Intelligent hospital epidemic prevention and control method and system

Abstract

The present application relates to the technical field of public health, in particular to a smart hospital epidemic prevention and control method and system, comprising the following steps: based on patient symptom examination information, analyzing patient medical record, medical detection result, interpersonal contact history information, evaluating the health status and infection risk of multiple patients, and generating an infection risk evaluation framework. In the present application, by analyzing patient symptom examination information and interpersonal contact history information, the health status and infection risk of multiple patients are evaluated, key transmission nodes are identified, the identification ability of the transmission path of infectious diseases is improved, the accuracy and efficiency of identifying latent infected persons are optimized, real-time epidemic data is combined to simulate and predict the development trend of the epidemic, the efficiency and matching degree of hospital resource scheduling are optimized, the speed and efficiency of epidemic response are improved, the outbreak and spread of the epidemic in the hospital are reduced, the safety of medical staff and patients is ensured, and the response ability of the hospital to the epidemic is improved.

Description

Technical Field

[0001] The present invention relates to the field of public health technology, and in particular to a smart hospital epidemic prevention and control method and system. Background Art

[0002] The field of public health technology aims to achieve multiple goals of disease prevention, life extension, and health promotion through various social organizations and collectives, including disease monitoring, vaccine development, health data analysis, environmental health, health education, and policy making. By controlling the spread of infectious diseases, improving sanitary conditions, reducing environmental hazards, and enhancing community health awareness, it uses epidemiological surveys, disease monitoring systems, health data analysis, and epidemic response strategies, combined with big data, cloud computing, and artificial intelligence to improve the efficiency of disease prevention and control, conduct health education and behavioral intervention, reduce public health threats, and improve the overall health of society.

[0003] Among them, the smart hospital epidemic prevention and control method aims to monitor, prevent and control the spread of infectious diseases through information and communication technology in the hospital environment, including the use of automated systems to monitor the health status of patients and medical staff, the sanitary conditions in the hospital in real time, and predict the development trend of the epidemic through data analysis, respond to health emergencies, optimize the allocation of human resources and materials during the epidemic, reduce the outbreak and spread of epidemics within the hospital, protect the safety of medical staff and patients, and improve the hospital's response capabilities to public health emergencies.

[0004] Traditional epidemic prevention and control technologies rely on traditional epidemiological surveys and disease monitoring systems, and have deficiencies in data processing and epidemic prediction. In a rapidly changing epidemic environment, the identification of latently infected people and the prediction of epidemic trends are not accurate enough, resulting in delayed and mismatched epidemic response measures. There is a lack of flexibility and foresight in resource allocation, resulting in resources being adjusted only after the outbreak, increasing the risk of hospital-acquired infections and leading to waste and shortage of resources. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a smart hospital epidemic prevention and control method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution, a smart hospital epidemic prevention and control method, comprising the following steps:

[0007] S1: Based on patient symptom examination information, analyze the patient's medical records, medical test results, and interpersonal contact history information to assess the health status and infection risk of multiple patients and generate an infection risk assessment framework;

[0008] S2: Based on the infection risk assessment framework, by analyzing the pathological characteristics and interpersonal contact information of multiple patients, the impact of multiple patients on the infection risk is evaluated, key transmission nodes are identified, and transmission node analysis results are obtained;

[0009] S3: Based on the transmission node analysis results, combined with the patient's contact history and movement trajectory, predict and identify infected persons in the incubation period to form an incubation period prediction result;

[0010] S4: Based on the incubation period prediction results, combined with real-time epidemic data, including confirmed cases and recovered cases, simulate and predict the epidemic development trend to obtain epidemic trend assessment information;

[0011] S5: Based on the epidemic trend assessment information and combined with historical material consumption data, hospital resource needs are predicted in real time, and material and manpower allocations are adjusted to form resource demand calculation data;

[0012] S6: Based on the resource demand calculation data, adjust the work schedule of medical staff, optimize human resource allocation and utilization, and generate hospital prevention and control scheduling information.

[0013] As a further solution of the present invention, the infection risk assessment framework includes information on the stage of symptoms of infected persons, virus infectivity score information, and patient immunity level information; the transmission node analysis results include key infected person identification results, contact frequency data, and infection probability assessment information; the incubation period prediction results include a list of risk infected persons, incubation period assessment results, and infection development probability information; the epidemic trend assessment information includes an epidemic growth curve, virus mutation analysis results, and epidemic spread speed prediction information; the resource demand calculation data includes medical supplies demand prediction results, human resources demand prediction information, and resource allocation results at key time points; the hospital prevention and control scheduling information includes work schedule optimization records, job personnel allocation information, and emergency response team configuration information.

