Intelligent dispatching and management system of quick dispensing machine based on Internet of Things and big data

Abstract

The invention discloses an intelligent dispatching and management system for a quick dispensing machine based on the Internet of Things and big data, and relates to the technical field of dispensing. Comprising an Internet of Things sensing and collecting module which is used for collecting a medicine inventory state, equipment operation parameters and environment data information in real time and uploading the information to a cloud server in real time; the cloud server is used for receiving, storing and processing data from each dispensing machine; the intelligent scheduling module comprehensively considers the geographic position, the medicine stock, the current task load and the patient demand factor of the medicine dispensing machine according to the big data analysis result; and the monitoring and alarming module is used for monitoring the running state of the dispensing machine in real time, and the user interaction terminal is used for medical staff, pharmacists and managers to operate and inquire, and the system combines the Internet of Things sensing technology, big data analysis and intelligent algorithm optimization. The method is used for improving medicine distribution efficiency, reducing manual intervention and achieving dynamic environment adaptability.

Description

Technical Field

[0001] The present invention relates to the field of medicine dispensing technology, and specifically to an intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data. Background Art

[0002] In modern hospitals, dispensing medicines faces numerous challenges. Traditional manual dispensing methods are inefficient and prone to errors, and they also fail to meet patients' needs for quick medical treatment.

[0003] Existing hospital medication dispensing systems mostly rely on manual operations or semi-automated equipment, which presents the following problems:

[0004] Inefficiency: Traditional medicine dispensing machines are unable to dynamically adjust task allocation based on real-time demand, resulting in severe queues during peak hours.

[0005] Unintelligent scheduling: There is a lack of multi-machine collaborative optimization strategies, and task allocation relies on fixed rules, making it difficult to cope with complex scenarios.

[0006] Insufficient real-time monitoring: Drug inventory, equipment status, and environmental parameters are difficult to monitor in real time, which can easily lead to errors or delays in dispensing.

[0007] Delayed fault response: Equipment anomalies rely on manual troubleshooting, resulting in low maintenance efficiency and affecting overall system reliability.

[0008] To address the above problems, the present invention proposes an intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data through the collaborative design of the Internet of Things perception layer, the cloud-based intelligent scheduling engine and the big data analysis module to solve the above problems. Summary of the Invention

[0009] The present invention provides an intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data to solve the problems in the background technology.

[0010] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent scheduling and management system for rapid medication dispensing machines based on the Internet of Things and big data, comprising:

[0011] The IoT sensing and acquisition module, consisting of a sensor array and communication module deployed inside the dispensing machine, is used to collect real-time information on drug inventory status, equipment operating parameters, and environmental data and upload it to the cloud server in real time;

[0012] The cloud server is connected to the IoT sensing and acquisition module to receive, store, and process data from each dispensing machine. It also generates dispatch instructions for the dispensing machines based on the preset intelligent dispatch module, thus realizing intelligent dispatch and management of the dispensing machines.

[0013] The intelligent scheduling module, deployed on the cloud server, generates the optimal dispensing task allocation plan based on big data analysis results, taking into account the geographical location of the dispensing machines, drug inventory, current task volume, and patient demand, and issues task instructions to the corresponding dispensing machines.

[0014] The monitoring and alarm module is connected to the cloud server and is used to monitor the operating status of the dispensing machine in real time. When a malfunction of the dispensing machine, insufficient drug inventory, or other abnormal conditions are detected, an alarm message is issued in a timely manner, and relevant personnel are notified to handle the situation.

[0015] User interaction terminal: Communicates with the cloud server and allows medical staff, pharmacists and management personnel to operate and query, including issuing medication dispensing tasks, querying medication inventory, querying medication dispensing records and setting system parameters.

[0016] Furthermore, the sensor array includes a weight sensor, an RFID tag reader, and a temperature and humidity sensor;

[0017] The medicine dispensing machine also includes a medicine storage module, a medicine dispensing execution module and a human-computer interaction interface;

[0018] The environmental data information includes the temperature and humidity, crowd density and emergency indication of the area where the medicine dispenser is located.

