A cloud computing-based internal medicine nursing resource optimization system

Abstract

The present invention discloses a cloud computing-based internal medicine nursing resource optimization system, which relates to the technical field of nursing resource optimization, obtains the medical record data of each patient, collects all personal medical record feature information data, outputs them as a medical record feature set, collects and stores drug inventory information data, establishes a personal drug demand model based on the personal medical record feature information data, establishes a total drug demand model based on the medical record feature set, generates a scheduling plan based on the total drug demand model, and establishes a drug emergency allocation mechanism based on the drug inventory information data. By integrating patient and drug data, the present invention realizes the precise connection between dynamic scheduling and drug demand, optimizes drug distribution and storage, and significantly reduces drug waste. At the same time, a drug inventory allocation model is formulated to ensure rapid allocation of drugs in emergency situations, improves work efficiency and the safety and reliability of medical services, and provides patients with more efficient and high-quality internal medicine nursing services.

Description

Technical Field

[0001] The present invention relates to the technical field of display nursing resource optimization, and in particular to an internal medicine nursing resource optimization system based on cloud computing. Background Art

[0002] With the continuous advancement of medical technology and the development of medical information technology, internal medicine nursing work faces increasing challenges. Traditional internal medicine nursing management methods are plagued by information silos, uneven resource allocation, and inefficient medication management. These issues increase the workload of nursing staff, lead to frequent medication waste, and reduce patient satisfaction.

[0003] At present, the Chinese invention patent with application number CN202311409785.3 discloses a nursing intelligent information management system that realizes multi-level linkage resource interaction, involving the field of information management technology, including an information collection unit, an interactive storage unit, a data processing unit and an optimization management unit. The resource interaction unit includes an information storage module and an information call module. The patient's condition information is stored and managed through the information collection unit, and multi-level linkage and resource interaction of information call are realized, thereby realizing the system's integrated management of condition information, and ensuring the privacy and security of patient information, facilitating supervision and data traceability, and classifying and refining the patient's physical parameters through the data processing unit, and realizing automatic optimization and upgrading of the system through the optimization management unit. The optimized resource interaction unit can further improve the accuracy of condition monitoring, ensure timely adjustment of personalized nursing plans, thereby improving the efficiency and effectiveness of nursing work, and providing patients with more accurate, efficient and high-quality nursing services.

[0004] The above technology makes it difficult to design a scheduling plan for nursing staff based on the quantity and location of medicines and the patient's medication usage. It is also difficult to arrange different numbers of nursing staff during different medication demand periods, which affects the patient's medical experience and the efficiency of nursing staff. Summary of the Invention

[0005] The technical problem solved by the present invention is that the above technology makes it difficult to design a scheduling plan for nursing staff based on the quantity and location of medicines and the patient's medication situation, and it is difficult to arrange different numbers of nursing staff during different medication demand periods, which affects the patient's medical experience and the efficiency of nursing staff.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A cloud computing-based internal medicine nursing resource optimization system, including a patient data integration module, a drug data analysis module, a nursing staff typesetting module, and a drug sharing control module;

[0008] The patient data integration module is used to obtain the medical record data of each patient, pre-process the medical record data of each patient, extract feature information, output it as individual medical record feature information data, aggregate all individual medical record feature information data, output it as a medical record feature set, and collect and store drug inventory information data;

[0009] The drug data analysis module is used to establish an individual drug demand model based on individual medical record feature information data, and to establish an overall drug demand model based on the medical record feature set;

[0010] The nursing staff scheduling module is used to generate a scheduling plan based on the total drug demand model;

[0011] The drug sharing mechanism module is used to establish a drug emergency allocation mechanism based on drug inventory information data.

[0012] Preferably, the patient data integration module includes a patient information management unit and a drug inventory management unit;

[0013] The patient information management unit is used to obtain the medical record data of each patient, pre-process the medical record data of each patient, the pre-processing including denoising and removing outliers, extracting feature information, the feature information including name data, bed number data, ward number data, medication name data, medication time data, medication course data, and medication dosage data, output the feature information as personal medical record feature information data, and output the collection of personal medical record feature information data of all patients as a medical record feature set;

[0014] The drug inventory management unit is used to obtain drug inventory information data, which includes drug name data, drug quantity data and drug location nursing station data, and store the drug inventory information data.