[0014] As a further embodiment of the present invention, based on patient symptom examination information, the patient's medical records, medical test results, and interpersonal contact history information are analyzed to assess the health status and infection risk of multiple patients. The steps of generating an infection risk assessment framework are as follows:

[0015] S101: Based on the patient's symptom examination information, collect and organize the patient's medical records, medical test results, and interpersonal contact history information to generate a patient medical record dataset;

[0016] S102: Analyze the health status and interpersonal contact history of multiple patients based on the patient medical record dataset, record the social activity frequency and number of contacts of the target patient, calculate the infection risk index, and generate contact history analysis results;

[0017] S103: Based on the contact history analysis result, the infection risk of multiple patients is evaluated considering the influence of health conditions and social behaviors, and an infection risk evaluation framework is generated.

[0018] As a further scheme of the present application, based on the infection risk evaluation framework, the influence degree of multiple patients on the infection risk is evaluated by analyzing the pathological characteristics and interpersonal contact information of the multiple patients, key transmission nodes are identified, and the transmission node analysis result is obtained, and the steps are specifically as follows:

[0019] S201: Based on the infection risk evaluation framework, the pathological characteristics and interpersonal contact information of the patients are analyzed and recorded, the risk influencing factors of multiple patients are evaluated and identified, and infection influence analysis data is generated;

[0020] S202: Based on the infection influence analysis data, the virus transmission risk data of multiple patients is calculated, the key patients affecting the transmission of the epidemic are identified, and the key node calculation result is obtained;

[0021] S203: Based on the key node calculation result, the transmission ability of multiple virus transmission nodes is analyzed, the nodes affecting the spread of the epidemic are identified, and the transmission node analysis result is obtained.

[0022] As a further scheme of the present application, based on the transmission node analysis result, in combination with the contact history and movement trajectory of the patients, the infected persons in the incubation period are predicted and identified, and the incubation period prediction result is formed, and the steps are specifically as follows:

[0023] S301: Based on the transmission node analysis result, by analyzing the key transmission patients, the contact history and movement trajectory of the target patients are recorded, the contact history data is generated, and a contact person analysis data set is generated;

[0024] S302: Based on the contact person analysis data set, a K-neighbor algorithm is used to analyze the contact times and durations of multiple contact persons, calculate the infection risk scores of multiple contact persons, match risk levels for multiple contact persons, and generate infection risk evaluation data;

[0025] S303: Based on the infection risk evaluation data, in combination with real-time epidemic data, infected contact persons are identified, the incubation period characteristics of multiple risk contact persons are analyzed and recorded, and an incubation period prediction result is generated.

[0026] As a further scheme of the present application, based on the incubation period prediction result, in combination with real-time epidemic data including confirmed cases and recovered cases, the development trend of the epidemic is simulated and predicted, and the epidemic trend evaluation information is obtained, and the steps are specifically as follows:

[0027] S401: Based on the incubation period prediction result, combined with real-time epidemic data, including confirmed cases and recovered cases, analyze the time series characteristics of the epidemic data and build an epidemic data model to generate an epidemic data analysis record;

[0028] S402: Based on the epidemic data analysis records, analyzing and identifying epidemic change indicators, including the ratio of new cases to recovered cases, performing trend analysis on the infection data, and generating change trend analysis data;

[0029] S403: Utilize the change trend analysis data to simulate and predict the epidemic development path, combine with historical epidemic change data, calculate the infection curve changes, and generate epidemic trend assessment information.

[0030] As a further solution of the present invention, based on the epidemic trend assessment information and combined with historical material consumption data, the hospital resource demand is predicted in real time, and the material and manpower allocation is adjusted to form resource demand calculation data. Specifically, the steps are as follows:

[0031] S501: Analyze historical epidemic material consumption based on the epidemic trend assessment information, combine it with real-time epidemic information, evaluate and calculate real-time resource demand changes, and obtain material consumption comparison results;

[0032] S502: Based on the material consumption comparison result and in combination with the hospital's real-time available beds and human resources, material and manpower requirements are predicted and calculated to obtain expected demand analysis information;

[0033] S503: According to the expected demand analysis information, the hospital's material allocation and manpower allocation are adjusted to optimize the consistency between resource allocation and actual demand, thereby forming resource demand calculation data.

[0034] As a further solution of the present invention, based on the resource demand calculation data, the steps of adjusting the work schedule of medical staff, optimizing human resource allocation and utilization, and generating hospital prevention and control scheduling information are specifically as follows:

[0035] S601: Based on the resource demand calculation data, the work intensity and work schedule of multiple medical staff are evaluated in real time to obtain a work time evaluation result;

[0036] S602: Based on the work time evaluation result, the medical staff's schedule is adjusted according to the staff type and work intensity, taking into account alternating shifts and emergency response needs, to obtain a schedule time calculation result;

[0037] S603: Using the scheduling time calculation results, taking into account response speed and medical quality, adjust human resource allocation in real time, optimize human resource allocation and utilization, and generate hospital prevention and control scheduling information.

