face + medical insurance card + mobile phone code. Once the verification is passed, proceed to the next step.

[0006] Preferably, the objective function of the prescription dynamic priority scoring model is as follows: , in: i is the type of prescription drug in the prescription form, : Prescription urgency weight (emergency medicine = 3, chronic medicine = 2, common medicine = 1), : The time difference from when the prescription was generated to now (hours), : Current drug inventory saturation (Si = inventory / average daily usage), γ,δ,ϵ: dynamic adjustment coefficients, where: , , Q is the number of people queuing at the window, and the coefficient γ,ϵ is dynamically adjusted every 10-15 minutes based on the number of people queuing at the window Q.

[0007] Preferably, the medicine taking channel includes: Emergency exit: If P i ≥ 8.0, the manual window goes straight through; Smart channel: 5.0 ≤ P i < 8.0, then the medicine dispenser is automatic; Self-service channel: P i < 5.0, then it is a smart medicine cabinet.

[0008] Preferably, in step S3, after calculating the priority score P of the outpatient, i and recommends the i After the matching medication channel, it also includes: Obtain the age and prescription type of the outpatient from the prescription sheet; Obtaining a current window load of the medication dispensing channel allocated to the outpatient; The age, prescription type, and current window load of the outpatient are input into a preset ‌XGBoost classifier, and the ‌XGBoost classifier automatically classifies and outputs a window number that matches the age, prescription type, and current window load of the outpatient; The window number is sent to the pharmacy LED screen for display.

[0009] On the other hand, an outpatient medication dispensing system based on an adaptive optimization supervision algorithm is provided. The system is applied to an outpatient medication dispensing method based on an adaptive optimization supervision algorithm. The system includes: HIS system, used to store the identity information of outpatients; Medical order database, used to store prescription orders for outpatients; An identity verification system is used to verify the identity information of outpatient patients collected through scanning and send the verified identity information to the outpatient medication collection system; The outpatient medication dispensing system is used to retrieve the outpatient medication prescription from the doctor's order database based on the verified identity information, and write the prescription information in the prescription into the preset prescription dynamic priority scoring model; calculate the outpatient patient's priority score P through the prescription dynamic priority scoring model. i and recommends the i Matching medication collection channels; sending the medication collection channels of outpatients to the pharmacy LED screen for display; The pharmacy’s large LED screen is used to display the medication collection channel for outpatients.

[0010] On the other hand, an electronic device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned outpatient medication collection methods based on the adaptive optimization supervision algorithm is implemented.

[0011] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned outpatient medication collection methods based on the adaptive optimization supervision algorithm.

[0012] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: The present invention retrieves the outpatient's prescription from the doctor's order database, and writes the prescription information in the prescription into a preset prescription dynamic priority scoring model; calculates the outpatient's priority score P through the prescription dynamic priority scoring model. i and recommends the i The outpatient medication collection channel is then sent to the pharmacy's LED screen for display. The prescription dynamic priority scoring model dynamically calculates the outpatient medication collection priority and recommends the medication collection channel based on prescription information. This collaborative optimization of dynamic priority scoring and machine learning classifiers enables precise diversion of the medication collection process, improving medication collection efficiency and reducing patient wait times. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is a flow chart of an outpatient medication collection method based on an adaptive optimization supervision algorithm provided by an embodiment of the present invention; Figure 2 This is an outpatient medication dispensing system framework based on an adaptive optimization supervision algorithm provided by an embodiment of the present invention; Figure 3 This is another block diagram of an outpatient medication dispensing system based on an adaptive optimization supervision algorithm provided by an embodiment of the present invention; Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0016] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0017] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0018] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0020] The embodiment of the present invention provides an outpatient medication collection method based on an adaptive optimization supervision algorithm. The method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The flowchart of the outpatient medication collection method based on the adaptive optimization supervision algorithm shown in FIG. 1 may include the following steps: S1. Obtain and verify the identity information of outpatients; S2. Based on the verified identity information, retrieve the outpatient's prescription from the medical order database, and write the prescription information in the prescription into a preset dynamic prescription priority scoring model; S3, calculate the priority score P of outpatients through the dynamic priority scoring model of prescriptions i and recommends the i Matching medicine collection channel; S4. Send the outpatient patient's medication collection channel to the pharmacy LED screen for display.