[0015] Preferably, the drug data analysis module includes a personal demand modeling unit and an overall demand modeling unit;

[0016] The personal demand modeling unit is used to establish a personal drug demand model based on personal medical record characteristic information data, and the personal drug demand model includes first data input, first data update and first data query;

[0017] First data input: input name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data;

[0018] First data update: manually update name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data;

[0019] First data query: input name data, bed number data and ward number data to query medication name data, medication time data, medication course data and medication dosage data.

[0020] Preferably, the overall demand modeling unit is used to establish an overall drug demand model based on the medical record feature set, and the overall drug demand model includes second data input, second data update and second data query;

[0021] Second data input: input all name data, bed number data, ward number data, medication name data, medication time data and medication dosage data in the medical record feature set;

[0022] Second data update: manually update all name data, bed number data, ward number data, medication name data, medication time data, medication course data, and medication dosage data in the medical record feature set;

[0023] Second data query: input medication time data, obtain name data, medication dosage data, bed number data, ward number data and medication name data under the current medication time data, calculate the total medication dosage data, the total number of bed number data, the total number of ward number data and the total number of medication name data.

[0024] Preferably, the nursing staff scheduling module includes a data aggregation and analysis unit and a scheduling plan formulation unit;

[0025] The data summary and analysis unit is used to input the medication time data in the medical record feature set into the total medication demand model, obtain the total medication dosage data, the total number of bed number data, the total number of ward number data, and the total number of medication name data, formulate a medication time period strategy, and output the medication time period. The medication time period includes a low-peak period, a mid-peak period, and a peak period. The medication time period strategy is:

[0026] When the total drug usage data is less than the total drug volume threshold, the medication period is considered a low-peak period;

[0027] When the total number of bed number data is less than the total bed number threshold, the medication period is a low-peak period;

[0028] When the total number of ward number data is less than the total ward number threshold, the medication period is a low-peak period;

[0029] When the total number of medication name data is less than the total medication name threshold, the medication period is a low-peak period;

[0030] When the total drug dosage data is at the total drug dosage threshold, the medication period is the mid-peak period;

[0031] When the total number of bed number data is at the threshold of the total number of bed numbers, the medication period is the mid-peak period;

[0032] When the total number of ward number data is at the threshold of the total number of ward numbers, the medication period is the peak period;

[0033] When the total number of medication name data is at the threshold of the total medication name amount, the medication period is the mid-peak period;

[0034] When the total drug usage data is greater than the total drug volume threshold, the medication period is the peak period;

[0035] When the total number of bed number data is greater than the total bed number threshold, the medication period is the peak period;

[0036] When the total number of ward number data is greater than the total ward number threshold, the medication period is the peak period;

[0037] When the total number of medication name data is greater than the total medication name threshold, the medication period is the peak period.

[0038] Preferably, the shift planning unit is used to formulate a shift plan according to the medication period strategy, and the shift plan is:

[0039] If the medication time period is a low-peak period, the number of personnel assigned is set to a small number of personnel assigned, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data in the low-peak period are sent to the nursing staff end assigned according to the small number of personnel assigned in the low-peak period;

[0040] If the medication time period is a mid-peak period, the number of personnel assigned is set to the medium number of personnel assigned, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data of the mid-peak period are sent to the nursing staff end assigned according to the medium number of personnel assigned during the mid-peak period;

[0041] If the medication period is a peak period, the number of personnel allocation is set to a large number of personnel allocations, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data during the peak period are sent to the nursing staff end allocated according to the large number of personnel allocations during the peak period.

[0042] Preferably, the drug sharing mechanism module includes a drug inventory allocation unit and a drug allocation sharing unit;

[0043] The drug inventory allocation unit is used to formulate a drug inventory allocation model based on the drug inventory information data and the personal medical record characteristic information data, and the drug inventory allocation model includes third data input, third data update and third data query;

[0044] Third data input: input drug name data, drug quantity data and drug location data from drug inventory information data, name data, bed number data, ward number data, medication name data, medication time data and medication period from personal medical record feature information data;

[0045] Third data update: manually update the drug name data, drug quantity data, and drug location data in the drug inventory information data, and the name data, bed number data, ward number data, medication name data, medication time data, and medication time period in the personal medical record feature information data;

[0046] The third data query: input medication name data, obtain the drug quantity data and the nursing station data where the drug is located corresponding to the drug name data that is the same as the medication name data.

