A dynamic space optimization method for the conversion between peacetime and emergency use in medical buildings

Abstract

The present invention discloses a dynamic space optimization method for the flat-to-emergency conversion of medical buildings, belonging to the field of medical building design. Specifically, it includes: constructing a medical building information database and collecting historical data; dividing functional areas according to usage requirements, and combining historical and real-time monitoring data to construct a dynamic demand prediction model; using the dynamic demand prediction model to predict the space requirements under different emergency event levels and generate an optimal space conversion plan; after implementing the space conversion plan, conducting real-time tracking and color marking of the usage of medical buildings and the flow of patients; continuously improving and optimizing the space conversion plan according to the tracking and marking results; realizing the optimization of the layout and usage process of medical buildings and improving medical efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of medical building design, and specifically relates to a dynamic space optimization method for the flat-to-emergency conversion of medical buildings. Background Art

[0002] Medical buildings mainly undertake the function of providing routine medical services, and their space layout is relatively fixed. However, when facing emergencies such as sudden public health events, the demand for medical resources, especially space, changes rapidly. For example, it is necessary to quickly add isolation wards, expand intensive care units, set up temporary testing points, etc. The static space of traditional medical buildings is difficult to flexibly adapt to these emergency needs, resulting in limited medical treatment efficiency and even being unable to meet the requirements of emergency response. At present, although there are some temporary renovation measures, there is a lack of systematic and efficient dynamic space optimization methods, which urgently need to be improved.

[0003] For example, the Chinese patent application with the publication number CN111967090A discloses a dynamic improvement method for optimizing the design space, including: combining variance analysis with the Pareto front to achieve the dynamic expansion of the design space. After optimizing for a certain number of steps, the range of the design space is dynamically adjusted according to the spatial distribution of the population individuals in the Pareto front, and the results of variance analysis are used to guide the expansion of the design space for the design variables at the boundary of the design space, which makes up for the problem of the initial design space being incomplete to a certain extent. The disclosed dynamic improvement method for optimizing the design space can reduce the range of the initial design space, thereby improving the optimization efficiency and accuracy and reducing the calculation cost under the same population size; at the same time, the population of the Pareto front in this technical solution provides the position information for adjusting the design space, and the results of variance analysis can give guiding suggestions on whether to expand or reduce the design space adjustment, thus improving the pertinence of the design space adjustment.

[0004] The above existing technologies have the following problems: They are not customized for specific fields, resulting in limitations in the application fields; they lack real-time and dynamic response capabilities; and they lack specific implementation plans. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a dynamic space optimization method for the flat-to-emergency conversion of medical buildings, which constructs a medical building information database and collects historical data; divides functional areas according to usage requirements, and constructs a dynamic demand prediction model in combination with historical and real-time monitoring data; uses the dynamic demand prediction model to predict the space requirements under different emergency event levels, and generates an optimal space conversion plan; after implementing the space conversion plan, the usage situation of the medical building and the patient flow are tracked and color-coded in real time; according to the tracking and marking results, the space conversion plan is continuously improved and optimized, realizing the optimization of the layout and usage process of the medical building and improving the medical efficiency.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A dynamic space optimization method for the flat-to-emergency conversion of medical buildings, comprising:

[0008] S1: Construct a medical building information database and collect historical data;

[0009] S2: According to the usage requirements of the medical building, divide the functional areas. In different functional areas, based on the historical data and combined with real-time monitoring data, construct a dynamic demand prediction model;

[0010] S3: According to the output results of the dynamic demand prediction model, predict the space requirements of the medical building under different emergency event levels, and combine with the medical building information database to generate an optimal space conversion plan;

[0011] S4: Perform space conversion on the medical building according to the optimal space conversion plan. After the space conversion is completed, conduct real-time tracking and color marking on the usage situation of the converted medical building and the patient flow situation;

[0012] S5: Improve and optimize the optimal space conversion plan according to the real-time tracking and color marking situation, and optimize the layout and usage process of the medical building according to the optimized space conversion plan.