[0038] A smart hospital epidemic prevention and control system, which is used to implement the above-mentioned smart hospital epidemic prevention and control method, includes:

[0039] The patient information assessment module analyzes the patient's symptom examination information, medical records, medical test results, and interpersonal contact history information based on the patient's symptom examination information, assesses the health status and infection risk, calculates the health and infection index, and generates risk patient file information;

[0040] The impact path analysis module analyzes the patient's pathological characteristics and interpersonal contact information based on the risk patient's archival information, evaluates the patient's impact on the infection risk, identifies key transmission nodes, and generates node impact assessment information;

[0041] The infection probability calculation module analyzes the patient's contact history and movement trajectory based on the node impact assessment information, evaluates the infection probability of multiple contacts, and generates an infection risk analysis record;

[0042] The epidemic spread assessment module simulates and analyzes the epidemic spread and development trends based on the infection risk analysis records and real-time epidemic data, and generates development trend forecast information;

[0043] Based on the development trend forecast information, combined with historical material consumption data and real-time resource status, the epidemic prevention resource scheduling module adjusts the hospital's material and human resource allocation, optimizes the work schedule of medical staff, and generates hospital prevention and control scheduling information.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by analyzing patient symptom examination information and interpersonal contact history information, the health status and infection risk of multiple patients are evaluated, key transmission nodes are identified, the ability to identify the transmission path of infectious diseases is improved, and the accuracy and efficiency of identifying latent infected persons are optimized, helping hospitals to make prevention and control preparations in advance. Combined with real-time epidemic data, the development trend of the epidemic is simulated and predicted, the accuracy of the prediction is improved, the efficiency and matching degree of hospital resource scheduling are optimized, the speed and efficiency of epidemic response are improved, the outbreak and spread of the epidemic within the hospital are reduced, the safety of medical staff and patients is guaranteed, and the hospital's response ability to the epidemic is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0048] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0049] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0050] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0051] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0052] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0053] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] Example 1

[0057] See also Figure 1 The present invention provides a technical solution, a smart hospital epidemic prevention and control method, comprising the following steps:

[0058] S1: Based on patient symptom examination information, analyze the patient's medical records, medical test results, and interpersonal contact history information to assess the health status and infection risk of multiple patients and generate an infection risk assessment framework;

[0059] S2: Based on the infection risk assessment framework, by analyzing the pathological characteristics and interpersonal contact information of multiple patients, the impact of multiple patients on the infection risk is evaluated, key transmission nodes are identified, and transmission node analysis results are obtained;

[0060] S3: Based on the results of the transmission node analysis, combined with the patient's contact history and movement trajectory, predict and identify infected people in the incubation period to form an incubation period prediction result;

[0061] S4: Based on the incubation period prediction results and combined with real-time epidemic data, including confirmed cases and recovered cases, simulate and predict the epidemic development trend to obtain epidemic trend assessment information;

[0062] S5: Based on epidemic trend assessment information and historical material consumption data, hospital resource needs are predicted in real time, and material and manpower allocations are adjusted to form resource demand calculation data;

[0063] S6: Based on resource demand calculation data, adjust the work schedule of medical staff, optimize human resource allocation and utilization, and generate hospital prevention and control scheduling information.

[0064] The infection risk assessment framework includes information on the stage of symptoms of infected individuals, viral infectivity scores, and patient immunity levels. Transmission node analysis results include identification of key infected individuals, contact frequency data, and infection probability assessments. Incubation period predictions include a list of risky individuals, incubation period assessments, and infection progression probability information. Epidemic trend assessments include epidemic growth curves, viral mutation analysis results, and epidemic spread rate forecasts. Resource demand calculation data includes forecasts for medical supplies and human resources needs, as well as resource allocation at key time points. Hospital prevention and control scheduling information includes optimized work schedules, position allocation information, and emergency response team configurations.