[0047] Preferably, the drug dispensing sharing unit is used to input the medication name data into the drug inventory dispensing model, obtain the drug quantity data and the nursing station data where the drugs are located, obtain the nursing station distance between the nursing station data where the drugs are located and the ward number data, establish a correspondence between the nursing station distance, ward number data, the nursing station data where the drugs are located and the drug quantity data, output it as a drug dispensing table, obtain the currently required drug quantity data, input the ward number data and the currently required drug quantity data into the drug dispensing table, obtain the nursing station distance and the nursing station data where the drugs are located, select the nursing station data where the drugs are located corresponding to the nursing station distance with the smallest nursing station distance and output it.

[0048] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a cloud computing-based internal medicine nursing resource optimization system is implemented.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a cloud computing-based internal medicine nursing resource optimization system.

[0050] Beneficial effects of the present invention: By integrating patient and drug data, the present invention realizes the precise connection between dynamic scheduling and drug demand, optimizes drug distribution and storage, and significantly reduces drug waste. At the same time, it formulates a drug inventory allocation model to ensure the rapid allocation of drugs in emergency situations, improves work efficiency and the safety and reliability of medical services, and provides patients with more efficient and high-quality internal medicine nursing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1A basic flow chart of a cloud computing-based internal medicine nursing resource optimization system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0053] Example, see Figure 1 , provides a cloud computing-based internal medicine nursing resource optimization system, including a patient data integration module, a drug data analysis module, a nursing staff typesetting module and a drug sharing control module.

[0054] The patient data integration module is used to obtain the medical record data of each patient, pre-process the medical record data of each patient, extract feature information, output it as personal medical record feature information data, collect all personal medical record feature information data, output it as a medical record feature set, collect and store drug inventory information data.

[0055] The drug data analysis module is used to establish an individual drug demand model based on personal medical record feature information data, and to establish an overall drug demand model based on the medical record feature set.

[0056] The nursing staff scheduling module is used to generate a scheduling plan based on the total drug demand model.

[0057] The drug sharing mechanism module is used to establish an emergency drug allocation mechanism based on drug inventory information data.

[0058] The patient data integration module includes a patient information management unit and a drug inventory management unit.

[0059] The patient information management unit is used to obtain the medical record data of each patient and preprocess the medical record data of each patient. The preprocessing includes denoising and removing outliers, extracting feature information, and the feature information includes name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data. The feature information is output as personal medical record feature information data, and the personal medical record feature information data of all patients are set and output as a medical record feature set.

[0060] The patient information management unit acquires and pre-processes patient medical record data, ensuring the accuracy and reliability of the data, extracting key feature information, providing a basis for personalized medical services, and collecting individual medical record feature information data into a medical record feature set to facilitate subsequent data analysis and application.

[0061] The drug inventory management unit is used to obtain drug inventory information data, which includes drug name data, drug quantity data and drug location nursing station data, and store the drug inventory information data.

[0062] The drug inventory management unit obtains and stores drug inventory information in real time, ensuring the accuracy and completeness of drug information, providing real-time and accurate data support for drug dispensing and inventory management, helping to reduce drug backlog and waste and improve drug utilization.

[0063] The patient data integration module integrates the functions of the patient information management unit and the drug inventory management unit to achieve comprehensive and efficient management of patient medical records and drug inventory information. It not only improves the accuracy and timeliness of data processing, but also provides a solid data foundation for subsequent nursing staff scheduling and drug dispensing.

[0064] The drug data analysis module includes an individual demand modeling unit and an overall demand modeling unit.

[0065] The personal demand modeling unit is used to establish a personal drug demand model according to personal medical record characteristic information data, and the personal drug demand model includes first data input, first data update and first data query.

[0066] First data input: input name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data.