[0019] Furthermore, the medicine storage module is used to store medicines of different types and specifications in a classified manner, and each medicine storage location has a unique RFID tag and a corresponding sensor for monitoring the type, quantity, expiration date and storage location of the medicine;

[0020] The medicine dispensing execution module accurately takes out the corresponding medicine from the medicine storage module according to the received task instructions and delivers the medicine to the medicine dispensing port;

[0021] The human-computer interaction interface is used for medical staff to interact with the medicine dispensing machine. It can display the status information of the medicine dispensing machine, medicine inventory information, and medicine dispensing records, and supports task reception, confirmation, and abnormal situation processing functions.

[0022] Furthermore, the edge computing node module has an embedded priority evaluation algorithm to perform real-time feature extraction and urgency classification on medication dispensing requests. The priority evaluation algorithm adopts a multi-dimensional weighted scoring model, and the priority scoring formula is:

[0023] P=α·E+β·og(1+D)+γ·S;

[0024] Where: E represents the prescription urgency coefficient (0-1); it is obtained by mapping the patient's vital signs data through the Sigmoid function;

[0025] D is the inverse of the drug demand time window; S is the correction value of the drug's special properties (S = 1.2 for refrigerated drugs, S = 1.0 for conventional drugs); α, β, γ are dynamic weight coefficients, satisfying α + β + γ = 1. The optimal combination is obtained through historical data training.

[0026] Furthermore, the cloud scheduling engine module builds a dynamic scheduling model based on an improved genetic algorithm to optimize the multi-machine collaborative operation path. The dynamic scheduling model adopts a two-layer optimization architecture: the upper layer path planning uses an improved genetic algorithm, and the fitness function is defined as:

[0027]

[0028] Where: T k represents the task completion time of the k-th dispensing machine; Q max is the maximum queue length of the system, η is the penalty coefficient (the empirical value is 0.8), and the lower-layer real-time scheduling adopts a dynamic window algorithm. The constraints include:

[0029]

[0030] Where: v(t) is the real-time velocity of the robot arm; a is the maximum acceleration; d(t) is the current safety distance.

[0031] Furthermore, the big data analysis module includes:

[0032] The drug demand forecasting module analyzes historical drug dispensing data, seasonal factors, disease epidemic trends, and multi-dimensional data on patient flow to predict the demand for various drugs in different time periods and departments, providing a decision-making basis for drug inventory management of drug dispensing machines;

[0033] The medication dispensing task scheduling optimization module takes minimizing medication dispensing time and maximizing medication dispensing efficiency as its objective function. It establishes a mathematical model based on the geographical location of medication dispensing machines, medication inventory distribution, and task urgency, and applies operations research methods to solve the optimal medication dispensing task allocation plan.

[0034] The fault prediction and diagnosis module, based on the historical operating data and real-time monitoring data of the dispensing machine, establishes a fault model through machine learning algorithms, predicts the possible fault types and fault times of the dispensing machine in advance, and quickly diagnoses and locates the faults that have occurred, thereby improving the reliability and maintenance efficiency of the equipment.

[0035] Furthermore, the cloud server includes:

[0036] Data storage unit: adopts a distributed database architecture, capable of storing massive amounts of historical and real-time data from the dispensing machine, including drug in and out records, dispensing task details, equipment operation logs, and medical staff's operation records;

[0037] Data cleaning and preprocessing unit: performs cleaning, denoising, normalization, and missing value filling preprocessing operations on the collected raw data to improve the quality and usability of the data;

[0038] Data analysis and mining unit: This unit conducts in-depth analysis of processed data to uncover valuable information on drug usage trends, peak dispensing times, equipment failure patterns, and medical staff operating habits, providing a basis for decision-making in intelligent scheduling algorithms.

[0039] Intelligent scheduling algorithm unit: Based on the results of data analysis and mining, an intelligent scheduling model for dispensing machines is established. Taking into account factors such as drug inventory, the urgency of the dispensing task, the performance of the dispensing machine, and the distance from the patient's location, dynamic programming and optimization algorithms are used to generate the optimal dispatching instructions for the dispensing machine.