[0065] See also Figure 2 Based on the patient symptom examination information, the patient's medical records, medical test results, and interpersonal contact history information are analyzed to assess the health status and infection risk of multiple patients. The specific steps to generate the infection risk assessment framework are as follows:

[0066] S101: Based on the patient's symptom examination information, the specific steps of collecting and organizing the patient's medical records, medical test results, and interpersonal contact history information to generate the patient's medical record data set are as follows;

[0067] In sub-step S101, based on the patient's symptom examination information, the patient's medical records, medical test results, and interpersonal contact history information are collected and organized. The patient's medical records for each visit are collected in real time, coded and stored. The medical test results are automatically updated through the medical information system. The interpersonal contact history is organized through the visit records. The medical record integration algorithm is used to associate and synchronize the data. The data is pre-processed, including removing irrelevant and erroneous items. The correlation algorithm is used to determine the correlation between the data. The formula is: Among them, f(D) represents the integration function of the medical record data set, d i represents the i-th data item, w i is the weight coefficient, which indicates the importance of the target data item in the integration to generate the patient medical record dataset.

[0068] S102: Based on the patient medical record dataset, the health status and interpersonal contact history of multiple patients are analyzed, the social activity frequency and number of contacts of the target patient are recorded, the infection risk index is calculated, and the specific steps for generating the contact history analysis results are as follows;

[0069] In sub-step S102, based on the patient medical record data set, the health status and interpersonal contact history of multiple patients are analyzed, the social activity frequency and number of contacts of the target patient are recorded, and the infection risk index is calculated. The social activity frequency of each patient is determined by the frequency analysis algorithm, and the contact network construction algorithm is used to establish the contact network of each patient. The infection risk index is calculated. The formula is: ,

[0070] Among them, R is the infection risk index, f soc represents the frequency of social activities, n con is the number of contacts, β is the transmission parameter, and c is the adjustment coefficient, which determines the relative importance of social frequency and number of contacts in risk assessment and generates the results of contact history analysis.

[0071] S103: Based on the results of contact history analysis, the infection risk of multiple patients is assessed, and the influence of health status and social behavior is considered. The specific steps for generating an infection risk assessment framework are as follows;

[0072] In sub-step S103, based on the results of the contact history analysis, the infection risk of multiple patients is evaluated, the influence of health status and social behavior is considered, a risk matrix is ​​generated based on health data and social data, and the risk assessment formula is applied for analysis. Taking into account the health status and social behavior of individuals, the probability of infection is predicted. The formula is P = γ·(h·w h +s·w s ), where P represents the risk of infection, h is the health status indicator, s is the social behavior indicator, and w h and w s are the weight coefficients of health and sociality respectively, and γ is the adjustment coefficient to generate the infection risk assessment framework.

[0073] See also Figure 3 Based on the infection risk assessment framework, by analyzing the pathological characteristics and interpersonal contact information of multiple patients, the impact of multiple patients on the infection risk is evaluated, and key transmission nodes are identified. The specific steps to obtain the transmission node analysis results are as follows:

[0074] S201: Based on the infection risk assessment framework, analyze and record the patient's pathological characteristics and interpersonal contact information, evaluate and identify risk factors for multiple patients, and generate infection impact analysis data. The specific steps are as follows;

[0075] In sub-step S201, based on the infection risk assessment framework, the patient's pathological characteristics and interpersonal contact information are analyzed and recorded, and the risk influencing factors of multiple patients are evaluated and identified. The pathological characteristic analysis module is used to record the clinical pathological data of each patient, integrate the patient's social network data, identify the frequency and intensity of multiple contact levels, and calculate the risk score through the risk factor assessment algorithm. The formula is F = α·(p·w p +c·w c ), where F represents the risk score, p is the pathological characteristic score, c is the interpersonal contact intensity, and w p and w c are the weight coefficients for pathology and exposure, respectively, and α is the adjustment factor for risk assessment to generate infection impact analysis data.

[0076] S202: Based on the infection impact analysis data, calculate the virus transmission risk data of multiple patients, identify key patients who affect the spread of the epidemic, and obtain the calculation results of key nodes. The specific steps are:

[0077] In sub-step S202, based on the infection impact analysis data, the virus transmission risk data of multiple patients is calculated to identify key patients who affect the spread of the epidemic. According to the pathological characteristics and social network information in the infection impact analysis data, the virus transmission model is applied to quantify the transmission risk of each patient. The key patient identification algorithm is used to identify the key nodes of the epidemic by comparing and sorting the transmission risk values. The formula is: Among them, R k Represents the risk value of the key node, v i is the viral load of the i-th patient, r i is the contact network risk value of the i-th patient, δ is the adjustment factor of the transmission risk, and the calculation results of the key nodes are obtained.