[0013] Specifically, the medical building information database includes the medical building floor plan, the area of each functional area, the passage layout, and the structural bearing capacity data; the historical data includes the personnel flow frequency in each functional area, the material transportation path and flow, and the environmental parameters; the environmental parameters include air quality and germ concentration; the optimal space conversion plan includes the conversion plan for each functional area, the layout of the converted medical building, the passage adjustment, and the material allocation.

[0014] Specifically, the specific steps of S2 include:

[0015] S2.1: According to the usage requirements of the medical building and combined with the space layout of the medical building, use the space syntax algorithm to automatically plan the functional areas of the medical building; the usage requirements of the medical building include daily medical services, emergency treatment, patient flow, and material transportation; the functional areas include the diagnosis and treatment area, the examination area, the inpatient area, and the emergency treatment area;

[0016] S2.2: According to the functional area division results, collect the historical data in each functional area and preprocess the collected historical data;

[0017] S2.3: Establish a real-time monitoring data interface. Combine the preprocessed historical data to train the pre-loaded time series analysis model, and generate a dynamic demand prediction model.

[0018] Specifically, the specific steps of S3 include:

[0019] S3.1: Extract historical medical building space usage data from the medical building information database and perform preprocessing;

[0020] S3.2: Calculate the covariance matrix of the preprocessed historical medical building space usage data , and perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and the corresponding eigenvectors, where λ = [ λ 1 , … , λ m ] represents the nth preprocessed historical medical building space usage data, and represents the mth eigenvalue; represents the mth eigenvalue;

[0021] S3.3: Select the first M eigenvalues as the principal components, and , and perform cluster analysis on the selected principal components to identify the core factors affecting the medical building space demand; the core factors include the emergency event level, the number of patients, and the medical equipment demand;

[0022] S3.4: According to the identification results, use the multiple linear regression method to establish an association model between the core factors and the medical building space demand.

[0023] Specifically, the specific steps of S3 also include:

[0024] S3.5: Load the dynamic demand prediction model, use the current emergency event level information as the input parameter, and input it into the dynamic demand prediction model in combination with the core factors and the association model;

[0025] S3.6: According to the output result of the dynamic demand prediction model, calculate the medical building space demand, and conduct a preliminary evaluation on the calculated medical building space demand; the medical building space demand includes the area and layout required for each functional area;

[0026] S3.7: According to the medical building space demand, combine the medical building information database, and use the simulated annealing algorithm to generate N space conversion plans, and evaluate the generated space conversion plans;

[0027] S3.8: According to the evaluation results, select the optimal space conversion plan, and verify and test the selected optimal space conversion plan.

[0028] Specifically, the specific steps of S4 include:

[0029] S4.1: According to the optimal space conversion plan, formulate an implementation plan, which includes the specific areas, time nodes, and required resources for the conversion;

[0030] S4.2: Use the Internet of Things method to establish a real-time tracking system for the usage of medical buildings, and monitor the usage of each functional area in real time;

[0031] S4.3: According to the usage of medical buildings and the flow of patients, formulate color marking rules, and assign different colors to different functional areas;

[0032] S4.4: Collect data through the real-time tracking system, and organize and analyze it to obtain the changing trends and patterns of the usage of medical buildings and the flow of patients;

[0033] S4.5: According to the analysis results, evaluate the effectiveness of the color marking rules and the adaptability of the optimal space conversion plan. According to the evaluation results, adjust the color marking rules or optimize the space conversion plan;

[0034] S4.6: According to the adjusted color marking rules and the data of the real-time tracking system, mark the colors of each functional area of the medical building, and update and display the color marking status of each functional area in real time.

[0035] Specifically, the specific steps of S5 include:

[0036] S5.1: According to the data analysis and color marking evaluation results, identify the parts of the space conversion plan that need improvement and optimization;

[0037] S5.2: For the identified parts of the space conversion plan that need improvement and optimization, improve the space conversion plan, and adjust the layout of the medical building according to the improved space conversion plan;

[0038] S5.3: According to the optimized layout of the medical building, redesign the patient flow and usage process, and simulate and test the new usage process;

[0039] S5.4: Implement the space conversion plan and usage process optimization according to the improvement and optimization plan, and conduct continuous monitoring during the implementation process;

[0040] S5.5: According to the monitoring results, evaluate and feedback the implementation effect, and adjust the space conversion plan.

