An AI-based automated drug dispensing management system and method

Abstract

This invention relates to the field of automated dispensing equipment management technology, specifically to an automated dispensing management system and method based on artificial intelligence. Upon detecting a new fault report, the invention triggers an assessment of the dispensing control platform within the corresponding work evaluation period. It determines whether this is due to performance degradation. The analysis involves analyzing the continuous operational distribution of the dispensing robot within the evaluation period and the frequency distribution of challenging task change nodes. Ultimately, this allows for the evaluation of the dispensing control platform's operational performance index within the evaluation period. This invention effectively monitors and addresses aging or damage caused by prolonged operation of the dispensing control platform, promptly identifying and resolving performance degradation issues.

Description

Technical Field

[0001] This invention relates to the field of automated drug dispensing equipment management technology, specifically to an automated drug dispensing management system and method based on artificial intelligence. Background Technology

[0002] Automated dispensing machines refer to automated medication dispensing equipment used in pharmacies. Their working principle is based on computer control and mechanical automation technology. When medication needs to be dispensed, instructions are transmitted to a dispensing robot, which can be a robotic arm. Using sensors and actuators, the robot precisely grasps the medication from the pillbox and places it at the dispensing port. The advent of automated dispensing machines can shorten patients' waiting time, improve the medical environment, free pharmacists from "medication handling work," provide patients with higher-quality pharmaceutical services, reduce errors, and improve dispensing accuracy.

[0003] With the increasing prevalence of automated dispensing machines in hospitals, malfunctions can significantly impact patient care. If automated dispensing machines operate for extended periods, they are prone to aging or damage, requiring timely replacement or repair. Therefore, outpatient pharmacy managers and staff should regularly check the equipment's operational status, promptly identify and address malfunctions, and ensure the normal operation of the outpatient department. Summary of the Invention

[0004] The purpose of this invention is to provide an automated drug dispensing management system and method based on artificial intelligence, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an automated medication dispensing management method based on artificial intelligence, the method comprising:

[0006] Step S1: Whenever a new fault error record is captured from the operation log of the drug dispensing control platform, the operation evaluation interval of the drug dispensing control platform is extracted, and each operation period when the drug dispensing control platform is in continuous operation is extracted within the operation evaluation interval.

[0007] If the drug dispensing robot in the drug dispensing control platform is found to have continuously retrieved and dispensed the relevant drugs for at least two prescriptions, then the drug dispensing control platform is considered to be in a continuous operating state within the time range of the above process.

[0008] Step S2: Collect the drug dispensing records generated by the drug dispensing control platform in chronological order during each working period to obtain the work list completed by the drug retrieval robot of the drug dispensing control platform during each working period. Each work record in the work list corresponds to a drug, and each two adjacent work records constitute a work change node of the drug retrieval robot.

[0009] Step S3: Simulate the intelligent medicine storage cabinet, establish a three-dimensional image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list. Based on the distribution of the storage location coordinates that constitute each work change node in the three-dimensional image model, calculate the state change coefficient for each work change node.

[0010] Step S4: Taking into account the state change coefficient of each job change node and the actual execution of the drug retrieval robot at each job change node, evaluate the degree of state change presented by the drug retrieval robot when executing each job change node.

[0011] Step S5: Analyze the interval distribution of the operation periods presented in the operation evaluation interval, and the frequency distribution of operation change nodes with corresponding status change values ​​greater than the threshold in each operation period, and evaluate the operation performance index presented by the drug dispensing control platform in the operation evaluation interval.

[0012] Step S6: When the operational performance index of the drug dispensing control platform exceeds the index threshold during the operational evaluation period, an anomaly is reported to the management personnel, prompting them to conduct performance testing on the drug dispensing control platform.

[0013] Preferably, step S1 includes: obtaining the generation timestamp Tr of the newly added fault error record, extracting the historical fault error record closest to the newly added fault error record, obtaining the generation timestamp Te of the historical fault error record, and using the time interval [Te,Tr] as the operation evaluation interval for conducting the corresponding operation stability evaluation of the drug dispensing control platform.

