Big-data-based intelligent medical ai management method, system and media

Abstract

A big data-based intelligent medical AI management method, system and media are provided, which can effectively improve medical data management and analysis efficiency, enable medical institutions to recognize and deal with disease risks in advance, and improve public health safety.SOLUTION: The method monitors data of a medical institution, acquires data storage scale and incremental information, determines an information storage system construction means, constructs an information storage system of a target medical institution on the basis of the means, periodically acquires diagnosis data, analyzes the data by an artificial intelligence algorithm, and predicts a case development tendency. Further, a disease epidemic analysis is performed on the basis of the case onset tendency, an epidemic occurrence risk is evaluated, and a countermeasure is determined.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of medical management technology, and in particular to a medical intelligent AI management method and system based on big data. [Background technology]

[0002] With the advancement of medical technology and the rapid development of information technology, medical institutions have generated and accumulated large amounts of medical data. This data includes patient diagnosis information, treatment records, medical chart data, etc., and is characterized by large scale, diverse type, and rapid growth. This medical data has important value in disease analysis, disease prediction, and public health surveillance. However, traditional medical data management methods cannot effectively handle this large scale and diverse data, and medical data storage, analysis, and utilization efficiency are often low.

[0003] With traditional technologies, many medical institutions rely on manual analysis and processing of data, which is not only time-consuming and labor-intensive but also prone to errors and oversights. At the same time, faced with complex and ever-changing disease trends, traditional methods make it difficult to achieve timely early warning and effective response to disease outbreaks, resulting in difficulties in predicting and controlling public health risks.

[0004] In recent years, the application of big data and artificial intelligence technologies in the medical field has gradually increased. Big data technology can efficiently process and store huge amounts of data, and artificial intelligence algorithms can mine valuable information from this data to predict and analyze disease development trends. The application of big data and artificial intelligence technologies to medical data management can significantly improve the efficiency of medical data processing and analysis, and improve the accuracy and timeliness of disease prediction.

[0005] This invention provides a medical intelligent AI management method based on big data, establishes an efficient information storage system, and uses artificial intelligence algorithms to analyze and predict medical data, enabling medical institutions to realize real-time disease epidemic monitoring and risk warning, and effectively enhance public health response capabilities. Such a method not only improves the efficiency of medical data management, but also provides a scientific basis for disease prevention and control, and is of great significance in ensuring public health. Summary of the Invention [Problem to be solved by the invention]

[0006] In order to solve at least one of the above technical problems, the present invention provides a medical intelligence AI management method and system based on big data. [Means for solving the problem]

[0007] A first aspect of the present invention provides a medical intelligence AI management method based on big data, the medical intelligence AI management method comprising: monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and the data increase information; Building an information storage system for the target medical institution based on big data technology and the information storage system building means; According to the information storage system, obtain the diagnostic data information of patients at the target medical institution at a predetermined time interval, analyze the diagnostic data information according to an artificial intelligence algorithm, and predict the disease incidence tendency of the target medical institution; Conducting a disease epidemic analysis based on the disease case occurrence tendency to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; and determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data.

[0008] In this means, the steps of monitoring the medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining the information storage system construction means of the target institution based on the data storage scale information and data increase information, specifically, include: obtaining historical medical data from a computer at the target medical institution, including electronic health records, laboratory test data, medical imaging data, patient monitoring data, and diagnostic and treatment data; Evaluating the total data volume of the target medical institution based on the historical medical data, and obtaining data scale information of the target medical institution, including the total data volume, the total data volume of each medical data item, and data volume ratio information of each medical data item; Monitoring the medical data of the target medical institution based on the data flow monitoring platform, establishing monitoring channels for different medical data items, monitoring each medical data item in real time based on the monitoring channels, recording the data generation speed and increment of each monitoring channel, and obtaining data increment information; identifying peak periods of data generation based on the data increment information and estimating a maximum data increase during the peak periods; analyzing the data increase trend of each medical data item based on the data increase information and the historical medical data to predict the future data increase amount; obtaining data access frequency information of each medical data item, setting a data access frequency threshold, and dividing the medical data of the target medical institution into hot data, warm data, and cold data according to the data access frequency threshold to obtain data access popularity information; Obtaining storage period requirement information of each medical data item of the target medical institution, and determining a storage device type, including SSD, HDD, and cloud storage, for each medical data item of the target medical institution based on the data access popularity information; determining the storage capacity of each storage device type based on the data size information, data increment information, future data increase amount, and medical data storage period requirement information of the target medical institution; determining data processor performance requirements for the target medical institution's data storage system based on the peak period maximum data growth and data access frequency information; and The method includes a step of determining an information storage system configuration means for the target medical institution based on the storage device type, storage flux of the storage device type, and data processor performance requirements of the information storage system of the target medical institution.

[0009] In this means, the step of constructing an information storage system for a target medical institution based on big data technology and the information storage system construction means specifically includes: constructing a hardware storage system for the target medical institution based on the information storage system construction means; Building a distributed file system of the target medical institution based on big data technology, building data partitions in the distributed file system according to medical data items, and obtaining medical data item storage partitions; obtaining a storage device type corresponding to each medical data item of the target medical institution from the information storage system construction means, determining a storage device type corresponding to each medical data item partition according to the storage device type corresponding to each medical data item, establishing a mapping relationship between the medical data item storage partition and the corresponding storage device type, and obtaining a storage partition-device type mapping table; A step of acquiring a data update request for each medical data item from the target medical institution, and determining a data update period for each medical data item in response to the data update request; The method includes a step of arranging a real-time data collection tool based on the storage partition-device type mapping table and the data update period, collecting data for each medical data item in the target medical institution based on the real-time data collection tool, and storing the data in the corresponding storage device and storage partition according to the data update time period, thereby forming a complete information storage system for the target medical institution.