[0041] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a dynamic space optimization method for the flat-to-emergency conversion of medical buildings.

[0042] A computer-readable storage medium stores computer instructions, which, when executed, perform the steps of a dynamic space optimization method for the flat-to-emergency conversion of medical buildings.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. The present invention proposes a dynamic space optimization method for the flat-to-emergency conversion of medical buildings. By constructing a medical building information database and a dynamic demand prediction model, it can accurately predict the space requirements under different emergency event levels, and then generate an optimal space conversion plan, improving the flexibility and efficiency of the use of medical building space and ensuring the rational allocation of medical resources.

[0045] 2. The present invention proposes a dynamic space optimization method for the flat-to-emergency conversion of medical buildings, which also realizes the real-time tracking and color marking of the usage situation of medical buildings and the flow of patients, facilitating the timely discovery and improvement of deficiencies in the space conversion plan. By continuously optimizing the layout and usage process of medical buildings, this method effectively improves the overall quality of medical services and patient satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of a dynamic space optimization method for the flat-to-emergency conversion of medical buildings according to the present invention;

[0047] Figure 2 It is a principle flowchart of a dynamic space optimization method for the flat-to-emergency conversion of medical buildings according to the present invention;

[0048] Figure 3 It is a flowchart for implementing the space conversion plan of a dynamic space optimization method for the flat-to-emergency conversion of medical buildings according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Example 1

[0050] Please refer to Figure 1 and Figure 2 For an example provided by the present invention: A dynamic space optimization method for the flat-to-emergency conversion of medical buildings includes the following steps:

[0051] S1: Construct a medical building information database and collect historical data;

[0052] The medical building information database includes the floor plan of the medical building, the area of each functional area, the passage layout, and the structural bearing capacity data; the historical data includes the personnel flow frequency in each functional area, the material transportation path and flow, and the environmental parameters; the environmental parameters include air quality and germ concentration; the optimal space conversion plan includes the conversion plan for each functional area, the layout of the medical building after conversion, the passage adjustment, and the material allocation.

[0053] S2: According to the usage requirements of the medical building, divide the functional areas. In different functional areas, based on the historical data and combined with the real-time monitoring data, construct a dynamic demand prediction model.

[0054] S3: According to the analysis results of the dynamic demand prediction model, predict the space requirements of the medical building under different emergency event levels, and combine with the medical building information database to generate an optimal space conversion plan.

[0055] S4: Carry out space conversion of the medical building according to the optimal space conversion plan. After the space conversion is completed, conduct real-time tracking and color marking on the usage situation of the converted medical building and the patient flow situation.

[0056] S5: Improve and optimize the optimal space conversion plan according to the real-time tracking and color marking situation, and optimize the layout and usage process of the medical building according to the optimized space conversion plan.

[0057] Exemplarily, during the planning and design or daily operation and maintenance of the medical building, complete the hardware installation and software debugging of the data acquisition module, ensure that the sensors stably collect data, input the complete building basic information into the database, and train professional personnel to operate the demand analysis model; when receiving the early warning signal of a public health emergency, start the demand analysis model, predict the emergency demand in combination with the real-time data, the model outputs a preliminary space conversion plan, organize the construction team to prepare movable facilities and standby medical equipment, and wait for further instructions; according to the increase in the number of confirmed cases, issue a formal conversion order, the construction personnel install movable partitions and deploy temporary ventilation systems quickly according to the space conversion strategy, the medical staff synchronously adjust the usage process, activate the temporary diagnosis and treatment points, and the monitoring platform records the conversion process and the initial operation status in real time; during the continuous period of the emergency state, the monitoring platform analyzes the space usage efficiency every certain period of time, such as 1 hour. Once a problem is found, immediately notify the on-site management personnel to rearrange according to the adjustment mechanism to ensure the smooth progress of medical treatment work until the emergency is lifted, and reverse the operation to restore the building to its original state or reserve experience data for the next emergency.

[0058] The specific steps of S2 include:

[0059] S2.1: According to the usage requirements of the medical building and in combination with the spatial layout of the medical building, automatically plan the functional areas of the medical building using the space syntax algorithm; the usage requirements of the medical building include daily medical services, emergency treatment, patient flow, and material transportation; the functional areas include the diagnosis and treatment area, the examination area, the inpatient area, and the emergency treatment area.