[0014] Preferably, step S3 includes:

[0015] Step S3-1: Suppose a certain job change node is caused by the i-th job record P in the job list. i and the (i+1)th job record P i+1 Composition, in which, in P i P i+1 The storage location coordinates of the corresponding drugs within are [a(P i ),b(P i )],[a(P i+1 ),b(P i+1 )]; where a(P i ) is Pi The storage layer number of the corresponding drug inside, a(P i+1 ) is the storage layer number of the corresponding drug inside P i+1 The storage layer number of the corresponding drug inside, b(P i ) is the storage layer number of the corresponding drug inside P i The storage column number of the corresponding drug inside, b(P i+1 ) is the storage column number of the corresponding drug inside P i+1 The storage column number of the corresponding drug inside;

[0016] Step S3-2: In the three-dimensional image model, connect the storage position coordinates [a(P i ), b(P i )] and the storage position coordinates [a(P i+1 ), b(P i+1 )] with a straight line to obtain a line segment L, extract the included angle β between the line segment L and the horizontal cabinet surface with the storage layer number a(P i ), where 90° ≥ β ≥ 0°, and calculate the state change coefficient f1 = sinβ of a certain operation change node; ​​​​​​​​​​​​​​​​​​​​​​​​​​​​L The shortest straight-line distance, or shortest path, between storage location coordinates [a(Pi), b(Pi)] and storage location coordinates [a(Pi+1), b(Pi+1)] is calculated when FS... L When the value is greater than δ, it means that the medicine-retrieving robot did not actually move along the path of line segment L in the actual scenario, and the actual distance traveled F is different from S. L The significant deviation indicates that directly transferring from storage location coordinates [a(Pi), b(Pi)] to storage location coordinates [a(Pi+1), b(Pi+1)] along the shortest straight-line distance is likely to be performance-limited from the perspective of the drug retrieval robot. In other words, the execution of this task change node is likely to be quite difficult in practice. When FS L When the value is less than δ, it means that in the actual scenario, the medicine retrieval robot moves along the line segment L or a path that is similar to the line segment L in terms of distance. This indicates that the direct transfer from the storage location coordinates [a(Pi), b(Pi)] to the storage location coordinates [a(Pi+1), b(Pi+1)] is completed in the direction of the shortest straight distance or the direction of the approximate shortest straight distance. From the perspective of the medicine retrieval robot, the possibility of being limited by performance is small. That is, the possibility of the operation change node being difficult to execute in practice is relatively small.

[0022] Preferably, step S5 includes:

[0023] Step S5-1: Organize the distribution of each work period in the work evaluation interval in chronological order, capture the interval between each two adjacent work periods, calculate the average interval Tw between all work periods, and obtain the total duration Tq covered by the work evaluation interval.

[0024] The smaller Tw is, the more frequently the drug dispensing control platform operates within the assessment period and the longer it remains in continuous operation. The smaller Tq is, the shorter the interval between newly added fault reports and historical fault reports that are close in time, indicating a higher frequency of fault reports on the drug dispensing control platform. The higher M / N is, the more frequently the drug dispensing control platform executes difficult operation change nodes within the assessment period.

[0025] Step S5-2: Accumulate the total number M of operation change nodes where the corresponding state change degree value η is greater than the degree threshold, and accumulate the total number N of operation change nodes executed by the drug retrieval robot within the operation evaluation interval;

[0026] Step S5-3: Calculate the operational performance index Φ of the drug control platform in the operational evaluation interval: Φ = 1 / Tw × 1 / Tq × (M / N).

[0027] To better implement the above methods, an automated drug dispensing management system is also proposed. The system includes an automated drug dispensing information acquisition module, a three-dimensional image information distribution and sorting module, a status change degree value evaluation and management module, an operation performance index evaluation and management module, and an early warning and prompting management module.

[0028] The automated drug dispensing information collection module is used to extract the operation evaluation interval of the drug dispensing control platform whenever a new fault error record is captured from the operation log of the drug dispensing control platform. Within the operation evaluation interval, it extracts each operation period in which the drug dispensing control platform is in continuous operation. It collects the drug dispensing records generated by the drug dispensing control platform in chronological order within each operation period to obtain the operation list of the drug retrieval robot of the drug dispensing control platform within each operation period. In the operation list, each operation record corresponds to a drug, and each two adjacent operation records constitute an operation change node of the drug retrieval robot.

[0029] The 3D image information distribution sorting module is used to simulate the intelligent medicine storage cabinet, establish a 3D image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list.

[0030] The State Change Degree Evaluation and Management Module is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates that constitute each job change node in the 3D image model, and to evaluate the state change degree value presented by the drug retrieval robot when executing each job change node in combination with the actual execution of each job change node by the drug retrieval robot.