[0010] In this means, the step of acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease onset tendency of the target medical institution specifically includes: A step of acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, and extracting disease type information of the patients examined based on the diagnostic data information; a step of aggregating case number information in which different disease types appear at target medical institutions in each time period based on disease type information of patients examined, plotting the case number information in which different disease types appear at target medical institutions in each time period as a case number change curve to obtain a case number change curve dataset, and dividing each curve in the case number change curve dataset into a training set and a test set at a predetermined period ratio; constructing a case number change prediction model based on the ARIMA algorithm, and analyzing the case number change curve dataset by the time series autocorrelation function to determine the autoregression order, difference number, and moving average order of the prediction model; arranging parameters of a case number change prediction model according to the autoregressive order, the number of differences, and the moving average order, introducing the training set into the case number change prediction model to fit and train the model, introducing the test set into the prediction model to evaluate the prediction effect, and if the prediction effect is smaller than a preset effect, adjusting the model parameters until the prediction effect is equal to or greater than the preset effect; A step of acquiring case number change data for a predetermined number of time periods and introducing it into the case number change prediction model, predicting the number of occurrences of cases of different disease types in a future predetermined time period at the target medical institution, and obtaining a prediction result; The method includes plotting the prediction results as a predicted case number change curve dataset, and determining the case occurrence trends of different disease types based on the predicted case number change curve dataset.

[0011] In this means, the step of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the risk of disease epidemic occurrence, and obtaining disease epidemic risk data specifically includes: a step of obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in a future time period at the target medical institution based on the case onset tendency, and obtaining case increase rate data; calculating a European distance between the case increase rate data and the case increase rate index data by comparing the case increase rate data and the case increase rate index data, and assessing the risk of epidemic outbreak of different disease types at each time point in a predetermined future time based on the European distance; The method includes steps of presetting a first risk threshold and a second risk threshold, marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease, marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease, and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, and obtaining disease epidemic risk data.

[0012] In this means, the step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: obtaining high-risk epidemic disease types based on the disease epidemic risk data, obtaining drugs and medical equipment required for treating the high-risk epidemic disease types, and obtaining treatment material information; determining a number of cases of the high-risk epidemic disease type based on the case incidence trend, and determining a required amount of treatment supplies based on the number of cases; and The method further includes a step of pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies, and obtaining disease epidemic response measures for the target medical institution.

[0013] A second aspect of the present invention further provides a big data-based medical intelligence AI management system, the system comprising: a memory and a processor; a big data-based medical intelligence AI management method program stored in the memory; and when the big data-based medical intelligence AI management method program is executed by the processor, monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and data increase information; Building an information storage system for the target medical institution based on big data technology and the information storage system building means; acquiring diagnostic data information of patients at the target medical institution at a predetermined time interval based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease incidence tendency of the target medical institution; Conducting a disease epidemic analysis based on the disease case occurrence tendency to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; and A step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data is realized.

[0014] In this means, the step of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the risk of disease epidemic occurrence, and obtaining disease epidemic risk data specifically includes: a step of obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in a future time period at the target medical institution based on the case onset tendency, and obtaining case increase rate data; calculating a European distance between the case increase rate data and the case increase rate index data by comparing the case increase rate data and the case increase rate index data, and assessing the risk of epidemic outbreak of different disease types at each time point in a predetermined future time based on the European distance; The method includes steps of presetting a first risk threshold and a second risk threshold, marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease, marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease, and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, and obtaining disease epidemic risk data.

[0015] In this means, the step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: obtaining high-risk epidemic disease types based on the disease epidemic risk data, obtaining drugs and medical equipment required for treating the high-risk epidemic disease types, and obtaining treatment material information; determining a number of cases of the high-risk epidemic disease type based on the case incidence trend, and determining a required amount of treatment supplies based on the number of cases; and The method further includes a step of pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies, and obtaining disease epidemic response measures for the target medical institution.

[0016] A third aspect of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a big data-based medical intelligence AI management program, which, when executed by a processor, realizes the steps of the big data-based medical intelligence AI management method described in any one of the above. [Effects of the Invention]

[0017] The present invention discloses a medical intelligence AI management method, system, and medium based on big data, which involves monitoring medical institution data, obtaining data storage scale and incremental information, determining information storage system construction measures, and building an information storage system for the target medical institution based on the measures. The system periodically obtains diagnostic data, analyzes the data using an artificial intelligence algorithm, and predicts case occurrence trends. Furthermore, based on the case occurrence trends, it performs disease epidemic analysis, assesses the risk of epidemic outbreaks, and determines response measures. The method effectively improves the efficiency of medical data management and analysis, enables medical institutions to recognize and respond to disease risks in advance, and improves public health safety. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a flowchart illustrating a medical intelligence AI management method based on big data according to the present invention. [Figure 2] 1 is a flow chart for obtaining disease epidemic risk data of the present invention. [Figure 3] 1 is a flowchart for determining disease epidemic response measures of a target medical institution according to the present invention. [Figure 4] FIG. 1 is a block diagram of a medical intelligence AI management system based on big data of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] In order to make the above objects, features and advantages of the present invention more clearly understandable, the present invention will be described in more detail below with reference to the drawings and specific embodiments. It should be noted that, unless conflicting, the embodiments and features in the embodiments of the present application can be combined with each other.

[0020] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention; however, the present invention may be practiced in ways other than those described herein, and therefore the scope of protection of the present invention is not limited to the specific examples disclosed below.

[0021] FIG. 1 is a flowchart of the medical intelligence AI management method based on big data of the present invention.