[0067] First data update: manually update name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data.

[0068] First data query: input name data, bed number data and ward number data to query medication name data, medication time data, medication course data and medication dosage data.

[0069] The personal demand modeling unit establishes a personalized drug demand model based on personal medical record characteristic information data, which can reflect the patient's medication needs, support manual updating of patient data, ensure the timeliness and accuracy of the model, adapt to changes in the patient's condition, and provide data query functions to facilitate medical staff to quickly obtain patients' medication information.

[0070] The overall demand modeling unit is used to establish an overall drug demand model according to the medical record feature set, and the overall drug demand model includes second data input, second data update and second data query.

[0071] Second data input: input all name data, bed number data, ward number data, medication name data, medication time data and medication dosage data in the medical record feature set.

[0072] Second data update: manually update all name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data in the medical record feature set.

[0073] Second data query: input medication time data, obtain name data, medication dosage data, bed number data, ward number data and medication name data under the current medication time data, calculate the total medication dosage data, the total number of bed number data, the total number of ward number data and the total number of medication name data.

[0074] The overall demand modeling unit establishes a total drug demand model based on the medical record feature set, which can comprehensively reflect the drug demand situation in the internal medicine ward, support manual updating of medical record feature set data, and automatically summarize and analyze it, providing a scientific basis for drug procurement, allocation and nursing staff scheduling. Through the query function, the total drug demand under specific medication time can be obtained.

[0075] The drug data analysis module integrates the functions of the individual demand modeling unit and the overall demand modeling unit to achieve a comprehensive analysis of patients' drug needs. It not only improves the scientificity and efficiency of drug management, but also provides strong data support for nursing staff scheduling, drug procurement and allocation, etc., which helps to optimize internal medicine nursing resources and improve the quality of medical services.

[0076] The nursing staff scheduling module includes a data aggregation and analysis unit and a scheduling plan making unit.

[0077] The data summary and analysis unit is used to input the medication time data in the medical record feature set into the total medication demand model, obtain the total medication dosage data, the total number of bed number data, the total number of ward number data, and the total number of medication name data, formulate a medication time strategy, and output the medication time. The medication time includes low-peak time, mid-peak time, and peak time. The medication time strategy is:

[0078] When the total drug usage data is less than the total drug volume threshold, the medication period is a low-peak period.

[0079] When the total number of bed number data is less than the total bed number threshold, the medication period is a low-peak period.

[0080] When the total number of ward number data is less than the total ward number threshold, the medication period is a low-peak period.

[0081] When the total number of medication name data is less than the total medication name threshold, the medication period is a low-peak period.

[0082] When the total drug dosage data is at the total drug dosage threshold, the medication period is the mid-peak period.

[0083] When the total number of bed number data is at the threshold of the total number of bed numbers, the medication period is the mid-peak period.

[0084] When the total number of ward number data is at the total ward number threshold, the medication period is the peak period.

[0085] When the total number of medication name data is at the threshold of the total amount of medication names, the medication period is the mid-peak period.

[0086] When the total drug usage data is greater than the total drug volume threshold, the medication period is the peak period.

[0087] When the total number of bed number data is greater than the total bed number threshold, the medication period is the peak period.

[0088] When the total number of ward number data is greater than the total ward number threshold, the medication period is the peak period.

[0089] When the total number of medication name data is greater than the total medication name threshold, the medication period is the peak period.

[0090] The data aggregation and analysis unit inputs the medication time data in the medical record feature set into the total drug demand model, accurately determines the medication time period, and formulates a scientific medication time period strategy based on the total drug demand and the threshold comparison of various data, providing a reliable basis for the formulation of the scheduling plan. As the medical record feature set data is updated, the medication time period strategy can be adjusted in real time to ensure the flexibility and adaptability of the scheduling plan.

[0091] The shift plan making unit is used to make a shift plan according to the medication period strategy. The shift plan is:

[0092] If the medication period is a low-peak period, the number of personnel assigned is set to a small number of personnel assigned, and the name data, drug dosage data, bed number data, ward number data and medication name data corresponding to the medication time data during the low-peak period are sent to the nursing staff end assigned according to the small number of personnel assigned during the low-peak period.