[0060] Furthermore, the specific steps for automatically planning the functional areas of the medical building include:

[0061] (1) Requirement analysis and positioning:

[0062] Understand the usage requirements of the medical building, including the hospital type, such as general hospital, specialized hospital, scale, such as the number of beds, department settings, and service scope.

[0063] Determine the spatial layout principles of the medical building, such as humanized design, streamlined optimization, and clear functional zoning.

[0064] (2) Import the basic information of the medical building, such as building dimensions, floor structure, and room type, into the architectural design software AutoCAD.

[0065] (3) Use the space syntax algorithm for preliminary planning of the functional areas. Among them, the space syntax algorithm will consider multiple factors, such as the functional requirements of the rooms, streamline efficiency, space utilization rate, etc., to generate a reasonable layout plan for the functional areas.

[0066] (4) Use AutoCAD for simulation analysis to evaluate the effects of different layout plans, including personnel flow simulation and medical equipment configuration simulation, and adjust and optimize the layout plan according to the simulation results to improve the efficiency and comfort of the functional areas.

[0067] (5) Before finally determining the layout plan, conduct on-site verification and evaluation to ensure that the plan meets the actual requirements and is feasible.

[0068] (6) Implement the layout plan and make necessary adjustments and improvements.

[0069] Among them, the specific steps for using the space syntax algorithm for preliminary planning of the functional areas include:

[0070] (1) Obtain the basic information of the medical building and organize the basic information of the medical building into a data format suitable for space syntax analysis, such as a DXF file.

[0071] (2) Import the prepared DXF file into the space syntax software.

[0072] (3) Select an axis model according to the spatial layout of the medical building and set the parameters of the model, such as the topological radius, etc. These parameters will affect the results of the space syntax analysis;

[0073] (4) Run the space syntax analysis software to quantitatively analyze the spatial structure of the medical building. Among them, the analysis results will provide information on indicators such as spatial accessibility, integration degree, and traversability;

[0074] (5) According to the results of the space syntax analysis, identify the key spatial nodes and flow lines in the medical building, and combine the usage requirements of the medical building, such as the functional requirements of different departments, patient flow lines, medical staff flow lines, etc., to preliminarily plan the functional areas to ensure that the layout of the functional areas is reasonable and convenient for patients to seek medical treatment and medical staff to work.

[0075] S2.2: According to the results of the functional area division, collect the historical data within each functional area and preprocess the collected historical data;

[0076] S2.3: Establish a real-time monitoring data interface, combine the preprocessed historical data, and train the pre-loaded time series analysis model to generate a dynamic demand prediction model. Among them, the time series analysis model is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0077] Embodiment 2

[0078] Please refer to Figure 3 , the specific steps of S3 in this embodiment include:

[0079] S3.1: Extract the historical medical building space usage data from the medical building information database and preprocess it;

[0080] S3.2: Calculate the covariance matrix of the preprocessed historical medical building space usage data and perform eigenvalue decomposition on the covariance matrix M cor = [cov( x l , x j )] l , j = 1 n , and obtain the eigenvalues λ = [ λ 1 , … , λ m ] and the corresponding eigenvectors B = [ b 1 , … , b m ] , where represents the l-th preprocessed historical medical building space usage data, represents and covariance, represents the m-th eigenvalue, represents the m-th eigenvector;

[0081] S3.3: According to α k = λ k ∑ i = 1 m λ i , k ∈ [ 1 , m ] Select the top M eigenvalues as the principal components according to their magnitudes, and perform cluster analysis on the selected principal components to identify the core factors affecting the space requirements of medical buildings. Among them, represents the i-th eigenvalue; the core factors include the level of emergency events, the number of patients, and the demand for medical equipment;

[0082] Among them, cluster analysis is the prior art content in the field and is not the creative solution of this application, so it will not be elaborated here.

[0083] It should be understood that after performing cluster analysis on the selected principal components, analyze the characteristics and meanings of the principal components in each cluster, and identify the core factors affecting the space requirements of medical buildings according to the clustering results. These core factors are usually the variables represented by the principal components that play a key role in the clustering.