[0078] S203: Based on the calculation results of key nodes, the propagation capabilities of multiple virus propagation nodes are analyzed to identify nodes that affect the spread of the epidemic. The specific steps for obtaining the propagation node analysis results are as follows:

[0079] In sub-step S203, based on the calculation results of key nodes, the propagation capabilities of multiple virus transmission nodes are analyzed, and the nodes that affect the spread of the epidemic are identified. The node propagation capability analysis module is used to evaluate the propagation capability of each key node, including the contact frequency and the sensitivity of the contact population. The propagation model optimization algorithm is applied to analyze and optimize the propagation capability data, and the spread path of the epidemic is predicted and controlled. The formula is: Among them, S represents the node's propagation ability score, f j is the contact frequency of the jth node, s j is the sensitivity score of the contact population, θ is the optimization factor, and the propagation node analysis results are obtained.

[0080] See also Figure 4 Based on the results of the transmission node analysis, combined with the patient's contact history and movement trajectory, the infected person in the incubation period is predicted and identified. The specific steps to form the incubation period prediction result are as follows:

[0081] S301: Based on the results of the propagation node analysis, by analyzing key propagation patients, recording the contact history and movement trajectory of the target patient, and the contact history data, the specific steps of generating a contact analysis data set are as follows;

[0082] In sub-step S301, based on the results of the propagation node analysis, by analyzing the key propagation patients, the contact history and movement trajectory of the target patients are recorded, and the geographic location data of each key propagation patient is collected using the movement trajectory recording module. The time, duration and contact information of each contact event are recorded through location tracking devices and mobile phone application data, and the contact relationship is analyzed to form a structured contact data set. The formula is Among them, C i represents the contact analysis score of the i-th patient, h k is the historical intensity of the kth contact event, λ k is the time weighting coefficient, which reflects the distance of the contact time and generates the contact analysis data set.

[0083] S302: Based on the contact analysis data set, using the K-nearest neighbor algorithm, analyze the number and duration of contacts of multiple contacts, calculate the infection risk scores of multiple contacts, match the risk levels of multiple contacts, and generate infection risk assessment data. The specific steps are as follows:

[0084] In sub-step S302, based on the contact analysis dataset, the number and duration of contacts for each contact recorded in the dataset are counted. By constructing a contact behavior feature vector, including contact frequency and total contact duration, the K-nearest neighbor algorithm is used to cluster the behavior patterns of similar contacts. Based on the contact behavior and clustering results of each contact, the infection risk score is calculated. The infection risk score is evaluated based on the similarity between the number and duration of contacts and infection cases. The calculated risk score is mapped to multiple risk levels, and a risk level is matched for each contact to identify the epidemic transmission chain and generate infection risk assessment data.

[0085] S303: Based on the infection risk assessment data and combined with real-time epidemic data, identifying infected contacts, analyzing and recording the incubation period characteristics of multiple risk contacts, and generating incubation period prediction results. The specific steps are as follows:

[0086] In sub-step S303, based on the infection risk assessment data and combined with real-time epidemic data, infected contacts are identified, the incubation period characteristics of multiple risk contacts are analyzed and recorded, real-time epidemic data and health status data of contacts are collected, and the incubation period prediction algorithm is applied to analyze data trends and time series, and the range of the incubation period is calculated. The incubation period prediction formula is: Among them, L p represents the incubation period prediction result, t is the current time, t0 is the contact time, τ is the time decay constant, and η is the normalization constant, which is used to adjust the prediction accuracy and generate the incubation period prediction result.

[0087] See also Figure 5 Based on the incubation period prediction results and combined with real-time epidemic data, including confirmed and recovered cases, the steps to simulate and predict the epidemic development trend and obtain epidemic trend assessment information are as follows:

[0088] S401: Based on the incubation period prediction results, combined with real-time epidemic data, including confirmed cases and recovered cases, analyze the time series characteristics of the epidemic data and build an epidemic data model to generate epidemic data analysis records. The specific steps are as follows:

[0089] In sub-step S401, based on the incubation period prediction results and combined with real-time epidemic data, including confirmed cases and recovered cases, target data are collected and integrated, and time series analysis is performed on the data to identify key time nodes and changing trends in the development of the epidemic, extract the cyclical patterns of epidemic growth and slowdown, and build an epidemic data model to analyze historical data and real-time data in combination with the incubation period data to improve the accuracy of the prediction, help medical institutions and public health officials identify epidemic dynamics, and generate epidemic data analysis records.

[0090] S402: Based on the epidemic data analysis records, analyze and identify epidemic change indicators, including the ratio of new and recovered cases, and perform trend analysis on the infection data. The specific steps for generating change trend analysis data are as follows;

[0091] In sub-step S402, based on the epidemic data analysis records, analyze and identify epidemic change indicators, including the ratio of new and recovered cases, perform trend analysis on infection data, calculate the daily ratio of new and recovered cases, use target data to evaluate the development speed and control effect of the epidemic, use linear regression analysis method to perform trend analysis on the target ratio, predict the development of the epidemic, and generate change trend analysis data.