[0040] Furthermore, the intelligent scheduling module includes:

[0041] Task allocation submodule: Based on the dispatch instructions generated by the big data processing center, the drug dispensing tasks are rationally allocated to different dispensing machines, with priority given to dispensing machines that are close to the patient, have sufficient drug inventory, and are idle;

[0042] Path planning submodule: For complex dispensing tasks that require the collaboration of multiple dispensing machines, it plans the optimal dispensing route to ensure that the medicine can be delivered to the patient as quickly as possible while avoiding conflicts and congestion between dispensing machines.

[0043] Task adjustment submodule: real-time monitoring of the execution status and emergencies of the medicine dispensing machine.

[0044] Furthermore, the monitoring and alarm module includes:

[0045] Real-time monitoring submodule: Through the cameras, sensors and status indicator devices installed on the dispenser, the operating status of the dispenser is monitored in real time 24 hours a day. The monitoring images and data are transmitted to the cloud server and user interaction terminal in real time, so that managers can understand the working status of the dispenser at any time.

[0046] Fault diagnosis submodule: Based on the dispenser's historical fault data and real-time operating status data, it automatically diagnoses and warns of possible faults, identifies potential fault hazards in advance, and provides maintenance personnel with fault causes and repair suggestions;

[0047] Alarm notification submodule: When the dispensing machine fails, the medicine expires, the inventory is insufficient or an emergency occurs, an alarm signal can be promptly sent to relevant personnel through sound and light alarms, SMS notifications, and email reminders to ensure that the problem can be handled in a timely manner.

[0048] Furthermore, it also includes:

[0049] Mobile terminal application: Communicates with the cloud server, allowing medical staff and pharmacists to receive medication dispensing task reminders, check medication dispensing progress and drug inventory status on mobile devices anytime and anywhere, and remotely perform simple operations on the dispensing machine;

[0050] The data interaction interface is used to interact with the hospital's HIS system to obtain patients' prescription information and medical order information, and to feed back medication dispensing records to the HIS system to achieve real-time synchronization and sharing of information.

[0051] Compared with the existing technology, the present invention provides an intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data, which has the following beneficial effects:

[0052] The intelligent dispatching and management system for rapid medicine dispensing machines based on the Internet of Things and big data combines Internet of Things sensing technology, big data analysis and intelligent algorithm optimization to improve the efficiency of medicine distribution, reduce manual intervention and achieve dynamic environmental adaptability, thereby achieving the goals of improving efficiency and reducing error rates, significantly improving medicine dispensing efficiency and system reliability, reducing labor costs, and having significant commercial promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of 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 paying any creative work.

[0054] Figure 1 This is a schematic diagram of the control system of the intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data of the present invention;

[0055] Figure 2 Schematic diagram of the cloud server system of the intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data of the present invention;

[0056] Figure 3 This is a schematic diagram of the big data analysis module system of the intelligent scheduling and management system of rapid medicine dispensing machines based on the Internet of Things and big data of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0060] See also Figure 1-3 The present invention discloses an intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data, including:

[0061] The IoT sensing and acquisition module, consisting of a sensor array and communication module deployed inside the dispensing machine, is used to collect real-time information on drug inventory status, equipment operating parameters, and environmental data and upload it to the cloud server in real time;

[0062] The cloud server is connected to the IoT sensing and acquisition module to receive, store, and process data from each dispensing machine. It also generates dispatch instructions for the dispensing machines based on the preset intelligent dispatch module, thus realizing intelligent dispatch and management of the dispensing machines.

[0063] The intelligent scheduling module, deployed on the cloud server, generates the optimal dispensing task allocation plan based on big data analysis results, taking into account the geographical location of the dispensing machines, drug inventory, current task volume, and patient demand, and issues task instructions to the corresponding dispensing machines.