[0084] S3.4: According to the identification results, use the multiple linear regression method to establish an association model between the core factors and the space requirements of medical buildings;

[0085] Furthermore, the specific steps of S3.4 include:

[0086] (1) Determine that the space requirement of the medical building is the dependent variable Y, and the identified core factors are the independent variables , where represents the r-th independent variable;

[0087] (2) Collect a data set containing the dependent variable and independent variables, ensure the integrity and accuracy of the data set, and preprocess the data, including cleaning the data, handling missing values and outliers;

[0088] (3) Initially observe the relationship between the independent variables and the dependent variable by plotting a scatter plot;

[0089] (4) According to the characteristics of the research problem, load the pre-constructed multiple linear regression model , and use the Python programming language to input data into the loaded pre-trained multiple linear regression model to train the pre-constructed multiple linear regression model to generate a trained association model. Among them, represents the intercept term, represents the regression coefficient, represents the error term.

[0090] S3.5: Load the dynamic demand prediction model, use the current emergency event level information as the input parameter, and input it into the dynamic demand prediction model in combination with the core factors and the association model;

[0091] Furthermore, the specific steps of S3.5 include:

[0092] (1) Load the dynamic demand prediction model, obtain the current emergency event level information from the monitoring system, and prepare the core factors and associated model data;

[0093] (2) Integrate the current emergency event level information, core factor data, and associated model data together to form a complete data set, and input this data set into the dynamic demand prediction model;

[0094] (3) Run the dynamic demand prediction model and make predictions based on the input data set;

[0095] (4) Obtain the output results of the dynamic demand prediction model, including the predicted demand quantity and related prediction indicators such as prediction error and confidence interval. Among them, the calculation formulas for the prediction error and confidence interval of the prediction model are the prior art content in this field and not the creative solution of this application, so they will not be elaborated here;

[0096] (5) Analyze and interpret the prediction results, understand the meaning and impact of the prediction results, and formulate corresponding decisions and action plans based on the prediction results to cope with demand changes.

[0097] S3.6: Calculate the medical building space demand based on the output results of the dynamic demand prediction model, and conduct a preliminary evaluation of the calculated medical building space demand; the medical building space demand includes the area and layout required for each functional area;

[0098] It should be understood that when calculating the medical building space demand, it is necessary to go through:

[0099] (1) Obtain the predicted value of the medical service demand quantity within a future period of time from the established dynamic demand prediction model, and determine the medical building space demand standard corresponding to each unit of medical service volume according to the type of medical service, the demand for medical equipment, the working space demand of medical staff, etc.;

[0100] (2) Multiply the output result of the dynamic demand prediction model by the medical building space demand standard to obtain the total amount of the next medical building space demand;

[0101] (3) Conduct a preliminary evaluation of the calculated medical building space demand according to the actual situation of the medical institution, such as the existing building space, expansion or renovation plan, and capital budget, etc., to judge its feasibility and rationality.

[0102] S3.7: Generate N space conversion plans using the simulated annealing algorithm based on the medical building space demand and in combination with the medical building information database, and evaluate the generated space conversion plans;

[0103] Further, the specific steps of S3.7 include:

[0104] (1) Determine the goal of space conversion based on the total demand for medical building space obtained from the analysis and the specific demands for various types of space.

[0105] (2) Use the simulated annealing algorithm and combine it with the medical building information database to generate N space conversion plans. Each space conversion plan should include detailed information such as space layout, area allocation, and function configuration. Among them, the simulated annealing algorithm is the existing technical content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0106] (3) Evaluate the N generated space conversion plans according to the preset evaluation criteria, such as space utilization efficiency, function satisfaction, and cost - benefit. The evaluation results should be able to reflect the advantages and disadvantages of each space conversion plan for subsequent selection and decision - making.

[0107] S3.8: According to the evaluation results, select the optimal space conversion plan and verify and test the selected optimal space conversion plan.

[0108] The specific steps of S4 include:

[0109] S4.1: According to the optimal space conversion plan, formulate an implementation plan, and the implementation plan includes the specific areas to be converted, time nodes, and required resources.