[0021] After the present invention verifies the identity information of outpatients (there are information QR codes and other identifications on the medication collection form), the prescription dynamic priority scoring model can dynamically calculate the outpatient patients' medication collection priority and recommend medication collection channels based on the prescription information. In this way, through the coordinated optimization of dynamic priority scoring and machine learning classifiers, accurate diversion of the medication collection process can be achieved, medication collection efficiency can be improved, and patient waiting time can be reduced.

[0022] The implementation steps of the present invention will be described in detail below.

[0023] The system architecture is as follows: 1. Multimodal identity verification: Real-time patient identification information is collected through facial recognition cameras, medical insurance card readers, and mobile phone scanners; 2. Dynamic Prioritization Engine: Integrates a dynamic scoring model with the XGBoost classifier, processing over 200 requests per second. 3. Multi-channel collaboration: Manual windows, automatic medicine dispensers, and smart medicine cabinets are linked to the system through smart gateways.

[0024] The system can be deployed at the nurse station center, medical terminal or edge computing server.

[0025] For example, hardware configuration: Edge computing node: NVIDIA Jetson AGX Xavier (for deploying scoring models and classifiers); RFID shelves: Synchronize drug inventory data to the central database every 5 minutes.

[0026] Disaster recovery mechanism: When the automatic dispensing machine fails, the intelligent channel request will be automatically transferred to the manual window, and the scoring threshold will be dynamically adjusted (for example, if Pi ≥ 7.0, the emergency channel can be entered). This system achieves precise diversion of the medication collection process through the coordinated optimization of dynamic priority scoring and machine learning classifiers. The technical advantages are as follows: Multimodal identity verification: ensuring the authenticity of the patient's identity (error rate <0.1%); Elastic Scoring Model: Dynamically adjusts resource allocation strategies based on real-time queues. Smart Window Allocation: Combines spatial location with load balancing to improve drug delivery efficiency.

[0027] Preferably, the method for verifying the identity information of the outpatient includes: Obtaining the three-modal identity information of outpatients: face + medical insurance card + mobile phone code; Verify the outpatient patient's three-modal identity information: face + medical insurance card + mobile phone code. Once the verification is passed, proceed to the next step.

[0028] Trimodal identity verification (S1)‌ Face recognition: The ArcFace model was used to extract feature vectors, and the comparison threshold was set to 0.72 (FRR=1%, FAR=0.01%)1.

[0029] Medical insurance card verification: decrypt the encrypted information in the card using the SM4 national encryption algorithm.

[0030] Mobile phone code verification: Dynamic QR code (refresh every 30 seconds) combined with timestamp verification.

[0031] Preferably, the objective function of the prescription dynamic priority scoring model is as follows: , in: i is the type of prescription drug in the prescription form, : Prescription urgency weight (emergency medicine = 3, chronic medicine = 2, common medicine = 1), : The time difference from when the prescription was generated to now (hours), : Current drug inventory saturation (Si = inventory / average daily usage), γ,δ,ϵ: dynamic adjustment coefficients, where: , , Q is the number of people queuing at the window, and the coefficient γ,ϵ is dynamically adjusted every 10-15 minutes based on the number of people queuing at the window Q.

[0032] Example calculation: Patient A's prescription includes emergency medicine (W=3), the prescription generation time difference = 2 hours, the inventory saturation S=0.6, and the number of people queuing at the current window Q=12.

[0033] Parameter adjustment: , Total score calculation: Pi = 0.41 × 3 + 0.3log (2 + 1) + 0.28 × 0.6 =1.23+0.33+0.17=1.73 (self-service channel).

[0034] Dynamic coefficient adjustment rules (pseudocode example): def update_coefficients(Q): gamma = 0.5 * math.exp(-Q / 20) epsilon = 0.2 + 0.1 * math.tanh(Q / 15) return gamma, epsilon # δ is fixed to 0.3.

[0035] like Figure 2 As shown, preferably, the medicine taking channel includes: Emergency exit: If P i ≥ 8.0, the manual window goes straight through; Smart channel: 5.0 ≤ P i < 8.0, then the medicine dispenser is automatic; Self-service channel: P i < 5.0, then it is a smart medicine cabinet.