[0031] The Operation Performance Index Evaluation and Management Module is used to analyze the interval distribution of operation periods within the operation evaluation period, as well as the frequency distribution of operation change nodes whose corresponding status change values ​​are greater than the threshold within each operation period, and to evaluate the operation performance index presented by the drug dispensing control platform within the operation evaluation period.

[0032] The early warning and notification management module is used to report an anomaly to the management personnel when the operation performance index of the drug dispensing control platform exceeds the index threshold during the operation evaluation period, prompting the management personnel to conduct performance testing on the drug dispensing control platform.

[0033] Preferably, the status change degree value assessment and management module includes a status change coefficient calculation unit and a status change degree value calculation unit;

[0034] The state change coefficient calculation unit is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates that constitute each job change node in the three-dimensional image model.

[0035] The state change degree value calculation unit is used to analyze the actual execution of the drug retrieval robot at each operation change node, and evaluate the state change degree value presented by the drug retrieval robot when executing each operation change node in combination with the state change coefficient.

[0036] Preferably, the job performance index evaluation and management module includes a distribution information sorting unit and a job performance index calculation unit;

[0037] The distribution information sorting unit is used to sort out the interval distribution of the work time periods presented within the work evaluation interval, as well as the frequency distribution of work change nodes whose corresponding status change degree values ​​are greater than the threshold within each work time period.

[0038] The operation performance index calculation unit is used to calculate the operation performance index of the drug dispensing control platform during the operation evaluation period.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention enables the detection of new fault error records on the dispensing control platform within the corresponding operation evaluation interval after each new fault error record is captured. It determines whether the new fault error record is due to performance degradation. The determination and analysis method is to analyze the continuous operation distribution of the dispensing robot of the dispensing control platform within the operation evaluation interval and the frequency distribution of corresponding operation change nodes with high execution difficulty. Ultimately, the operation performance index of the dispensing control platform in the operation evaluation interval is evaluated. The present invention can effectively monitor the aging or damage caused by the long-term operation of the dispensing control platform in a timely manner, promptly detect and deal with the performance degradation of the dispensing control platform, and ensure the normal operation of the outpatient department. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a flowchart illustrating an automated medication dispensing management method based on artificial intelligence according to the present invention.

[0042] Figure 2 This is a schematic diagram of the structure of an automated drug dispensing management system based on artificial intelligence according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figures 1-2 The present invention provides a technical solution: an automated medication dispensing management method based on artificial intelligence, the method comprising:

[0045] Step S1: Whenever a new fault error record is captured from the operation log of the drug dispensing control platform, the operation evaluation interval of the drug dispensing control platform is extracted, and each operation period when the drug dispensing control platform is in continuous operation is extracted within the operation evaluation interval.

[0046] Step S1 includes: obtaining the generation timestamp Tr of the newly added fault error record, extracting the historical fault error record closest to the newly added fault error record, obtaining the generation timestamp Te of the historical fault error record, and using the time interval [Te,Tr] as the operation evaluation interval for conducting the corresponding operation stability evaluation of the drug dispensing control platform.

[0047] Normally, the server of the dispensing control platform creates a dispensing list based on the prescription information sent by the hospital information system. At the same time, the dispensing control platform's drug retrieval robot responds to the dispensing list, identifies and retrieves the relevant drugs for each prescription on the dispensing list, and transports them to the dispensing port.

[0048] Specifically, when identifying whether the drug dispensing control platform is in a continuous operating state, it can be determined whether the drug retrieval robot, after transporting all the drugs corresponding to a prescription to the dispensing port, will proceed to retrieve the drugs for the next prescription or return to a standby state.

[0049] For example, if it is detected that the medication retrieval robot started retrieving and delivering all the medications involved in the first prescription at 13:42, and completed the retrieval and delivery of all the medications involved in the first prescription by 13:48, then started retrieving and delivering all the medications involved in the second prescription at 13:49, and completed the retrieval and delivery of all the medications involved in the second prescription by 13:54, then it is determined that the medication retrieval robot was in a continuous operating state from 13:42 to 13:54.

[0050] Step S2: Collect the drug dispensing records generated by the drug dispensing control platform in chronological order during each working period to obtain the work list completed by the drug retrieval robot of the drug dispensing control platform during each working period. Each work record in the work list corresponds to a drug, and each two adjacent work records constitute a work change node of the drug retrieval robot.