[0022] As shown in FIG. 1, a first aspect of the present invention provides a medical intelligence AI management method based on big data, the medical intelligence AI management method comprising: S102: monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means for the target institution based on the data storage scale information and data increase information; S104: building an information storage system of the target medical institution based on big data technology and the information storage system building means; S106: based on the information storage system, obtain diagnostic data information of patients at the target medical institution at a predetermined time interval, analyze the diagnostic data information based on an artificial intelligence algorithm, and predict the disease incidence tendency of the target medical institution; S108: performing a disease epidemic analysis based on the case occurrence tendency to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; and The method includes determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data (S110).

[0023] It is necessary to explain that the medical data of the target medical institution is monitored, its data storage scale and data increment information is obtained, and based on this, the means for constructing the information storage system is determined. By monitoring the data storage scale and increment, the data storage requirements of the medical institution can be accurately evaluated, and the waste and shortage of system resources can be avoided. The customized storage means can reduce unnecessary hardware and software input, reduce operation costs, and improve resource utilization. Based on big data technology and the previously determined means for constructing the information storage system, the information storage system of the target medical institution is constructed, and big data technology is used to create an efficient information storage system. By building a system that improves data processing and query speeds and shortens data access times, and by using artificial algorithms to conduct disease epidemic analysis and predict disease development trends, potential epidemic risks can be detected early and warning information can be provided to public health departments. Timely epidemic analysis and prediction can help formulate effective prevention and control measures and reduce the transmission and spread of diseases. Based on disease epidemic risk data, disease epidemic response measures for target medical institutions can be formulated. Early formulation of response measures allows medical institutions to respond quickly when diseases break out and reduce the negative impact on society and the economy. The target medical institutions include hospitals, clinics, and community health centers.

[0024] According to an embodiment of the present invention, the steps of monitoring medical data of the target medical institution, obtaining data storage scale information and data increment information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and data increment information, specifically include: obtaining historical medical data from a computer at the target medical institution, including electronic health records, laboratory test data, medical imaging data, patient monitoring data, and diagnostic and treatment data; Evaluating the total data volume of the target medical institution based on the historical medical data, and obtaining data scale information of the target medical institution, including the total data volume, the total data volume of each medical data item, and data volume ratio information of each medical data item; Monitoring the medical data of the target medical institution based on the data flow monitoring platform, establishing monitoring channels for different medical data items, monitoring each medical data item in real time based on the monitoring channels, recording the data generation speed and increment of each monitoring channel, and obtaining data increment information; identifying peak periods of data generation based on the data increment information and estimating a maximum data increase during the peak periods; analyzing the data increase trend of each medical data item based on the data increase information and the historical medical data to predict the future data increase amount; obtaining data access frequency information of each medical data item, setting a data access frequency threshold, and dividing the medical data of the target medical institution into hot data, warm data, and cold data according to the data access frequency threshold to obtain data access popularity information; Obtaining storage period requirement information of each medical data item of the target medical institution, and determining a storage device type, including SSD, HDD, and cloud storage, for each medical data item of the target medical institution based on the data access popularity information; determining the storage capacity of each storage device type based on the data size information, data increment information, future data increase amount, and medical data storage period requirement information of the target medical institution; determining data processor performance requirements for the target medical institution's data storage system based on the peak period maximum data growth and data access frequency information; and The method includes a step of determining an information storage system configuration means for the target medical institution based on the storage device type, storage flux of the storage device type, and data processor performance requirements of the information storage system of the target medical institution.

[0025] It is necessary to explain that the access frequency of each medical data item of the target medical institution is analyzed to determine data access popularity information, and storage devices are determined according to different data access popularity, with hot data being preferentially stored on high-speed storage devices to increase data access speed and cold data being stored on low-cost storage media to reduce storage costs and optimize the use of storage resources. The hot data is frequently accessed data such as real-time monitoring data and active electronic health records, warm data is data with medium access frequency such as recent medical images and diagnostic reports, and cold data is data with low access frequency such as historical data and archive files. HDDs (mechanical hard drives) have the advantage of large capacity and low cost, making them suitable for storing warm data, but the disadvantage of slow access speed, making them unsuitable for frequently accessed data. SSDs (solid-state drives) have the advantage of fast read and write speeds, making them suitable for storing frequently accessed hot data, but are more costly and suitable for storing data with relatively small capacity requirements. Cloud storage has the advantages of being highly flexible, expanding as needed, suitable for storing cold data, and supporting remote access, but also the disadvantages of being costly over the long term, having longer data transfer delays than local storage, and relying on a network connection. The construction method for the information storage system is ultimately determined based on the storage device type, storage flux of the storage device type, and performance requirements of the data processor of the target medical institution's information storage system. The rationally constructed information storage system enhances data management capabilities and ensures efficient data storage, management, and utilization. The method takes into account future data growth requirements and storage expansion requirements, ensuring the system can flexibly respond to future changes. The data processor performance requirements include data read and write speeds and data access volume processing capacity. The medical data items are electronic health records, laboratory test data, medical image data, patient monitoring data, and diagnosis and treatment data.

[0026] According to an embodiment of the present invention, the step of constructing an information storage system of the target medical institution based on the big data technology and the information storage system construction means specifically includes: constructing a hardware storage system for the target medical institution based on the information storage system construction means; Building a distributed file system of the target medical institution based on big data technology, building data partitions in the distributed file system according to medical data items, and obtaining medical data item storage partitions; obtaining a storage device type corresponding to each medical data item of the target medical institution from the information storage system construction means, determining a storage device type corresponding to each medical data item partition according to the storage device type corresponding to each medical data item, establishing a mapping relationship between the medical data item storage partition and the corresponding storage device type, and obtaining a storage partition-device type mapping table; A step of acquiring a data update request for each medical data item from the target medical institution, and determining a data update period for each medical data item in response to the data update request; The method includes a step of arranging a real-time data collection tool based on the storage partition-device type mapping table and the data update period, collecting data for each medical data item in the target medical institution based on the real-time data collection tool, and storing the data in the corresponding storage device and storage partition according to the data update time period, thereby forming a complete information storage system for the target medical institution.