[0093] If the medication period is the mid-peak period, set the number of personnel allocation to the medium number of personnel allocation, and send the name data, drug dosage data, bed number data, ward number data and medication name data corresponding to the medication time data of the mid-peak period to the nursing staff end allocated according to the medium number of personnel allocation during the mid-peak period.

[0094] If the medication period is a peak period, the number of personnel allocation is set to a large number of personnel allocations, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data during the peak period are sent to the nursing staff end allocated according to the large number of personnel allocations during the peak period.

[0095] The scheduling plan formulation unit sets the number of personnel allocated for different time periods based on the medication period strategy, ensures the rational allocation of nursing resources under different needs, and accurately pushes the medication information for each time period to the corresponding number of nursing staff, thereby improving the pertinence and efficiency of nursing work. By dynamically adjusting the scheduling plan, it effectively avoids the waste and shortage of nursing resources and improves the overall nursing service level.

[0096] The nursing staff scheduling module organically combines data aggregation and analysis with scheduling plan formulation to achieve efficient scheduling of nursing staff based on dynamic changes in drug demand. It not only improves the allocation efficiency of nursing resources, but also ensures that patients can receive timely and professional nursing services at different medication times, effectively improving the quality of medical services and patient satisfaction.

[0097] The drug sharing mechanism module includes a drug inventory allocation unit and a drug allocation sharing unit.

[0098] The drug inventory allocation unit is used to formulate a drug inventory allocation model based on the drug inventory information data and the personal medical record characteristic information data. The drug inventory allocation model includes third data input, third data update and third data query.

[0099] The third data input: input the drug name data, drug quantity data and drug location nursing station data in the drug inventory information data, the name data, bed number data, ward number data, medication name data and medication time data and medication period in the personal medical record feature information data.

[0100] Third data update: manually update the drug name data, drug quantity data and drug location nursing station data in the drug inventory information data, the name data, bed number data, ward number data, medication name data and medication time data and medication period in the personal medical record feature information data.

[0101] The third data query: input medication name data, obtain the drug quantity data and the nursing station data where the drug is located corresponding to the drug name data that is the same as the medication name data.

[0102] The drug inventory allocation unit integrates drug inventory information and personal medical record characteristic information into the drug inventory allocation model through the third data input function, providing comprehensive and accurate data support for subsequent drug allocation. The third data update function allows manual updating of drug inventory and personal medical record characteristic information to ensure the timeliness and accuracy of the data, providing a reliable basis for drug allocation. Through the third data query function, the inventory status of drugs with specified medication names can be quickly obtained, providing a convenient way for drug allocation.

[0103] The drug dispensing sharing unit is used to input the medication name data into the drug inventory dispensing model, obtain the drug quantity data and the nursing station data where the drugs are located, obtain the nursing station distance between the nursing station data where the drugs are located and the ward number data, establish a corresponding relationship between the nursing station distance, ward number data, the nursing station data where the drugs are located and the drug quantity data, and output it as a drug dispensing table, obtain the currently required drug quantity data, input the ward number data and the currently required drug quantity data into the drug dispensing table, obtain the nursing station distance and the nursing station data where the drugs are located, select the nursing station data where the drugs are located corresponding to the nursing station distance with the smallest nursing station distance and output it.

[0104] The drug dispensing sharing unit inputs the drug name data into the drug inventory dispensing model, automatically obtains the distance between the nursing station data where the drug is located and the ward number data, establishes a corresponding relationship, and generates a drug dispensing table, realizing intelligent drug dispensing and improving dispensing efficiency and accuracy. By comparing the distances of different nursing stations, the nursing station closest to the ward is selected to provide drugs, reducing the transportation time and cost of drug dispensing and optimizing the allocation of drug resources. According to the current required drug quantity data, the ward number data and drug quantity are input into the drug dispensing table, and the information of the nearest nursing station where the drug is located can be quickly obtained, realizing real-time response and rapid delivery of drugs, ensuring that patients can obtain the required drugs in a timely manner.