[0051] Step S3: Simulate the intelligent medicine storage cabinet, establish a three-dimensional image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list. Based on the distribution of the storage location coordinates that constitute each work change node in the three-dimensional image model, calculate the state change coefficient for each work change node.

[0052] Step S3 includes:

[0053] Step S3-1: Suppose a certain job change node is caused by the i-th job record P in the job list. i and the (i+1)th job record P i+1 Composition, in which, in P i P i+1 The storage location coordinates of the corresponding drugs within are [a(P i ),b(P i )],[a(P i+1 ),b(P i+1 )]; where a(P i ) is P i The number of storage layers for the corresponding drug, a(P) i+1 ) is P i+1 The number of storage layers for the corresponding drug, b(P) i ) is P i The number of storage columns for the corresponding drugs, b(P) i+1 ) is P i+1 The number of storage columns for the corresponding drugs;

[0054] Step S3-2: In the 3D image model, store the position coordinates [a(P i ),b(P i )] and storage location coordinates [a(P i+1 ),b(P i+1 Connect the lines to obtain line segment L. Extract line segment L and the storage layer number a(P). i The included angle β between the horizontal cabinet surfaces is given by 90°≥β≥0°. The state change coefficient f1=sinβ is calculated for a certain work change node.

[0055] Step S4: Considering the status change coefficient of each job change node and the actual execution of the medicine fetching robot for each job change node, evaluate the degree of status change presented by the medicine fetching robot when executing each job change node; where step 4 includes:

[0056] Step S4-1: Obtain the actual movement route of the medicine fetching robot moving from the storage position coordinates [a(P i ), b(P i )] to the storage position coordinates [a(P i+1 ), b(P i+1 )] according to the medicine delivery operation path planned by the medicine delivery control platform server, and the total time T consumed during the process, and obtain the distance S corresponding to the line segment L L ;

[0057] Step S4-2: Set weights A and B, where A < B; when F - S L > δ, the evaluated degree of status change η presented by the medicine fetching robot when executing a certain job change node is η = A × f1 × (F / T); when F - S L < δ, the evaluated degree of status change η presented by the medicine fetching robot when executing a certain job change node is η = B × f1 × (F / T);

[0058] Step S5: Sort out the interval distribution of the job time periods presented within the job evaluation interval, and the frequency distribution of the job change nodes with the corresponding degree of status change greater than the threshold within each job time period, and evaluate the job performance index presented by the medicine delivery control platform within the job evaluation interval;

[0059] where step S5 includes:

[0060] Step S5-1: Sort out the distribution of each job time period within the job evaluation interval in chronological order, capture the interval duration between every two adjacent job time periods, calculate the average interval duration Tw between all job time periods, and obtain the total duration Tq covered by the job evaluation interval;

[0061] Step S5-2: Accumulate the total number M of job change nodes with the corresponding degree of status change η greater than the degree threshold, and accumulate the total number N of job change nodes executed by the medicine fetching robot within the job evaluation interval;

[0062] Step S5-3: Calculate the job performance index Φ presented by the medicine delivery control platform within the job evaluation interval as Φ = 1 / Tw × 1 / Tq × (M / N);

[0063] Step S6: When the operational performance index of the drug dispensing control platform exceeds the index threshold during the operational evaluation period, an anomaly is reported to the management personnel, prompting them to conduct performance testing on the drug dispensing control platform.

[0064] To better implement the above methods, an automated drug dispensing management system is also proposed. The system includes an automated drug dispensing information acquisition module, a three-dimensional image information distribution and sorting module, a status change degree value evaluation and management module, an operation performance index evaluation and management module, and an early warning and prompting management module.

[0065] The automated drug dispensing information collection module is used to extract the operation evaluation interval of the drug dispensing control platform whenever a new fault error record is captured from the operation log of the drug dispensing control platform. Within the operation evaluation interval, it extracts each operation period in which the drug dispensing control platform is in continuous operation. It collects the drug dispensing records generated by the drug dispensing control platform in chronological order within each operation period to obtain the operation list of the drug retrieval robot of the drug dispensing control platform within each operation period. In the operation list, each operation record corresponds to a drug, and each two adjacent operation records constitute an operation change node of the drug retrieval robot.

[0066] The 3D image information distribution sorting module is used to simulate the intelligent medicine storage cabinet, establish a 3D image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list.

[0067] The State Change Degree Evaluation and Management Module is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates that constitute each job change node in the 3D image model, and to evaluate the state change degree value presented by the drug retrieval robot when executing each job change node in combination with the actual execution of each job change node by the drug retrieval robot.