[0027] It is necessary to explain that an efficient and stable hardware storage system is built based on information storage system construction means, ensuring safe storage and fast access of medical data; a distributed file system supports large-scale data storage and management, improving data management efficiency and reliability; data partition management ensures data storage order and manageability, and facilitates fast data search and access; a distributed system can effectively allocate and balance data loads, improving the overall performance and stability of the system; mapping relationships allow data to be flexibly stored on different devices according to access frequency and importance, reducing the waste of high-performance devices and reducing overall storage costs; rationally setting data update periods reduces system resource consumption, improving the operating efficiency and data processing capacity of the storage system; automatic data collection and storage is achieved by arranging collection tools, reducing manual operations and human errors, and improving data management efficiency; data partitions are built according to medical data items, so that medical data items can be stored in corresponding partitions; and the storage partition-device type mapping table can accurately store different medical data items of target medical institutions in corresponding storage devices; and the real-time data collection tools include Flume and Kafka.

[0028] According to an embodiment of the present invention, the step of obtaining the diagnostic data information of patients in the target medical institution at a predetermined time period based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease incidence tendency of the target medical institution specifically includes: A step of acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, and extracting disease type information of the patients examined based on the diagnostic data information; a step of aggregating case number information in which different disease types appear at target medical institutions in each time period based on disease type information of patients examined, plotting the case number information in which different disease types appear at target medical institutions in each time period as a case number change curve to obtain a case number change curve dataset, and dividing each curve in the case number change curve dataset into a training set and a test set at a predetermined period ratio; constructing a case number change prediction model based on the ARIMA algorithm, and analyzing the case number change curve dataset by the time series autocorrelation function to determine the autoregression order, difference number, and moving average order of the prediction model; arranging parameters of a case number change prediction model according to the autoregressive order, the number of differences, and the moving average order, introducing the training set into the case number change prediction model to fit and train the model, introducing the test set into the prediction model to evaluate the prediction effect, and if the prediction effect is smaller than a preset effect, adjusting the model parameters until the prediction effect is equal to or greater than the preset effect; A step of acquiring case number change data for a predetermined number of time periods and introducing it into the case number change prediction model, predicting the number of occurrences of cases of different disease types in a future predetermined time period at the target medical institution, and obtaining a prediction result; The method includes plotting the prediction results as a predicted case number change curve dataset, and determining the case occurrence trends of different disease types based on the predicted case number change curve dataset.

[0029] It is important to note that big data technology and artificial intelligence algorithms are used to comprehensively collect and analyze medical data, thereby building an effective case number change prediction model, and periodically compiling and predicting the number of cases of different disease types, thereby accurately grasping future case occurrence trends. The preset time period is days, and the ARIMA (autoregressive integrated moving average model) is an artificial intelligence algorithm used for time series data analysis and aggregation, which captures data trends to model and predict data.

[0030] FIG. 2 is a flow chart for obtaining disease epidemic risk data of the present invention.

[0031] According to an embodiment of the present invention, the step of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the disease epidemic occurrence risk, and obtaining disease epidemic risk data specifically includes: S202: obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in the future time period at the target medical institution based on the case onset tendency, and obtaining case increase rate data; S204: calculating a European distance between the disease case increase rate data and the disease case increase rate index data by comparing the disease case increase rate data and the disease case increase rate index data, and assessing the epidemic outbreak risk of different disease types at each time point in a future preset time based on the European distance; and The method includes S206 of presetting a first risk threshold and a second risk threshold, marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease, marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease, and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, and obtaining disease epidemic risk data.

[0032] It is important to note that by calculating the distance between the rate of increase in cases and indicator data, the epidemic risk of different diseases within the future time period can be accurately assessed. Based on the preset risk threshold, the epidemic risk of diseases can be clearly classified, allowing health departments and medical institutions to formulate targeted prevention and control measures, rationally allocate medical resources according to the risk level of the disease, and prioritize the treatment of high-risk diseases, thereby improving the efficiency of medical resource utilization.

[0033] FIG. 3 is a flowchart for determining disease epidemic response measures of a target medical institution according to the present invention.

[0034] According to an embodiment of the present invention, the step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: S302: obtain high-risk epidemic disease types based on the disease epidemic risk data, obtain drugs and medical equipment required for treating the high-risk epidemic disease types, and obtain treatment material information; S304: determining the number of cases of the high-risk epidemic disease type based on the case incidence trend, and determining the required amount of medical supplies based on the number of cases; and The method includes S306: pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies, and obtaining disease epidemic response measures for the target medical institution.

[0035] It is important to note that accurately identifying the types of high-risk epidemic diseases that medical institutions are facing can help them focus and prepare for corresponding responses, predict demand, optimize medical supply chain management, ensure timely supply of sufficient medical supplies when needed, improve their ability to respond to sudden epidemics and disease outbreaks, stockpile medical supplies in advance, and respond quickly and deploy emergency treatment during the peak of the disease epidemic.