[0110] S4.2: Use the Internet of Things method to establish a real - time tracking system for the usage of medical buildings to monitor the usage of each functional area in real time.

[0111] S4.3: According to the usage of medical buildings, the flow of patients, etc., formulate color - marking rules and assign different colors to different functional areas.

[0112] Further, the specific steps of S4.3 include:

[0113] (1) Collect the usage data of each functional area in the medical building, including patient flow, activities of medical staff, and usage of medical equipment, and analyze this data to identify high - flow areas, low - flow areas, and key process nodes.

[0114] (2) Analyze the medical treatment process of patients, including the whole process from pre - examination and triage, waiting for consultation, seeing a doctor, examination, treatment to discharge or referral.

[0115] (3) Identify the bottlenecks and key paths in the patient flow, as well as potential cross - infection risk points.

[0116] (4) According to the usage of medical buildings and the flow of patients, formulate a set of color - marking rules. Among them, the color - marking rules should be able to clearly distinguish different functional areas and at the same time reflect factors such as the importance of the area, flow size, and potential risks.

[0117] (5) According to the formulated color marking rules, assign colors to different functional areas within the medical building to ensure that the color assignment not only complies with the rules but also is easy to identify and remember;

[0118] (6) Implement the color marking rules within the medical building, including making identification signs and updating the wayfinding system;

[0119] (7) Collect feedback from medical staff and patients, and adjust and optimize the color marking rules.

[0120] S4.4: Collect data through a real-time tracking system, organize and analyze it to obtain the changing trends and patterns of the usage situation of the medical building and the patient flow situation;

[0121] S4.5: According to the analysis results, evaluate the effectiveness of the color marking rules and the adaptability of the optimal space conversion plan. According to the evaluation results, adjust the color marking rules or optimize the space conversion plan in a timely manner;

[0122] Among them, the adaptability evaluation of the optimal space conversion plan mainly checks whether the space conversion plan meets the medical process requirements, whether it improves the space utilization rate, whether it reduces the operating costs, etc. If deficiencies are found in the space conversion plan, optimize and adjust it, such as adjusting the space layout and adding functional areas.

[0123] S4.6: According to the adjusted color marking rules and the data of the real-time tracking system, conduct color marking on each functional area of the medical building, and update and display the color marking situation of each functional area in real time.

[0124] The specific steps of S5 include:

[0125] S5.1: According to the data analysis and color marking evaluation results, identify the parts of the space conversion plan that need to be improved and optimized;

[0126] Among them, the specific process of identifying the space conversion plan includes:

[0127] (1) Collect data related to the space conversion plan, including color marking evaluation results, patient flow data, medical staff work efficiency data, space utilization data, etc., and organize the collected data to ensure the accuracy and integrity of the data;

[0128] (2) Apply data analysis tools and methods to analyze the collected data, identify trends, patterns, and outliers in the data to determine which parts of the space conversion plan may have problems or need improvement;

[0129] (3) According to the color marking rules, evaluate the color marking of each functional area of ​​the medical building, analyze the consistency between the color marking and actual usage, and identify areas where the color marking is inaccurate or does not meet actual needs;

[0130] (4) Combine data analysis and color-coded assessment results to identify parts of the space conversion plan that need to be improved and optimized. These parts may include unreasonable spatial layout, unclear functional area division, and poor patient flow.

[0131] S5.2: Improve the space conversion plan for the parts of the space conversion plan that need to be improved and optimized, and adjust the layout of the medical building according to the improved space conversion plan;

[0132] S5.3: Redesign the patient flow and medical staff use process based on the optimized medical building layout, and simulate and test the new use process;

[0133] S5.4: Implement space conversion plans and optimization of usage processes according to the improvement and optimization plan, and conduct continuous monitoring during the implementation process;

[0134] S5.5: Evaluate and provide feedback on the implementation effects based on the monitoring results, and adjust the space conversion plan in a timely manner.