[0068] The status change degree value assessment and management module includes a status change coefficient calculation unit and a status change degree value calculation unit.

[0069] The state change coefficient calculation unit is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates that constitute each job change node in the three-dimensional image model.

[0070] The state change degree value calculation unit is used to analyze the actual execution of the drug retrieval robot at each operation change node, and evaluate the state change degree value presented by the drug retrieval robot when executing each operation change node in combination with the state change coefficient.

[0071] The Operation Performance Index Evaluation and Management Module is used to analyze the interval distribution of operation periods within the operation evaluation period, as well as the frequency distribution of operation change nodes whose corresponding status change values ​​are greater than the threshold within each operation period, and to evaluate the operation performance index presented by the drug dispensing control platform within the operation evaluation period.

[0072] The job performance index evaluation and management module includes a distribution information sorting unit and a job performance index calculation unit.

[0073] The distribution information sorting unit is used to sort out the interval distribution of the work time periods presented within the work evaluation interval, as well as the frequency distribution of work change nodes whose corresponding status change degree values ​​are greater than the threshold within each work time period.

[0074] The operation performance index calculation unit is used to calculate the operation performance index of the drug dispensing control platform during the operation evaluation period;

[0075] The early warning and notification management module is used to report an anomaly to the management personnel when the operation performance index of the drug dispensing control platform exceeds the index threshold during the operation evaluation period, prompting the management personnel to conduct performance testing on the drug dispensing control platform.

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0077] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automated medication dispensing management method based on artificial intelligence, characterized in that, The method includes: Step S1: Whenever a new fault error record is captured from the operation log of the drug dispensing control platform, the operation evaluation interval of the drug dispensing control platform is extracted, and each operation period when the drug dispensing control platform is in continuous operation is extracted within the operation evaluation interval. Step S2: Collect the drug dispensing records generated by the drug dispensing control platform in chronological order during each working period to obtain the work list completed by the drug retrieval robot of the drug dispensing control platform during each working period. Each work record in the work list corresponds to a drug, and each two adjacent work records constitute a work change node of the drug retrieval robot. Step S3: Simulate the intelligent medicine storage cabinet, establish a three-dimensional image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list. Based on the distribution of the storage location coordinates that constitute each work change node in the three-dimensional image model, calculate the state change coefficient for each work change node. Step S4: Taking into account the state change coefficient of each job change node and the actual execution of the drug retrieval robot at each job change node, evaluate the degree of state change presented by the drug retrieval robot when executing each job change node. Step S5: Analyze the interval distribution of the work periods presented in the work evaluation interval, and the frequency distribution of work change nodes whose corresponding status change value is greater than the threshold in each work period, and evaluate the work performance index presented by the drug dispensing control platform in the work evaluation interval. Step S6: When the operation performance index of the drug dispensing control platform in the operation evaluation interval is greater than the index threshold, an anomaly is reported to the management personnel, prompting the management personnel to conduct performance testing on the drug dispensing control platform; Step S3 includes: Step S3-1: Suppose a certain job change node is caused by the i-th job record P in the job list. i and the (i+1)th job record P i+1 Composition, in which, in P i P i+1 The storage location coordinates of the corresponding drugs within are [a(P i ),b(P i )],[a(P i+1 ),b(P i+1 )]; where a(P i ) is P i The number of storage layers for the corresponding drug, a(P) i+1 ) is P i+1 The number of storage layers for the corresponding drug, b(P) i ) is P i The number of storage columns for the corresponding drugs, b(P) i+1 ) is P i+1 The number of storage columns for the corresponding drugs; Step S3-2: In the three-dimensional image model, store the position coordinates [a(P i ),b(P i )] and storage location coordinates [a(P i+1 ),b(P i+1 Connect the lines to obtain line segment L. Extract line segment L and the storage layer number a(P). i The included angle β between the horizontal cabinet surfaces is given by 90°≥β≥0°. The state change coefficient f1=sinβ is calculated for the certain operation change node. Step S4 includes: Step S4-1: Obtain the drug dispensing robot's dispensing path planned by the drug dispensing control platform server, and retrieve the storage location coordinates [a(P] from the operation change node. i ),b(P i Move to the storage location coordinates [a(P)] i+1 ),b(P i+1 The actual movement route of the line segment L and the total time T consumed during the process are used to obtain the distance S corresponding to the line segment L. L ; Step S4-2: Set weights A and B, where A < B; when F - S L > δ, the degree value η of the state change presented by the medicine dispensing robot when executing the certain operation change node is evaluated as η = A × f1 × (F / T); when F - S L < δ, the degree value η of the state change presented by the medicine dispensing robot when executing the certain operation change node is evaluated as η = B × f1 × (F / T).