[0036] According to an embodiment of the invention: monitoring real-time usage data of therapeutic supplies in said disease outbreak response means and determining a consumption rate of supplies based on said usage data; establishing a dynamic inventory management system for the target medical institution, monitoring the inventory levels of medical supplies in the target medical institution in real time, and determining a replenishment period for each medical supply according to the consumption rate and the inventory levels of medical supplies; Acquiring data on the urgency, frequency of use, and supply time for each medical supply, and performing linear programming analysis on the urgency, frequency of use, supply time data, and supply cycle of the medical supplies based on a linear programming algorithm to determine the arrival time for each medical supply; The method further includes a step of procuring each medical supply at the target medical institution based on the arrival time.

[0037] It is important to note that during epidemics, medical institutions experience a sharp increase in demand for medical supplies, making the rapid consumption and replenishment of supplies a key issue in responding to epidemics. However, traditional supply management methods lack the ability to monitor and adjust dynamically in real time, which can lead to supply supply instability and supply shortages or overstocking. Therefore, a dynamic inventory management system is established for the target medical institution to determine the supply cycle for medical supplies, and a linear programming algorithm is used to plan the urgency, frequency of use, and supply time data for each medical supply, determine the rotation time for each medical supply, and finally determine the optimal arrival time early. Through scientific optimization of arrival time and supply procurement, emergency response time can be shortened and supplies can be delivered to required locations in a timely manner. Through the integration of supply time data and optimization of arrival planning, the stability and continuity of the supply chain can be ensured.

[0038] FIG. 4 is a block diagram illustrating a medical intelligence AI management system based on big data of the present invention.

[0039] A second aspect of the present invention further provides a big data-based medical intelligence AI management system 4, the system comprising a memory 41 and a processor 42, the memory storing a big data-based medical intelligence AI management method program, and when the big data-based medical intelligence AI management method program is executed by the processor, monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and data increase information; Building an information storage system for the target medical institution based on big data technology and the information storage system building means; acquiring diagnostic data information of patients at the target medical institution at a predetermined time interval based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease incidence tendency of the target medical institution; Conducting a disease epidemic analysis based on the disease case occurrence tendency to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; and A step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data is realized.

[0040] According to an embodiment of the present invention, the steps of monitoring the medical data of the target medical institution, obtaining data storage scale information and data increment information of the target medical institution, and determining the information storage system construction means of the target institution based on the data storage scale information and data increment information, specifically include: obtaining historical medical data from a computer at the target medical institution, including electronic health records, laboratory test data, medical imaging data, patient monitoring data, and diagnostic and treatment data; Evaluating the total data volume of the target medical institution based on the historical medical data, and obtaining data scale information of the target medical institution, including the total data volume, the total data volume of each medical data item, and data volume ratio information of each medical data item; Monitoring the medical data of the target medical institution based on the data flow monitoring platform, establishing monitoring channels for different medical data items, monitoring each medical data item in real time based on the monitoring channels, recording the data generation speed and increment of each monitoring channel, and obtaining data increment information; identifying peak periods of data generation based on the data increment information and estimating a maximum data increase during the peak periods; analyzing the data increase trend of each medical data item based on the data increase information and historical medical data to predict the future data increase amount; obtaining data access frequency information of each medical data item, setting a data access frequency threshold, and dividing the medical data of the target medical institution into hot data, warm data, and cold data according to the data access frequency threshold to obtain data access popularity information; Obtaining storage period requirement information of each medical data item of the target medical institution, and determining a storage device type, including SSD, HDD, and cloud storage, for each medical data item of the target medical institution based on the data access popularity information; determining the storage capacity of each storage device type based on the data size information, data increment information, future data increase amount, and medical data storage period requirement information of the target medical institution; determining data processor performance requirements for the target medical institution's data storage system based on the peak period maximum data growth and data access frequency information; and The method includes a step of determining an information storage system configuration means for the target medical institution based on the storage device type, storage flux of the storage device type, and data processor performance requirements of the information storage system of the target medical institution.

[0041] According to an embodiment of the present invention, the step of constructing an information storage system of the target medical institution based on the big data technology and the information storage system construction means specifically includes: constructing a hardware storage system for the target medical institution based on the information storage system construction means; Building a distributed file system of the target medical institution based on big data technology, building data partitions in the distributed file system according to medical data items, and obtaining medical data item storage partitions; obtaining a storage device type corresponding to each medical data item of the target medical institution from the information storage system construction means, determining a storage device type corresponding to each medical data item partition according to the storage device type corresponding to each medical data item, establishing a mapping relationship between the medical data item storage partition and the corresponding storage device type, and obtaining a storage partition-device type mapping table; A step of acquiring a data update request for each medical data item from the target medical institution, and determining a data update period for each medical data item in response to the data update request; The method includes a step of arranging a real-time data collection tool based on the storage partition-device type mapping table and the data update period, collecting data for each medical data item in the target medical institution based on the real-time data collection tool, and storing the data in the corresponding storage device and storage partition according to the data update time period, thereby forming a complete information storage system for the target medical institution.

[0042] According to an embodiment of the present invention, the step of obtaining the diagnostic data information of patients in the target medical institution at a predetermined time period based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease incidence tendency of the target medical institution specifically includes: A step of acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, and extracting disease type information of the patients examined based on the diagnostic data information; a step of aggregating case number information in which different disease types appear at target medical institutions in each time period based on disease type information of patients examined, plotting the case number information in which different disease types appear at target medical institutions in each time period as a case number change curve to obtain a case number change curve dataset, and dividing each curve in the case number change curve dataset into a training set and a test set at a predetermined period ratio; constructing a case number change prediction model based on the ARIMA algorithm, and analyzing the case number change curve dataset by the time series autocorrelation function to determine the autoregression order, difference number, and moving average order of the prediction model; arranging parameters of a case number change prediction model according to the autoregressive order, the number of differences, and the moving average order, introducing the training set into the case number change prediction model to fit and train the model, introducing the test set into the prediction model to evaluate the prediction effect, and if the prediction effect is smaller than a preset effect, adjusting the model parameters until the prediction effect is equal to or greater than the preset effect; A step of acquiring case number change data for a predetermined number of time periods and introducing it into the case number change prediction model, predicting the number of occurrences of cases of different disease types in a future predetermined time period at the target medical institution, and obtaining a prediction result; The method includes plotting the prediction results as a predicted case number change curve dataset, and determining the case occurrence trends of different disease types based on the predicted case number change curve dataset.