[0064] The monitoring and alarm module is connected to the cloud server and is used to monitor the operating status of the dispensing machine in real time. When a malfunction of the dispensing machine, insufficient drug inventory, or other abnormal conditions are detected, an alarm message is issued in a timely manner, and relevant personnel are notified to handle the situation.

[0065] User interaction terminal: Communicates with the cloud server and allows medical staff, pharmacists and management personnel to operate and query, including issuing medication dispensing tasks, querying medication inventory, querying medication dispensing records and setting system parameters.

[0066] Specifically, the sensor array includes a weight sensor, an RFID tag reader and a temperature and humidity sensor; the medicine dispensing machine also includes a medicine storage module, a medicine dispensing execution module and a human-computer interaction interface; the environmental data information includes the temperature and humidity, crowd density and emergency indication of the area where the medicine dispensing machine is located.

[0067] Specifically, the medicine storage module is used to store medicines of different types and specifications in a classified manner. Each medicine storage location has a unique RFID tag and a corresponding sensor for monitoring the type, quantity, expiration date and storage location of the medicine.

[0068] The medicine dispensing execution module accurately takes out the corresponding medicine from the medicine storage module according to the received task instructions and delivers the medicine to the medicine dispensing port;

[0069] The human-computer interaction interface is used for medical staff to interact with the medicine dispensing machine. It can display the status information of the medicine dispensing machine, medicine inventory information, and medicine dispensing records, and supports task reception, confirmation, and abnormal situation processing functions.

[0070] Specifically, the edge computing node module has an embedded priority evaluation algorithm to perform real-time feature extraction and urgency classification on medication dispensing requests. The priority evaluation algorithm adopts a multi-dimensional weighted scoring model, and the priority scoring formula is:

[0071] P=α·E+β·log(1+d)+γ·S;

[0072] Where: E represents the prescription urgency coefficient (0-1); it is obtained by mapping the patient's vital signs data through the Sigmoid function;

[0073] D is the inverse of the drug demand time window; S is the correction value of the drug's special properties (S = 1.2 for refrigerated drugs, S = 1.0 for conventional drugs); α, β, γ are dynamic weight coefficients, satisfying α + β + γ = 1. The optimal combination is obtained through historical data training.

[0074] Specifically, the cloud scheduling engine module builds a dynamic scheduling model based on an improved genetic algorithm to optimize the multi-machine collaborative operation path. The dynamic scheduling model adopts a two-layer optimization architecture: the upper layer path planning uses an improved genetic algorithm, and the fitness function is defined as:

[0075]

[0076] Where: T k represents the task completion time of the k-th dispensing machine; Q max is the maximum queue length of the system, η is the penalty coefficient (the empirical value is 0.8), and the lower-layer real-time scheduling adopts a dynamic window algorithm. The constraints include:

[0077]

[0078] Where: v(t) is the real-time velocity of the robot arm; a is the maximum acceleration; d(t) is the current safety distance.

[0079] Specifically, the big data analysis module includes:

[0080] The drug demand forecasting module analyzes historical drug dispensing data, seasonal factors, disease epidemic trends, and multi-dimensional data on patient flow to predict the demand for various drugs in different time periods and departments, providing a decision-making basis for drug inventory management of drug dispensing machines;

[0081] The medication dispensing task scheduling optimization module takes minimizing medication dispensing time and maximizing medication dispensing efficiency as its objective function. It establishes a mathematical model based on the geographical location of medication dispensing machines, medication inventory distribution, and task urgency, and applies operations research methods to solve the optimal medication dispensing task allocation plan.

[0082] The fault prediction and diagnosis module, based on the historical operating data and real-time monitoring data of the dispensing machine, establishes a fault model through machine learning algorithms, predicts the possible fault types and fault times of the dispensing machine in advance, and quickly diagnoses and locates the faults that have occurred, thereby improving the reliability and maintenance efficiency of the equipment.