[0105] The drug sharing mechanism module integrates drug inventory information and personal medical record characteristic information to achieve effective allocation and sharing of drug resources. It not only improves the efficiency and accuracy of drug use, but also promotes collaboration between nursing stations, ensuring that patients can obtain the required drugs in a timely manner, thereby improving the overall level of medical services.

[0106] Through the patient data integration module and medication data analysis module, the system obtains patients' medical records and medication needs in real time, providing a scientific basis for nursing staff scheduling. This combination makes scheduling more reasonable, ensuring sufficient nursing staff to provide services during peak medication demand periods, thereby improving work efficiency.

[0107] The scheduling plan generated by the present invention according to the total drug demand model can dynamically adjust the number of allocated nursing staff to ensure the reasonable allocation of nursing staff resources during different medication periods. This dynamic scheduling method effectively avoids the waste of human resources, improves the response speed and quality of nursing services, and grasps the drug inventory situation and patient medication needs in real time, providing data support for the distribution and storage of drugs. By optimizing the drug distribution strategy, the backlog and waste of drugs are reduced, and the utilization rate of drugs is improved. The drug sharing mechanism module realizes the effective connection of adjacent nursing resource points by establishing a corresponding relationship. Through the drug allocation sharing unit, it can obtain drug inventory information and patient medication needs in real time, quickly formulate a drug allocation plan, and in an emergency, it can quickly allocate drug resources of adjacent nursing stations to ensure that patients can obtain the required drugs in time, thereby improving the reliability and safety of medical services.

[0108] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] 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. A cloud computing-based internal medicine nursing resource optimization system, characterized in that: It includes patient data integration module, drug data analysis module, nursing staff typesetting module and drug sharing control module; The patient data integration module is used to obtain the medical record data of each patient, pre-process the medical record data of each patient, extract feature information, output it as individual medical record feature information data, aggregate all individual medical record feature information data, output it as a medical record feature set, and collect and store drug inventory information data; The drug data analysis module is used to establish an individual drug demand model based on individual medical record feature information data, and to establish an overall drug demand model based on the medical record feature set; The nursing staff scheduling module is used to generate a scheduling plan based on the total drug demand model; The drug sharing mechanism module is used to establish a drug emergency allocation mechanism based on drug inventory information data.

2. The cloud computing-based internal medicine nursing resource optimization system according to claim 1, characterized in that: The patient data integration module includes a patient information management unit and a drug inventory management unit; The patient information management unit is used to obtain the medical record data of each patient, pre-process the medical record data of each patient, the pre-processing including denoising and removing outliers, extracting feature information, the feature information including name data, bed number data, ward number data, medication name data, medication time data, medication course data, and medication dosage data, output the feature information as personal medical record feature information data, and output the collection of personal medical record feature information data of all patients as a medical record feature set; The drug inventory management unit is used to obtain drug inventory information data, which includes drug name data, drug quantity data and drug location nursing station data, and store the drug inventory information data.

3. The cloud computing-based internal medicine nursing resource optimization system according to claim 2, characterized in that: The drug data analysis module includes an individual demand modeling unit and an overall demand modeling unit; The personal demand modeling unit is used to establish a personal drug demand model based on personal medical record characteristic information data, and the personal drug demand model includes first data input, first data update and first data query; First data input: input name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data; First data update: manually update name data, bed number data, ward number data, medication name data, medication time data, medication course data and medication dosage data; First data query: input name data, bed number data and ward number data to query medication name data, medication time data, medication course data and medication dosage data.

4. The cloud computing-based internal medicine nursing resource optimization system according to claim 3, characterized in that: The overall demand modeling unit is used to establish an overall drug demand model based on the medical record feature set, and the overall drug demand model includes second data input, second data update and second data query; Second data input: input all name data, bed number data, ward number data, medication name data, medication time data and medication dosage data in the medical record feature set; Second data update: manually update all name data, bed number data, ward number data, medication name data, medication time data, medication course data, and medication dosage data in the medical record feature set; Second data query: input medication time data, obtain name data, medication dosage data, bed number data, ward number data and medication name data under the current medication time data, calculate the total medication dosage data, the total number of bed number data, the total number of ward number data and the total number of medication name data.