[0043] According to an embodiment of the present invention, the step of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the disease epidemic occurrence risk, and obtaining disease epidemic risk data specifically includes: a step of obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in a future time period at the target medical institution based on the case onset tendency, and obtaining case increase rate data; calculating a European distance between the case increase rate data and the case increase rate index data by comparing the case increase rate data and the case increase rate index data, and assessing the risk of epidemic outbreak of different disease types at each time point in a predetermined future time based on the European distance; The method includes steps of presetting a first risk threshold and a second risk threshold, marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease, marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease, and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, and obtaining disease epidemic risk data.

[0044] According to an embodiment of the present invention, the step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: obtaining high-risk epidemic disease types based on the disease epidemic risk data, obtaining drugs and medical equipment required for treating the high-risk epidemic disease types, and obtaining treatment material information; determining a number of cases of the high-risk epidemic disease type based on the case incidence trend, and determining a required amount of treatment supplies based on the number of cases; and The method further includes a step of pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies, and obtaining disease epidemic response measures for the target medical institution.

[0045] A third aspect of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a big data-based medical intelligence AI management program, which, when executed by a processor, realizes the steps of the big data-based medical intelligence AI management method described in any one of the above.

[0046] This invention discloses a medical intelligent AI management method, system, and medium based on big data, which monitors medical institution data, obtains data storage scale and incremental information, determines information storage system construction measures, and builds an information storage system for the target medical institution based on this measure. The system periodically obtains diagnostic data, analyzes the data using an artificial intelligence algorithm, and predicts case occurrence trends. Furthermore, it performs disease epidemic analysis based on the case occurrence trends, evaluates the risk of epidemic outbreaks, and determines response measures. This method effectively improves the efficiency of medical data management and analysis, enables medical institutions to recognize and respond to disease risks in advance, and improves public health safety.

[0047] It should be understood that the disclosed devices and methods in some embodiments of the present application can be realized in other ways. The above device embodiments are merely exemplary. For example, the division of the units is only a logical function division. In actual implementation, other division methods may be used. For example, multiple units or assemblies may be combined or integrated into another system, or some features may be ignored or not implemented. Furthermore, the couplings, direct couplings, or communication connections between each other shown or discussed may be indirect couplings or communication connections through several interfaces, devices, or units, and may be electrical, mechanical, or other types of connections.

[0048] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the present embodiment.

[0049] Furthermore, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may be a single unit, or two or more units may be integrated into one unit, and the integrated unit may be realized in the form of hardware or in the form of a hardware and software functional unit.

[0050] Those skilled in the art will understand that all or part of the steps of the above method embodiments may be realized by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, which, when executed, performs the steps comprising the above method embodiments, and the storage medium includes various media capable of storing program code, such as a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk.

[0051] Alternatively, the above-mentioned integrated units of the present invention can be realized in the form of a software functional unit and stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that essentially contribute to the prior art or the parts of the technical solutions, can be expressed in the form of a software product, and the computer software product is stored in a storage medium and includes some instructions for a computer device (such as a personal computer, a server, or a network device) to execute all or part of the methods described in each embodiment of the present invention. The storage medium can be a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a disk, a CD, or other media that can store various program codes.

[0052] Although the above are only specific embodiments of the present invention, the scope of protection of the present invention is not limited thereto, and any modifications or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present invention are all included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined based on the scope of protection of the claims.