[0092] S403: Using the trend analysis data to simulate and predict the epidemic development path, combining historical epidemic change data to calculate the infection curve changes, and generating epidemic trend assessment information. The specific steps are:

[0093] In sub-step S403, the trend analysis data is used to simulate and predict the development path of the epidemic. The infection curve changes are calculated by combining the historical epidemic change data. The epidemic simulation module is used to build an infection curve model based on historical and current trend data to predict the development of the epidemic. The exponential smoothing method is used to predict the infection curve.

[0094] Control the impact of historical data on predictions and generate epidemic trend assessment information.

[0095] See also Figure 6 Based on the epidemic trend assessment information and combined with historical material consumption data, the hospital resource demand is predicted in real time, and the material and manpower allocation is adjusted to form the resource demand calculation data. The specific steps are as follows:

[0096] S501: Analyze historical epidemic material consumption based on epidemic trend assessment information, combine with real-time epidemic information, evaluate and calculate real-time resource demand changes, and obtain material consumption comparison results.

[0097] In sub-step S501, based on the epidemic trend assessment information, historical epidemic material consumption is analyzed. Combined with real-time epidemic information, real-time resource demand changes are evaluated and calculated. Historical material consumption data and current epidemic data are collected. By comparing the consumption of the same period in history with the current real-time data, trend analysis methods are used to evaluate changes in resource demand. The resource demand forecasting model is described by the following formula: , where Q t represents the resource demand at time point t, D t and D t-1 They represent the epidemic data at time points t and t-1 respectively, and ρ is the adjustment coefficient, which reflects the impact of epidemic changes on resource demand, and the comparison results of material consumption are obtained.

[0098] S502: Based on the material consumption comparison results, combined with the hospital's real-time available beds and human resources, predict and calculate material and manpower requirements to obtain expected demand analysis information. Specific steps are:

[0099] In sub-step S502, based on the material consumption comparison results, combined with the hospital's real-time available beds and human resources, the material and manpower requirements are predicted and calculated. The resource matching module is used to calculate the material and manpower requirements based on the material consumption comparison results and the hospital's real-time resource data, including the availability of beds and medical staff. The expected demand model uses the formula Among them, N t represents the expected total resource demand, Q t is the predicted material demand, B t and H t are the available numbers of hospital beds and medical staff respectively, σ and μ are the resource allocation coefficients, and the expected demand analysis information is obtained.

[0100] S503: Based on the expected demand analysis information, the specific steps of adjusting the hospital's material allocation and manpower allocation to optimize the consistency between resource allocation and actual demand and forming resource demand calculation data are as follows;

[0101] In sub-step S503, according to the expected demand analysis information, the hospital's material allocation and manpower allocation are adjusted to optimize the consistency between resource allocation and actual demand. According to the expected demand results, a material and manpower reallocation plan is formulated. Through dynamic adjustment strategies, the resource allocation is ensured to be consistent with the actual demand and the resource utilization efficiency is improved. The resource adjustment model is A t =δ·N t +(1-δ)·A t-1 , where A t Represents the adjusted resource allocation, N t is the newly calculated resource demand, δ is the adjustment response coefficient, A t-1 It forms resource demand calculation data for resource allocation in the previous cycle.

[0102] See also Figure 7 Based on the resource demand calculation data, the steps to adjust the work schedule of medical staff, optimize human resource allocation and utilization, and generate hospital prevention and control scheduling information are as follows:

[0103] S601: Based on the resource demand calculation data, the specific steps of evaluating the work intensity and work schedule of multiple medical staff in real time and obtaining the work time evaluation result are as follows;

[0104] In sub-step S601, based on the resource demand calculation data, the work intensity and work schedule of multiple medical staff are evaluated in real time. The work intensity evaluation module is used to collect the current work records and resource demand data of medical staff. The workload analysis algorithm is used to calculate the workload of each medical staff and predict the work intensity. The work time evaluation model uses the formula Among them, W t represents the total work intensity at time point t, t i is the working hours of the i-th medical staff, l i is the corresponding workload coefficient, κ is the work efficiency adjustment coefficient, and the working time evaluation result is obtained.

[0105] S602: Based on the work time assessment results, the medical staff's shift schedule is adjusted according to the personnel type and work intensity, taking into account the alternating shifts and emergency response needs. The specific steps for obtaining the shift schedule calculation result are as follows:

[0106] In sub-step S602, based on the work time evaluation results, the medical staff's shift schedule is adjusted according to the personnel type and work intensity, taking into account the alternating shifts and emergency response needs. The shift optimization module is used to build a shift system based on the work intensity and professional type of each medical staff member. The shift time calculation model uses the formula Among them, S t represents the optimal value of the shift schedule, f j is the job type factor of the jth medical staff, x j Assign variables to working time, θ is the scheduling efficiency coefficient, and the scheduling time calculation results are obtained.