5. The cloud computing-based internal medicine nursing resource optimization system according to claim 4, characterized in that: The nursing staff scheduling module includes a data aggregation and analysis unit and a scheduling plan formulation unit; The data summary and analysis unit is used to input the medication time data in the medical record feature set into the total medication demand model, obtain the total medication dosage data, the total number of bed number data, the total number of ward number data, and the total number of medication name data, formulate a medication time period strategy, and output the medication time period. The medication time period includes a low-peak period, a mid-peak period, and a peak period. The medication time period strategy is: When the total drug usage data is less than the total drug volume threshold, the medication period is considered a low-peak period; When the total number of bed number data is less than the total bed number threshold, the medication period is a low-peak period; When the total number of ward number data is less than the total ward number threshold, the medication period is a low-peak period; When the total number of medication name data is less than the total medication name threshold, the medication period is a low-peak period; When the total drug dosage data is at the total drug dosage threshold, the medication period is the mid-peak period; When the total number of bed number data is at the threshold of the total number of bed numbers, the medication period is the mid-peak period; When the total number of ward number data is at the threshold of the total number of ward numbers, the medication period is the peak period; When the total number of medication name data is at the threshold of the total medication name amount, the medication period is the mid-peak period; When the total drug usage data is greater than the total drug volume threshold, the medication period is the peak period; When the total number of bed number data is greater than the total bed number threshold, the medication period is the peak period; When the total number of ward number data is greater than the total ward number threshold, the medication period is the peak period; When the total number of medication name data is greater than the total medication name threshold, the medication period is the peak period.

6. The cloud computing-based internal medicine nursing resource optimization system according to claim 5, characterized in that: The shift plan making unit is used to make a shift plan according to the medication period strategy. The shift plan is: If the medication time period is a low-peak period, the number of personnel assigned is set to a small number of personnel assigned, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data in the low-peak period are sent to the nursing staff end assigned according to the small number of personnel assigned in the low-peak period; If the medication time period is a mid-peak period, the number of personnel assigned is set to the medium number of personnel assigned, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data of the mid-peak period are sent to the nursing staff end assigned according to the medium number of personnel assigned during the mid-peak period; If the medication period is a peak period, the number of personnel allocation is set to a large number of personnel allocations, and the name data, medication dosage data, bed number data, ward number data and medication name data corresponding to the medication time data during the peak period are sent to the nursing staff end allocated according to the large number of personnel allocations during the peak period.

7. The cloud computing-based internal medicine nursing resource optimization system according to claim 6, characterized in that: The drug sharing mechanism module includes a drug inventory allocation unit and a drug allocation sharing unit; The drug inventory allocation unit is used to formulate a drug inventory allocation model based on the drug inventory information data and the personal medical record characteristic information data, and the drug inventory allocation model includes third data input, third data update and third data query; Third data input: input drug name data, drug quantity data and drug location data from drug inventory information data, name data, bed number data, ward number data, medication name data, medication time data and medication period from personal medical record feature information data; Third data update: manually update the drug name data, drug quantity data, and drug location data in the drug inventory information data, and the name data, bed number data, ward number data, medication name data, medication time data, and medication time period in the personal medical record feature information data; The third data query: input medication name data, obtain the drug quantity data and the nursing station data where the drug is located corresponding to the drug name data that is the same as the medication name data.

8. The cloud computing-based internal medicine nursing resource optimization system according to claim 7, characterized in that: The drug dispensing sharing unit is used to input the medication name data into the drug inventory dispensing model, obtain the drug quantity data and the nursing station data where the drugs are located, obtain the nursing station distance between the nursing station data where the drugs are located and the ward number data, establish a correspondence between the nursing station distance, ward number data, the nursing station data where the drugs are located and the drug quantity data, output it as a drug dispensing table, obtain the currently required drug quantity data, input the ward number data and the currently required drug quantity data into the drug dispensing table, obtain the nursing station distance and the nursing station data where the drugs are located, select the nursing station data where the drugs are located corresponding to the nursing station distance with the smallest nursing station distance and output it.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the cloud computing-based internal medicine nursing resource optimization system according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the cloud computing-based internal medicine nursing resource optimization system according to any one of claims 1 to 8.

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