Claims

1. A medical intelligence AI management method based on big data, comprising: monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and data increase information; Building an information storage system for the target medical institution based on big data technology and the information storage system building means; According to the information storage system, obtain the diagnostic data information of patients at the target medical institution at a predetermined time interval, analyze the diagnostic data information according to an artificial intelligence algorithm, and predict the disease incidence tendency of the target medical institution; Conducting a disease epidemic analysis based on the disease case occurrence tendency to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data; The steps of monitoring medical data of the target medical institution, acquiring data storage scale information and data increment information of the target medical institution, and determining the information storage system construction means of the target institution based on the data storage scale information and data increment information, specifically include: obtaining historical medical data from a computer at the target medical institution, including electronic health records, laboratory test data, medical imaging data, patient monitoring data, and diagnostic and treatment data; Evaluating the total data volume of the target medical institution based on the historical medical data, and obtaining data scale information of the target medical institution, including the total data volume, the total data volume of each medical data item, and data volume ratio information of each medical data item; Monitoring the medical data of the target medical institution based on the data flow monitoring platform, establishing monitoring channels for different medical data items, monitoring each medical data item in real time based on the monitoring channels, recording the data generation speed and increment of each monitoring channel, and obtaining data increment information; identifying peak periods of data generation based on the data increment information and estimating a maximum data increase during the peak periods; analyzing the data increase trend of each medical data item based on the data increase information and historical medical data to predict the future data increase amount; obtaining data access frequency information of each medical data item, setting a data access frequency threshold, and dividing the medical data of the target medical institution into hot data, warm data, and cold data according to the data access frequency threshold to obtain data access popularity information; Obtaining storage period requirement information of each medical data item of the target medical institution, and determining a storage device type, including SSD, HDD, and cloud storage, for each medical data item of the target medical institution based on the data access popularity information; determining the storage capacity of each storage device type based on the data size information, data increment information, future data increase amount, and medical data storage period requirement information of the target medical institution; determining data processor performance requirements for the target medical institution's data storage system based on the peak period maximum data growth and data access frequency information; and determining an information storage system construction means of the target institution based on the storage device type, storage flux of the storage device type, and data processor performance requirements of the information storage system of the target medical institution; The step of constructing an information storage system of the target medical institution based on big data technology and the information storage system construction means specifically includes: constructing a hardware storage system for the target medical institution based on the information storage system construction means; Building a distributed file system of the target medical institution based on big data technology, building data partitions in the distributed file system according to medical data items, and obtaining medical data item storage partitions; obtaining a storage device type corresponding to each medical data item of the target medical institution from the information storage system construction means, determining a storage device type corresponding to each medical data item partition according to the storage device type corresponding to each medical data item, establishing a mapping relationship between the medical data item storage partition and the corresponding storage device type, and obtaining a storage partition-device type mapping table; A step of acquiring a data update request for each medical data item from the target medical institution, and determining a data update period for each medical data item in response to the data update request; The method includes the steps of: arranging a real-time data collection tool according to the storage partition-device type mapping table and the data update period; collecting data for each medical data item in the target medical institution according to the real-time data collection tool; and storing the data in the corresponding storage device and storage partition according to the data update time period, thereby forming a complete information storage system for the target medical institution; The step of obtaining the diagnostic data information of patients in the target medical institution at a predetermined time period based on the information storage system, analyzing the diagnostic data information based on an artificial intelligence algorithm, and predicting the disease incidence tendency of the target medical institution specifically includes: A step of acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, and extracting disease type information of the patients examined based on the diagnostic data information; a step of aggregating case number information in which different disease types appear at target medical institutions in each time period based on disease type information of patients examined, plotting the case number information in which different disease types appear at target medical institutions in each time period as a case number change curve to obtain a case number change curve dataset, and dividing each curve in the case number change curve dataset into a training set and a test set at a predetermined period ratio; constructing a case number change prediction model based on the ARIMA algorithm, and analyzing the case number change curve dataset by a time series autocorrelation function to determine the autoregression order, difference number, and moving average order of the prediction model; arranging parameters of a case number change prediction model according to the autoregressive order, the number of differences, and the moving average order, introducing the training set into the case number change prediction model to fit and train the model, introducing the test set into the prediction model to evaluate the prediction effect, and if the prediction effect is smaller than a preset effect, adjusting the model parameters until the prediction effect is equal to or greater than the preset effect; A step of acquiring case number change data for a predetermined number of time periods and introducing it into the case number change prediction model, predicting the number of occurrences of cases of different disease types in a future predetermined time period at the target medical institution, and obtaining a prediction result; A medical intelligence AI management method based on big data, comprising the steps of plotting the prediction results as a predicted case number change curve dataset, and determining the case onset trends of different disease types based on the predicted case number change curve dataset.

2. The step of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the risk of disease epidemic occurrence, and obtaining disease epidemic risk data specifically includes: a step of obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in a future time period at the target medical institution based on the case onset tendency, and obtaining case increase rate data; calculating a European distance between the case increase rate data and the case increase rate index data by comparing the case increase rate data and the case increase rate index data, and assessing the risk of epidemic outbreak of different disease types at each time point in a predetermined future time based on the European distance; The big data-based medical intelligence AI management method of claim 1, further comprising the steps of: pre-setting a first risk threshold and a second risk threshold; marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease; marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease; and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, thereby obtaining disease epidemic risk data.

3. The step of determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: obtaining high-risk epidemic disease types based on the disease epidemic risk data, obtaining drugs and medical equipment required for treating the high-risk epidemic disease types, and obtaining treatment material information; determining a number of cases of the high-risk epidemic disease type based on the case incidence trend, and determining a required amount of treatment supplies based on the number of cases; and The big data-based medical intelligence AI management method according to claim 1, further comprising a step of pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies, and obtaining disease epidemic response measures for the target medical institution.