[0036] This department can also recommend numbers based on the patient's age and the window load of the assigned channel (each medication channel contains several windows, and the load can be represented by numerical coding).

[0037] like Figure 3 As shown, preferably, in step S3, after calculating the priority score P of the outpatient i and recommends the i After the matching medication channel, it also includes: Obtain the age and prescription type of the outpatient from the prescription sheet; Obtaining a current window load of the medication dispensing channel allocated to the outpatient; The age, prescription type, and current window load of the outpatient are input into a preset ‌XGBoost classifier, and the ‌XGBoost classifier automatically classifies and outputs a window number that matches the age, prescription type, and current window load of the outpatient; The window number is sent to the pharmacy LED screen for display.

[0038] We use the XGBoost classifier to automatically identify patient information and recommend medication pick-up windows.

[0039] Window-wise Assignment (XGBoost Classifier)‌ The feature engineering is shown in Table 1:

[0040] Table 1 Model training: import xgboost as xgb params = { 'max_depth': 5, 'eta': 0.1, 'objective': 'multi:softmax', 'num_class': 8 # Assume the pharmacy has 8 windows } dtrain = xgb.DMatrix(X_train, label=y_train) model = xgb.train(params, dtrain, num_boost_round=100).

[0041] ‌Assignment Logic Example‌: Input: Age = 68 (chronic disease prescription), Window 3 load rate = 85%; Output: Recommended window 5 (load rate is only 45% and is closer to the chronic disease medicine cabinet.

[0042] The performance test of XGBoost classifier is shown in Table 2:

[0043] Example scenario: During the peak period at 10:00 a.m. in a certain tertiary hospital, the pharmacy opened 3 manual windows, 5 automatic medicine dispensing machines, and 10 sets of smart medicine cabinets. The number of people queuing at the current window was Q=18Q=18.

[0044] ‌Processing Flow‌ Patient B's identity verification: facial recognition (0.7s) + medical insurance card (0.15s) + dynamic code (0.25s) → total time: 1.1s.

[0045] Prescription retrieval: Retrieve prescriptions from the HIS system (including 2 types of chronic drugs, with a generation time difference of T=6T=6 hours).

[0046] ‌Priority calculation‌: W=2, T=6, S=0.8; γ=0.5e−18 / 20 ≈0.31, ϵ=0.2+0.1tanh(18 / 15)≈0.31, Pi=0.31×2+0.3×log7+0.31×0.8≈0.62+0.25+0.25=1.12. At this time, the patient is recommended to go to the smart channel (automatic medication dispenser). ‌Window Allocation‌: XGBoost input: age = 72, prescription type = chronic, window load = 75%; Output: Distributed to automatic dispensing machine No. 3 (load rate 62%, near the chronic medicine shelf); Technical Effects Improved efficiency: Compared with the traditional model (average medication collection time of 6 minutes), this system shortens it to 2.3 minutes.

[0047] Resource Optimization: The pharmacist manual intervention rate dropped from 100% to 26%, and peak processing capacity increased by 210%.

[0048] On the other hand, an outpatient medication dispensing system based on an adaptive optimization supervision algorithm is provided. The system is applied to an outpatient medication dispensing method based on an adaptive optimization supervision algorithm. The system includes: HIS system, used to store the identity information of outpatients; Medical order database, used to store prescription orders for outpatients; An identity verification system is used to verify the identity information of outpatient patients collected through scanning and send the verified identity information to the outpatient medication collection system; The outpatient medication dispensing system is used to retrieve the outpatient medication prescription from the doctor's order database based on the verified identity information, and write the prescription information in the prescription into the preset prescription dynamic priority scoring model; calculate the outpatient patient's priority score P through the prescription dynamic priority scoring model. i and recommends the i Matching medication collection channels; sending the medication collection channels of outpatients to the pharmacy LED screen for display; The pharmacy’s large LED screen is used to display the medication collection channel for outpatients.

[0049] Please understand the above system in conjunction with the corresponding steps in the previous method.

[0050] Figure 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, the electronic device may include the above Figure 3Optionally, the electronic device 410 may include a first processor 2001 .

[0051] Optionally, the electronic device 410 may further include a memory 2002 and a transceiver 2003 .