[0083] The big data analysis module uses a time series prediction model to predict drug demand and dynamically allocates inventory through a resource constraint programming model. The resource constraint programming model uses mixed integer linear programming, and the objective function is:

[0084]

[0085] Constraints:

[0086]

[0087] x i ∈{0,1},y j ≥0;

[0088] Where: x i represents the replenishment decision variable for drug i, y j represents the storage capacity of shelf j, c i is the replenishment cost, h j is the storage cost, d k To predict demand, the model is solved using the branch and bound method combined with column generation technology.

[0089] Specifically, the cloud server includes:

[0090] Data storage unit: adopts a distributed database architecture, capable of storing massive amounts of historical and real-time data from the dispensing machine, including drug in and out records, dispensing task details, equipment operation logs, and medical staff's operation records;

[0091] Data cleaning and preprocessing unit: performs cleaning, denoising, normalization, and missing value filling preprocessing operations on the collected raw data to improve the quality and usability of the data;

[0092] Data analysis and mining unit: This unit conducts in-depth analysis of processed data to uncover valuable information on drug usage trends, peak dispensing times, equipment failure patterns, and medical staff operating habits, providing a basis for decision-making in intelligent scheduling algorithms.

[0093] Intelligent scheduling algorithm unit: Based on the results of data analysis and mining, an intelligent scheduling model for dispensing machines is established. Taking into account factors such as drug inventory, the urgency of the dispensing task, the performance of the dispensing machine, and the distance from the patient's location, dynamic programming and optimization algorithms are used to generate the optimal dispatching instructions for the dispensing machine.

[0094] Specifically, the intelligent scheduling module includes:

[0095] Task allocation submodule: Based on the dispatch instructions generated by the big data processing center, the drug dispensing tasks are rationally allocated to different dispensing machines, with priority given to dispensing machines that are close to the patient, have sufficient drug inventory, and are idle;

[0096] Path planning submodule: For complex dispensing tasks that require the collaboration of multiple dispensing machines, it plans the optimal dispensing route to ensure that the medicine can be delivered to the patient as quickly as possible while avoiding conflicts and congestion between dispensing machines.

[0097] Task adjustment submodule: Real-time monitoring of the execution status and emergencies of the dispensing machine, such as dispensing machine failure, temporary drug shortage or patient location change, and timely dynamic adjustment of the dispensing task, reallocation of tasks or adjustment of the dispensing route to ensure the continuity and accuracy of the dispensing process.

[0098] Specifically, the monitoring and alarm module includes:

[0099] Real-time monitoring submodule: Through the cameras, sensors and status indicator devices installed on the dispenser, the operating status of the dispenser is monitored in real time 24 hours a day. The monitoring images and data are transmitted to the cloud server and user interaction terminal in real time, so that managers can understand the working status of the dispenser at any time.

[0100] Fault diagnosis submodule: Based on the dispenser's historical fault data and real-time operating status data, it automatically diagnoses and warns of possible faults, identifies potential fault hazards in advance, and provides maintenance personnel with fault causes and repair suggestions;

[0101] Alarm notification submodule: When the dispensing machine fails, the medicine expires, the stock is insufficient, or an emergency occurs, an alarm signal can be promptly sent to relevant personnel through multiple means such as sound and light alarms, SMS notifications, and email reminders to ensure that the problem can be handled in a timely manner;

[0102] The monitoring and alarm module adopts a multi-level alarm mechanism, and sets different alarm levels and notification methods according to the severity and urgency of the fault or abnormality, including sound alarm, light alarm, SMS notification and system pop-up prompt, to ensure timely and effective notification of relevant personnel for processing.

[0103] Specifically, the user interaction terminal includes:

[0104] Medication dispensing task management interface: Medical staff can quickly issue medication dispensing tasks on this interface, specify patient information, medication type, quantity, and dispensing time parameters, and view the execution progress and status of medication dispensing tasks in real time;

[0105] Drug inventory query interface: Pharmacists can use this interface to query the drug inventory status of each dispensing machine, including detailed information on drug type, quantity, expiration date and storage location, so as to timely replenish drugs and manage inventory;

[0106] Medication dispensing record query interface: Management personnel and medical staff can query historical medication dispensing records, including dispensing time, dispensing machine number, patient information, medication information, and dispensing personnel, facilitating statistical analysis and quality traceability of medication dispensing;

[0107] System parameter setting interface: Users with corresponding permissions can set and adjust various system parameters on this interface, such as the operating parameters of the dispensing machine, parameters of the intelligent scheduling algorithm, alarm thresholds, and user permission management.