4. A medical intelligence AI management system based on big data, the medical intelligence AI management system based on big data comprising a memory and a processor, the memory including a medical intelligence AI management method program based on big data, when the medical intelligence AI management method program based on big data is executed by the processor, monitoring medical data of the target medical institution, acquiring data storage scale information and data increase information of the target medical institution, and determining an information storage system construction means of the target institution based on the data storage scale information and data increase information; Building an information storage system for the target medical institution based on big data technology and the information storage system building means; Based on the information storage system, obtain diagnostic data information of patients at the target medical institution at a predetermined time period, analyze the diagnostic data information based on an artificial intelligence algorithm, and predict the disease incidence tendency of the target medical institution; Conducting a disease epidemic analysis based on the disease case occurrence trends to determine the risk of disease epidemic occurrence and obtain disease epidemic risk data; and determining disease epidemic response measures of the target medical institution based on the disease epidemic risk data; Specifically, monitoring medical data of the target medical institution, acquiring data storage scale information and data increment information of the target medical institution, and determining the information storage system construction means of the target institution based on the data storage scale information and data increment information includes: Retrieving historical medical data, including electronic health records, laboratory test data, medical imaging data, patient monitoring data, and diagnostic and treatment data, from the target medical institution's computers; Evaluating the total data volume of the target medical institution based on the historical medical data, and obtaining data scale information of the target medical institution, including the total data volume, the total data volume of each medical data item, and data volume ratio information of each medical data item; Monitoring the medical data of the target medical institution based on the data flow monitoring platform, establishing monitoring channels for different medical data items, monitoring each medical data item in real time based on the monitoring channels, recording the data generation speed and increment of each monitoring channel, and obtaining data increment information; identifying a peak period of data generation based on the data increment information and estimating a maximum data increase during the peak period; Analyzing the data increase trend of each medical data item based on the data increase information and historical medical data to predict the future data increase amount; Obtaining data access frequency information for each medical data item, setting a data access frequency threshold, dividing the medical data of the target medical institution into hot data, warm data, and cold data based on the data access frequency threshold, and obtaining data access popularity information; Obtaining storage period requirement information of each medical data item of the target medical institution, and determining a storage device type, including SSD, HDD, and cloud storage, for each medical data item of the target medical institution based on the data access popularity information; determining the storage capacity of each storage device type based on the data size information, data increment information, future data increase amount, and medical data storage period requirement information of the target medical institution; determining data processor performance requirements for the target medical institution's data storage system based on the peak period maximum data growth and data access frequency information; and determining an information storage system construction means for the target medical institution based on the storage device type, storage flux of the storage device type, and data processor performance requirements of the information storage system of the target medical institution; Specifically, building an information storage system for a target medical institution based on big data technology and the information storage system building means includes: constructing a hardware storage system for the target medical institution based on the information storage system construction means; Building a distributed file system of the target medical institution based on big data technology, building data partitions in the distributed file system according to medical data items, and obtaining medical data item storage partitions; Obtaining a storage device type corresponding to each medical data item of the target medical institution from the information storage system construction means, determining a storage device type corresponding to each medical data item partition based on the storage device type corresponding to each medical data item, establishing a mapping relationship between the medical data item storage partition and the corresponding storage device type, and obtaining a storage partition-device type mapping table; Acquiring a data update request for each medical data item from the target medical institution, and determining a data update period for each medical data item in accordance with the data update request; and arranging a real-time data collection tool according to the storage partition-device type mapping table and the data update period; collecting data for each medical data item in the target medical institution according to the real-time data collection tool, and storing it in the corresponding storage device and storage partition according to the data update time period, thereby forming a complete information storage system for the target medical institution; Specifically, the information storage system acquires diagnostic data information of patients at the target medical institution at a predetermined time interval, analyzes the diagnostic data information based on an artificial intelligence algorithm, and predicts the disease incidence tendency of the target medical institution. acquiring diagnostic data information of patients examined at the target medical institution at a predetermined time period based on the information storage system, and extracting disease type information of the patients examined based on the diagnostic data information; aggregating case number information in which different disease types appear at target medical institutions in each time period based on disease type information of patients examined, plotting the case number information in which different disease types appear at target medical institutions in each time period as a case number change curve to obtain a case number change curve dataset, and dividing each curve in the case number change curve dataset into a training set and a test set at a predetermined period ratio; Building a case count change prediction model based on the ARIMA algorithm, and analyzing the case count change curve dataset by a time series autocorrelation function to determine the autoregression order, difference number, and moving average order of the prediction model; arranging parameters of a case number change prediction model according to the autoregressive order, the number of differences, and the moving average order, introducing the training set into the case number change prediction model to fit and train the model, introducing the test set into the prediction model to evaluate the prediction effect, and if the predicted effect is smaller than a preset effect, adjusting the model parameters until the predicted effect is equal to or greater than the preset effect; Obtaining case number change data for a predetermined number of time periods and introducing it into the case number change prediction model, predicting the number of future occurrences of cases of different disease types in a predetermined time period at the target medical institution, and obtaining prediction results; A medical intelligence AI management system based on big data, comprising: plotting the prediction results as a predicted case number change curve dataset; and determining the case onset trends of different disease types based on the predicted case number change curve dataset.

5. The method of performing a disease epidemic analysis based on the disease case occurrence tendency, determining the risk of disease epidemic occurrence, and obtaining disease epidemic risk data specifically includes: Obtaining case increase rate index data for evaluating disease prevalence, determining case increase rates for different disease types at each time point in a future time period at a target medical institution based on the case onset tendency, and obtaining case increase rate data; Calculating a European distance between the case growth rate data and the case growth rate index data by comparing the case growth rate data and the case growth rate index data, and assessing the risk of epidemic outbreak of different disease types at each time point in a predetermined future time based on the European distance; The big data-based medical intelligent AI management system of claim 4, further comprising: presetting a first risk threshold and a second risk threshold; marking a disease whose epidemic outbreak risk is greater than the first risk threshold as a high-risk epidemic disease; marking a disease whose epidemic outbreak risk is between the first risk threshold and the second risk threshold as a medium-risk epidemic disease; and marking a disease whose epidemic outbreak risk is less than the second risk threshold as a low-risk epidemic disease, thereby obtaining disease epidemic risk data.

6. Determining the disease epidemic response measures of the target medical institution based on the disease epidemic risk data specifically includes: Obtaining high-risk epidemic disease types based on the disease epidemic risk data, obtaining drugs and medical equipment required for treating the high-risk epidemic disease types, and obtaining treatment supply information; determining the number of cases of the high-risk epidemic disease type based on the case incidence trends, and determining the required amount of treatment supplies based on the number of cases; and The big data-based medical intelligent AI management system according to claim 4, further comprising: pre-stocking medical supplies for the target medical institution based on the medical supply information and the required amount of medical supplies; and obtaining disease epidemic response measures for the target medical institution.

7. A computer-readable storage medium, comprising: a big data-based medical intelligence AI management program; and when the big data-based medical intelligence AI management program is executed by a processor, the computer-readable storage medium realizes the steps of the big data-based medical intelligence AI management method according to any one of claims 1 to 3.

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