[0052] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0053] The following combination Figure 4 The components of the electronic device 410 are described in detail. The first processor 2001 is the control center of the electronic device 410 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0054] Optionally, the first processor 2001 can execute various functions of the electronic device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0055] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0056] In a specific implementation, as an embodiment, the electronic device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0057] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0058] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0059] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0060] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0061] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0062] It should be noted that Figure 4 The structure of the electronic device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0063] In addition, the technical effects of the electronic device 410 can refer to the technical effects of the outpatient medication collection method based on the adaptive optimization supervision algorithm described in the above method embodiment, and will not be repeated here.

[0064] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0065] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0066] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0067] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0068] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0069] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0071] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0072] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of the system or unit, which can be electrical, mechanical or other forms.

[0073] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0074] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0075] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for outpatient medication collection based on an adaptive optimization supervision algorithm, characterized in that: The method comprises: S1. Obtain and verify the identity information of outpatients; S2. Based on the verified identity information, retrieve the outpatient's prescription from the medical order database, and write the prescription information in the prescription into a preset dynamic prescription priority scoring model; S3, calculate the priority score P of outpatients through the dynamic priority scoring model of prescriptions i and recommends the i Matching medicine collection channel; S4. Send the outpatient patient's medication collection channel to the pharmacy LED screen for display.

2. The outpatient medication collection method based on the adaptive optimization supervision algorithm according to claim 1 is characterized in that: The method for verifying the identity information of the outpatient patient comprises: Obtaining the three-modal identity information of outpatients: face + medical insurance card + mobile phone code; Verify the outpatient patient's three-modal identity information: face + medical insurance card + mobile phone code. Once the verification is passed, proceed to the next step.

3. The outpatient medication collection method based on the adaptive optimization supervision algorithm according to claim 1 is characterized in that: The objective function of the prescription dynamic priority scoring model is as follows: ， in: i is the type of prescription drug in the prescription form, : Prescription urgency weight (emergency medicine = 3, chronic medicine = 2, common medicine = 1), : The time difference from when the prescription was generated to now (hours), : Current drug inventory saturation (Si = inventory / average daily usage), γ,δ,ϵ: dynamic adjustment coefficients, where: ， ， Q is the number of people queuing at the window, and the coefficient γ,ϵ is dynamically adjusted every 10-15 minutes based on the number of people queuing at the window Q.

4. The outpatient medication collection method based on the adaptive optimization supervision algorithm according to claim 1 is characterized in that: The medicine taking channel includes: Emergency exit: If P i ≥ 8.0, the manual window goes straight through; Smart channel: 5.0 ≤ P i < 8.0, then the medicine dispenser is automatic; Self-service channel: P i < 5.0, then it is a smart medicine cabinet.

5. The outpatient medication collection method based on the adaptive optimization supervision algorithm according to claim 1 is characterized in that: In step S3, after calculating the priority score P of the outpatient, i and recommends the i After the matching medication access channel, it also includes: Obtain the age and prescription type of the outpatient from the prescription sheet; Obtaining a current window load of the medication dispensing channel allocated to the outpatient; The age, prescription type, and current window load of the outpatient are input into a preset ‌XGBoost classifier, and the ‌XGBoost classifier automatically classifies and outputs a window number that matches the age, prescription type, and current window load of the outpatient; The window number is sent to the pharmacy LED screen for display.

6. An outpatient medication dispensing system based on an adaptive optimization supervision algorithm, wherein the outpatient medication dispensing system based on an adaptive optimization supervision algorithm is used to implement the outpatient medication dispensing method based on an adaptive optimization supervision algorithm as described in any one of claims 1 to 5, characterized in that: The system comprises: HIS system, used to store the identity information of outpatients; Medical order database, used to store prescription orders for outpatients; An identity verification system is used to verify the identity information of outpatient patients collected through scanning and send the verified identity information to the outpatient medication collection system; The outpatient medication dispensing system is used to retrieve the outpatient medication prescription from the doctor's order database based on the verified identity information, and write the prescription information in the prescription into the preset prescription dynamic priority scoring model; calculate the outpatient patient's priority score P through the prescription dynamic priority scoring model. i and recommends the i Matching medication collection channels; sending the medication collection channels of outpatients to the pharmacy LED screen for display; The pharmacy’s large LED screen is used to display the medication collection channel for outpatients.

7. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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