[0108] Specifically, the mobile terminal application has the following functions:

[0109] Medication dispensing task reminder function: When a new medication dispensing task is issued, a reminder notification is automatically sent to the mobile terminals of relevant medical staff and pharmacists to ensure that the medication dispensing task can be processed in time;

[0110] Real-time tracking of medication dispensing progress: Medical staff and pharmacists can use mobile terminals to view the progress of medication dispensing tasks in real time, including medication preparation status, dispensing machine working status, and estimated completion time, facilitating advance preparation.

[0111] Remote operation and control function: In emergency situations, medical staff and pharmacists can remotely control the dispensing machine through mobile terminals, such as pausing dispensing, adjusting dispensing speed, and reallocating dispensing tasks to cope with emergencies;

[0112] Information query and statistics function: Users can query drug inventory information, dispensing records and personal operation history on mobile terminals, and generate corresponding statistical reports to facilitate data analysis and decision support.

[0113] Specifically, the medicine dispensing machine also includes an identity recognition and authorization module, which is used to perform identity recognition and authority verification on medical staff and pharmacists who use the medicine dispensing machine. Only personnel who have passed the identity authentication and have the corresponding operating authority can operate the medicine dispensing machine to ensure the safety and compliance of the medicine dispensing process; the identity recognition and authorization module adopts a combination of one or more identity recognition technologies such as fingerprint recognition, face recognition, password verification or smart card recognition to improve the accuracy and reliability of identity recognition.

[0114] Specifically, it also includes:

[0115] Mobile terminal application: Communicates with the cloud server, allowing medical staff and pharmacists to receive medication dispensing task reminders, check medication dispensing progress and drug inventory status anytime and anywhere on their mobile devices, and remotely perform simple operations on the dispensing machine, such as pausing medication dispensing and adjusting the dispensing speed.

[0116] The data interaction interface is used to interact with the hospital's HIS system (hospital information system), obtain patients' prescription information and medical order information, and feed back medication dispensing records to the HIS system to achieve real-time synchronization and sharing of information.

[0117] The intelligent scheduling and management method of rapid medicine dispensing machines based on the Internet of Things and big data includes the following steps:

[0118] Step 1: The rapid dispensing machine uploads its operating status, drug inventory information, and dispensing record data to the cloud server in real time through the IoT communication module;

[0119] Step 2: The cloud server receives and stores data from each dispensing machine, uses big data analysis algorithms to mine and analyze the data, and generates data mining results;

[0120] Step 3: The scheduling management module generates the optimal dispensing task allocation plan based on the data mining results, taking into account the geographical location of the dispensing machine, drug inventory, current task volume, and patient demand factors;

[0121] Step 4: The cloud server sends the drug dispensing task allocation plan to the corresponding rapid drug dispensing machine. After receiving the task instruction, the drug dispensing machine completes the drug dispensing operation through the drug dispensing execution module;

[0122] Step 5: The monitoring and alarm module monitors the operating status of the dispensing machine in real time. When an abnormal situation is detected, an alarm message is issued in time and relevant personnel are notified to handle it;

[0123] Step 6: The data interaction interface interacts with the hospital HIS system to achieve real-time synchronization and sharing of information, ensuring the continuity and accuracy of the entire medical process.

[0124] In step 3, when generating the drug dispensing task allocation plan, the scheduling management module also considers the maintenance plan of the drug dispensing machine, the expiration date of the drug, and the management requirements of special drugs to ensure the rationality and compliance of the drug dispensing process.

[0125] In step 5, the monitoring and alarm module also performs real-time video monitoring of the drug dispensing process and stores the video data in the cloud server for backtracking and analysis when necessary, further improving the transparency and traceability of the drug dispensing process.