[0107] S603: Using the scheduling calculation results, considering response speed and medical quality, adjusting human resource allocation in real time, optimizing human resource allocation and utilization, and generating hospital prevention and control scheduling information. The specific steps are:

[0108] In sub-step S603, the scheduling time calculation results are used to consider response speed and medical quality, adjust human resource allocation in real time, optimize human resource allocation and utilization, and dynamically adjust human resource allocation to match the changing workload through the human resource optimization module, combining the scheduling time calculation results and actual medical needs. The human resource adjustment model uses the formula Among them, H t represents the adjusted human resource allocation, g k Score the work intensity of the kth medical staff, v k is the change in work demand, ξ is the resource adjustment response coefficient, H t-1 Generate hospital prevention and control scheduling information for the human resource allocation of the previous calculation cycle.

[0109] See also Figure 8 A smart hospital epidemic prevention and control system is provided. The smart hospital epidemic prevention and control system is used to implement the above-mentioned smart hospital epidemic prevention and control method. The system includes:

[0110] The patient information assessment module analyzes the patient's symptom examination information, medical records, medical test results, and interpersonal contact history information based on the patient's symptom examination information, assesses the health status and infection risk, calculates the health and infection index, and generates risk patient file information;

[0111] The impact path analysis module analyzes the patient's pathological characteristics and interpersonal contact information based on the risk patient's archival information, assesses the patient's impact on the infection risk, identifies key transmission nodes, and generates node impact assessment information;

[0112] The infection probability calculation module analyzes the patient's contact history and movement trajectory based on the node impact assessment information, evaluates the infection probability of multiple contacts, and generates infection risk analysis records;

[0113] The epidemic spread assessment module simulates and analyzes the epidemic spread and development trends based on infection risk analysis records and real-time epidemic data, generating development trend forecast information;

[0114] The epidemic prevention resource scheduling module adjusts the hospital's material and human resource allocation based on development trend forecast information, combined with historical material consumption data and real-time resource status, optimizes the work schedule of medical staff, and generates hospital prevention and control scheduling information.

[0115] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A smart hospital epidemic prevention and control method, characterized in that: The following steps are involved: Based on patient symptom examination information, analyze patients' medical records, medical test results, and interpersonal contact history information to assess the health status and infection risk of multiple patients and generate an infection risk assessment framework; Based on the infection risk assessment framework, by recording the clinical pathology data of each patient, integrating the patient's social network data, identifying the frequency and intensity of multiple contact levels, and using the risk factor assessment algorithm to evaluate the impact of multiple patients on the infection risk, the virus transmission model is applied to quantify the transmission risk of each patient. Using the key patient identification algorithm, by comparing and sorting the transmission risk values, the key nodes of the epidemic spread are identified, and the transmission capacity of each key node is evaluated, including the contact frequency and the sensitivity of the contact population. The transmission model optimization algorithm is applied to predict and control the spread path of the epidemic and obtain the results of the transmission node analysis; Based on the transmission node analysis results, combined with the patient's contact history and movement trajectory, predict and identify infected people in the incubation period to form an incubation period prediction result; Based on the incubation period prediction results, combined with real-time epidemic data, including confirmed cases and recovered cases, the epidemic development trend is simulated and predicted to obtain epidemic trend assessment information; Based on the epidemic trend assessment information and combined with historical material consumption data, hospital resource needs are predicted in real time, and material and manpower allocations are adjusted to form resource demand calculation data; Based on the resource demand calculation data, the work schedule of medical staff is adjusted, human resource allocation and utilization are optimized, and hospital prevention and control scheduling information is generated.

2. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: The infection risk assessment framework includes information on the stage of symptoms of infected persons, virus infectivity score information, and patient immunity level information. The transmission node analysis results include key infected person identification results, contact frequency data, and infection probability assessment information. The incubation period prediction results include a list of risk infected persons, incubation period assessment results, and infection development probability information. The epidemic trend assessment information includes the epidemic growth curve, virus mutation analysis results, and epidemic spread speed prediction information. The resource demand calculation data includes medical supplies demand prediction results, human resources demand prediction information, and resource allocation results at key time points. The hospital prevention and control scheduling information includes work schedule optimization records, job personnel allocation information, and emergency response team configuration information.

3. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: Based on the patient symptom examination information, the patient's medical records, medical test results, and interpersonal contact history information are analyzed to assess the health status and infection risk of multiple patients. The specific steps to generate the infection risk assessment framework are as follows: Based on the patient's symptom examination information, collect and organize the patient's medical records, medical test results, and interpersonal contact history information to generate a patient medical record data set; Based on the patient medical record dataset, the health status and interpersonal contact history of multiple patients are analyzed, the social activity frequency and number of contacts of the target patient are recorded, the infection risk index is calculated, and the contact history analysis results are generated; Based on the contact history analysis results, the infection risk of multiple patients is evaluated, and the influence of health status and social behavior is considered to generate an infection risk assessment framework.

4. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: Based on the transmission node analysis results, combined with the patient's contact history and movement trajectory, the infected person in the incubation period is predicted and identified. The steps for forming the incubation period prediction result are as follows: Based on the analysis results of the transmission nodes, by analyzing key transmission patients, recording the contact history and movement trajectory of the target patients, and contact history data, a contact analysis dataset is generated; Based on the contact analysis data set, a K-nearest neighbor algorithm is used to analyze the number and duration of contacts of multiple contacts, calculate the infection risk scores of the multiple contacts, match the risk levels of the multiple contacts, and generate infection risk assessment data; Based on the infection risk assessment data, combined with real-time epidemic data, infected contacts are identified, the incubation period characteristics of multiple risk contacts are analyzed and recorded, and incubation period prediction results are generated.

5. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: Based on the incubation period prediction results, combined with real-time epidemic data, including confirmed cases and recovered cases, the steps for simulating and predicting the epidemic development trend and obtaining epidemic trend assessment information are as follows: Based on the incubation period prediction results, combined with real-time epidemic data, including confirmed cases and recovered cases, analyze the time series characteristics of the epidemic data and build an epidemic data model to generate epidemic data analysis records; Based on the epidemic data analysis records, analyze and identify epidemic change indicators, including the ratio of new and recovered cases, conduct trend analysis on infection data, and generate change trend analysis data; The change trend analysis data is used to simulate and predict the development path of the epidemic. Combined with historical epidemic change data, the infection curve changes are calculated to generate epidemic trend assessment information.

6. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: Based on the epidemic trend assessment information and combined with historical material consumption data, the hospital resource demand is predicted in real time, and the material and manpower allocation is adjusted to form the resource demand calculation data. The specific steps are as follows: Based on the epidemic trend assessment information, historical epidemic material consumption is analyzed, and combined with real-time epidemic information, real-time resource demand changes are evaluated and calculated to obtain material consumption comparison results; Based on the material consumption comparison results, combined with the hospital's real-time available beds and human resources, material and manpower requirements are predicted and calculated to obtain expected demand analysis information; Based on the expected demand analysis information, the hospital's material allocation and manpower allocation are adjusted to optimize the consistency between resource allocation and actual demand, and form resource demand calculation data.

7. The smart hospital epidemic prevention and control method according to claim 1, characterized in that: Based on the resource demand calculation data, the steps for adjusting the work schedule of medical staff, optimizing human resource allocation and utilization, and generating hospital prevention and control scheduling information are as follows: Based on the resource demand calculation data, the work intensity and work schedule of multiple medical staff are evaluated in real time to obtain a work time evaluation result; Based on the work time assessment results, the medical staff's schedule is adjusted according to the type of personnel and work intensity, taking into account alternating shifts and emergency response needs, to obtain a schedule time calculation result; By utilizing the scheduling time calculation results, taking into account response speed and medical quality, human resource allocation is adjusted in real time, human resource allocation and utilization are optimized, and hospital prevention and control scheduling information is generated.

8. A smart hospital epidemic prevention and control system, characterized by: The smart hospital epidemic prevention and control method according to any one of claims 1 to 7, wherein the system comprises: The patient information assessment module analyzes the patient's symptom examination information, medical records, medical test results, and interpersonal contact history information based on the patient's symptom examination information, assesses the health status and infection risk, calculates the health and infection index, and generates risk patient file information; The impact path analysis module analyzes the patient's pathological characteristics and interpersonal contact information based on the risk patient's archival information, evaluates the patient's impact on the infection risk, identifies key transmission nodes, and generates node impact assessment information; The infection probability calculation module analyzes the patient's contact history and movement trajectory based on the node impact assessment information, evaluates the infection probability of multiple contacts, and generates an infection risk analysis record; The epidemic spread assessment module simulates and analyzes the epidemic spread and development trends based on the infection risk analysis records and real-time epidemic data, and generates development trend forecast information; Based on the development trend forecast information, combined with historical material consumption data and real-time resource status, the epidemic prevention resource scheduling module adjusts the hospital's material and human resource allocation, optimizes the work schedule of medical staff, and generates hospital prevention and control scheduling information.

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