2. The automated medication dispensing management method based on artificial intelligence according to claim 1, characterized in that, Step S1 includes: obtaining the generation timestamp Tr of the newly added fault error record, extracting the historical fault error record closest to the newly added fault error record, obtaining the generation timestamp Te of the historical fault error record, and using the time interval [Te,Tr] as the operation evaluation interval for conducting a corresponding operation stability evaluation of the drug dispensing control platform.

3. The automated medication dispensing management method based on artificial intelligence according to claim 1, characterized in that, Step S5 includes: Step S5-1: Organize the distribution of each work period in the work evaluation interval in chronological order, capture the interval between each two adjacent work periods, calculate the average interval Tw between all work periods, and obtain the total duration Tq covered by the work evaluation interval. Step S5-2: Accumulate the total number M of operation change nodes whose corresponding state change degree value η is greater than the degree threshold, and accumulate the total number N of operation change nodes executed by the drug retrieval robot within the operation evaluation interval; Step S5-3: Calculate the operational performance index Φ of the drug control platform in the operational evaluation interval: Φ = 1 / Tw × 1 / Tq × (M / N).

4. An automated medication dispensing management system for executing the automated medication dispensing management method based on artificial intelligence as described in any one of claims 1-3, characterized in that, The system includes an automated drug dispensing information acquisition module, a three-dimensional image information distribution and sorting module, a status change degree value evaluation and management module, an operation performance index evaluation and management module, and an early warning and prompting management module; The automated drug dispensing information collection module is used to trigger the extraction of the operation evaluation interval of the drug dispensing control platform whenever a new fault error record is captured from the operation log of the drug dispensing control platform, and to extract each operation period when the drug dispensing control platform is in continuous operation within the operation evaluation interval; and to collect the drug dispensing records generated by the drug dispensing control platform in chronological order within each operation period to obtain the operation list of the drug retrieval robot of the drug dispensing control platform within each operation period, wherein each operation record in the operation list corresponds to a drug, and each two adjacent operation records constitute an operation change node of the drug retrieval robot; The three-dimensional image information distribution sorting module is used to simulate the intelligent medicine storage cabinet, establish a three-dimensional image model of the intelligent medicine storage cabinet, and sequentially obtain the storage location coordinates of the corresponding medicine in each work record in the work list. The state change degree evaluation and management module is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates of each job change node in the three-dimensional image model, and evaluate the state change degree value presented by the drug retrieval robot when executing each job change node in combination with the actual execution of each job change node by the drug retrieval robot. The operation performance index evaluation and management module is used to sort out the interval distribution of operation periods presented in the operation evaluation interval, as well as the frequency distribution of operation change nodes whose corresponding status change degree value is greater than the threshold in each operation period, and evaluate the operation performance index presented by the drug dispensing control platform in the operation evaluation interval. The early warning and notification management module is used to report an anomaly to the management personnel when the operation performance index of the drug dispensing control platform in the operation evaluation interval is greater than the index threshold, and to prompt the management personnel to conduct performance testing on the drug dispensing control platform.

5. The automated medication dispensing management system according to claim 4, characterized in that, The status change degree value assessment and management module includes a status change coefficient calculation unit and a status change degree value calculation unit; The state change coefficient calculation unit is used to calculate the state change coefficient for each job change node based on the distribution of the storage location coordinates that constitute each job change node in the three-dimensional image model. The state change degree value calculation unit is used to analyze the actual execution of each operation change node by the drug retrieval robot, and evaluate the state change degree value presented by the drug retrieval robot when executing each operation change node in combination with the state change coefficient.

6. The automated medication dispensing management system according to claim 4, characterized in that, The job performance index evaluation and management module includes a distribution information sorting unit and a job performance index calculation unit; The distribution information sorting unit is used to sort out the interval distribution of work periods within the work evaluation interval, and the frequency distribution of work change nodes with corresponding status change values ​​greater than a threshold within each work period. The operation performance index calculation unit is used to calculate the operation performance index presented by the drug dispensing control platform in the operation evaluation interval.

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