[0126] In step 6, the data interaction interface is also regularly tested and maintained to ensure the stability and reliability of data interaction and to avoid data transmission problems affecting the normal operation of the entire system.

[0127] To sum up, the intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data, which combines the Internet of Things sensing technology, big data analysis and intelligent algorithm optimization, is used to improve the efficiency of medicine distribution, reduce manual intervention and realize dynamic environmental adaptability, so as to achieve the purpose of improving efficiency and reducing error rate, significantly improve medicine dispensing efficiency and system reliability, reduce labor costs, and have significant commercial promotion value.

[0128] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data, characterized by: include: The IoT sensing and acquisition module, consisting of a sensor array and communication module deployed inside the dispensing machine, is used to collect real-time information on drug inventory status, equipment operating parameters, and environmental data and upload it to the cloud server in real time; The cloud server is connected to the IoT sensing and acquisition module to receive, store, and process data from each dispensing machine. It also generates dispatch instructions for the dispensing machines based on the preset intelligent dispatch module, thus realizing intelligent dispatch and management of the dispensing machines. The intelligent scheduling module, deployed on the cloud server, generates the optimal dispensing task allocation plan based on big data analysis results, taking into account the geographical location of the dispensing machines, drug inventory, current task volume, and patient demand, and issues task instructions to the corresponding dispensing machines. The monitoring and alarm module is connected to the cloud server and is used to monitor the operating status of the dispensing machine in real time. When a malfunction of the dispensing machine, insufficient drug inventory, or other abnormal conditions are detected, an alarm message is issued in a timely manner, and relevant personnel are notified to handle the situation. The user interaction terminal is connected to the cloud server for medical staff, pharmacists and management personnel to operate and query, including issuing drug dispensing tasks, drug inventory query, drug dispensing record query and system parameter setting functions.

2. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The sensor array includes a weight sensor, an RFID tag reader, and a temperature and humidity sensor; The medicine dispensing machine also includes a medicine storage module, a medicine dispensing execution module and a human-computer interaction interface; The environmental data information includes the temperature and humidity, crowd density and emergency indication of the area where the medicine dispenser is located.

3. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 2 is characterized by: The medicine storage module is used to store medicines of different types and specifications in a classified manner. Each medicine storage location has a unique RFID tag and a corresponding sensor for monitoring the type, quantity, expiration date and storage location of the medicine. The medicine dispensing execution module accurately takes out the corresponding medicine from the medicine storage module according to the received task instructions and delivers the medicine to the medicine dispensing port; The human-computer interaction interface is used for medical staff to interact with the medicine dispensing machine. It can display the status information of the medicine dispensing machine, medicine inventory information, and medicine dispensing records, and supports task reception, confirmation, and abnormal situation processing functions.

4. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The edge computing node module has an embedded priority evaluation algorithm to perform real-time feature extraction and urgency classification on medication dispensing requests. The priority evaluation algorithm adopts a multi-dimensional weighted scoring model. The priority scoring formula is: P=α·E+β·og(1+D)+γ·S; Where: E represents the prescription urgency coefficient (0-1); it is obtained by mapping the patient's vital signs data through the Sigmoid function; D is the inverse of the drug demand time window; S is the correction value of the drug's special properties (S = 1.2 for refrigerated drugs, S = 1.0 for conventional drugs); α, β, γ are dynamic weight coefficients, satisfying α + β + γ = 1. The optimal combination is obtained through historical data training.

5. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The cloud scheduling engine module builds a dynamic scheduling model based on an improved genetic algorithm to optimize the multi-machine collaborative operation path. The dynamic scheduling model adopts a two-layer optimization architecture: the upper layer path planning uses an improved genetic algorithm, and the fitness function is defined as: Where: T k represents the task completion time of the k-th dispensing machine; Q max is the maximum queue length of the system, η is the penalty coefficient (the empirical value is 0.8), and the lower-layer real-time scheduling adopts a dynamic window algorithm. The constraints include: Where: v(t) is the real-time velocity of the robot arm; a is the maximum acceleration; d(t) is the current safety distance.

6. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The big data analysis module includes: The drug demand forecasting module analyzes historical drug dispensing data, seasonal factors, disease epidemic trends, and multi-dimensional data on patient flow to predict the demand for various drugs in different time periods and departments, providing a decision-making basis for drug inventory management of drug dispensing machines; The medication dispensing task scheduling optimization module takes minimizing medication dispensing time and maximizing medication dispensing efficiency as its objective function. It establishes a mathematical model based on the geographical location of medication dispensing machines, medication inventory distribution, and task urgency, and applies operations research methods to solve the optimal medication dispensing task allocation plan. The fault prediction and diagnosis module, based on the historical operating data and real-time monitoring data of the dispensing machine, establishes a fault model through machine learning algorithms, predicts the possible fault types and fault times of the dispensing machine in advance, and quickly diagnoses and locates the faults that have occurred, thereby improving the reliability and maintenance efficiency of the equipment.

7. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The cloud server includes: Data storage unit: adopts a distributed database architecture, capable of storing massive amounts of historical and real-time data from the dispensing machine, including drug in and out records, dispensing task details, equipment operation logs, and medical staff's operation records; Data cleaning and preprocessing unit: performs cleaning, denoising, normalization, and missing value filling preprocessing operations on the collected raw data to improve the quality and usability of the data; Data analysis and mining unit: This unit conducts in-depth analysis of processed data to uncover valuable information on drug usage trends, peak dispensing times, equipment failure patterns, and medical staff operating habits, providing a basis for decision-making in intelligent scheduling algorithms. Intelligent scheduling algorithm unit: Based on the results of data analysis and mining, an intelligent scheduling model for dispensing machines is established. Taking into account factors such as drug inventory, the urgency of the dispensing task, the performance of the dispensing machine, and the distance from the patient's location, dynamic programming and optimization algorithms are used to generate the optimal dispatching instructions for the dispensing machine.

8. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The intelligent scheduling module includes: Task allocation submodule: Based on the dispatch instructions generated by the big data processing center, the drug dispensing tasks are rationally allocated to different dispensing machines, with priority given to dispensing machines that are close to the patient, have sufficient drug inventory, and are idle; Path planning submodule: For complex dispensing tasks that require the collaboration of multiple dispensing machines, it plans the optimal dispensing route to ensure that the medicine can be delivered to the patient as quickly as possible while avoiding conflicts and congestion between dispensing machines. Task adjustment submodule: real-time monitoring of the execution status and emergencies of the medicine dispensing machine.

9. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: The monitoring and alarm module includes: Real-time monitoring submodule: Through the cameras, sensors and status indicator devices installed on the dispenser, the operating status of the dispenser is monitored in real time 24 hours a day. The monitoring images and data are transmitted to the cloud server and user interaction terminal in real time, so that managers can understand the working status of the dispenser at any time. Fault diagnosis submodule: Based on the dispenser's historical fault data and real-time operating status data, it automatically diagnoses and warns of possible faults, identifies potential fault hazards in advance, and provides maintenance personnel with fault causes and repair suggestions; Alarm notification submodule: When the dispensing machine fails, the medicine expires, the inventory is insufficient or an emergency occurs, an alarm signal can be promptly sent to relevant personnel through sound and light alarms, SMS notifications, and email reminders to ensure that the problem can be handled in a timely manner.

10. The intelligent scheduling and management system for rapid medicine dispensing machines based on the Internet of Things and big data according to claim 1 is characterized by: Also included are: Mobile terminal application: Communicates with the cloud server, allowing medical staff and pharmacists to receive medication dispensing task reminders, check medication dispensing progress and drug inventory status on mobile devices anytime and anywhere, and remotely perform simple operations on the dispensing machine; The data interaction interface is used to interact with the hospital's HIS system to obtain patients' prescription information and medical order information, and to feed back medication dispensing records to the HIS system to achieve real-time synchronization and sharing of information.

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