[0135] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

[0136] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A dynamic space optimization method for the flat and emergency conversion of medical buildings, characterized in that: include: S1: Build a medical building information database and collect historical data; S2: Divide the functional areas according to the use requirements of the medical building, and build a dynamic demand prediction model in different functional areas based on the historical data combined with the real-time monitoring data; S3: predicting the medical building space demand under different emergency levels according to the output results of the dynamic demand prediction model, and generating an optimal space conversion plan in combination with the medical building information database; S4: Performing space conversion on the medical building according to the optimal space conversion plan, and after the space conversion is completed, real-time tracking and color marking of the usage of the converted medical building and the patient flow; S5: Improve and optimize the optimal space conversion plan based on real-time tracking and color marking, and optimize the medical building layout and usage process based on the optimized space conversion plan.

2. A dynamic space optimization method for the flat and emergency conversion of medical buildings as claimed in claim 1, characterized in that: The medical building information database includes the medical building floor plan, the area of ​​each functional area, the channel layout and the structural bearing capacity data; the historical data includes the personnel flow frequency of each functional area, the material transportation path and flow and environmental parameters; the environmental parameters include air quality and pathogen concentration; the optimal space conversion plan includes the conversion plan of each functional area, the layout of the converted medical building, channel adjustment and material allocation.

3. A dynamic space optimization method for the flat and emergency conversion of medical buildings as claimed in claim 2, characterized in that: The specific steps of S2 include: S2.1: Based on the use requirements of medical buildings and the spatial layout of medical buildings, the functional areas of medical buildings are automatically planned using a space syntax algorithm; the use requirements of medical buildings include daily medical services, emergency treatment, patient flow and material transportation; the functional areas include diagnosis and treatment areas, examination areas, hospitalization areas and emergency treatment areas; S2.2: According to the functional area division results, collect historical data in each functional area and pre-process the collected historical data; S2.3: Establish a real-time monitoring data interface, combine the pre-processed historical data, train the pre-loaded time series analysis model, and generate a dynamic demand forecasting model.

4. A dynamic space optimization method for the flat and emergency conversion of medical buildings as claimed in claim 3, characterized in that: The specific steps of S3 include: S3.1: Extract historical medical building space usage data from the medical building information database and perform preprocessing; S3.2: Calculate pre-processed historical medical building space usage data The covariance matrix of , and the covariance matrix Perform eigenvalue decomposition and obtain the eigenvalue and the corresponding eigenvectors, where represents the nth preprocessed historical medical building space usage data, represents the mth eigenvalue; S3.3: Select the first M eigenvalues ​​as principal components, and , and cluster analysis was performed on the selected principal components to identify the core factors that affect the space demand of medical buildings; the core factors include emergency event level, number of patients and medical equipment demand; S3.4: Based on the identification results, use the multiple linear regression method to establish the association model between the core factors and the medical building space requirements.

5. A dynamic space optimization method for medical building flat and emergency conversion as claimed in claim 4, characterized in that: The specific steps of S3 also include: S3.5: Load the dynamic demand forecasting model, use the current emergency level information as input parameters, combine the core factors and the association model, and input them into the dynamic demand forecasting model; S3.6: Calculate the medical building space demand based on the output results of the dynamic demand forecasting model, and make a preliminary assessment of the calculated medical building space demand; the medical building space demand includes the area and layout required for each functional area; S3.7: Based on the medical building space requirements and in combination with the medical building information database, use the simulated annealing algorithm to generate N space conversion schemes, and evaluate the generated space conversion schemes; S3.8: Based on the evaluation results, select the optimal space conversion scheme, and verify and test the selected optimal space conversion scheme.

6. A dynamic space optimization method for medical building flat and emergency conversion as claimed in claim 5, characterized in that: The specific steps of S4 include: S4.1: Develop an implementation plan based on the optimal spatial conversion plan, which includes the specific areas, time nodes and required resources for conversion; S4.2: Use the Internet of Things to establish a real-time tracking system for the use of medical buildings and monitor the use of each functional area in real time; S4.3: Develop color coding rules based on the use of medical buildings and patient flow, and assign different colors to different functional areas; S4.4: Collect data through the real-time tracking system, organize and analyze it to obtain the changing trends and patterns of medical building usage and patient flow; S4.5: Based on the analysis results, evaluate the effectiveness of the color marking rules and the adaptability of the optimal space conversion scheme, and adjust the color marking rules or optimize the space conversion scheme based on the evaluation results; S4.6: Color-mark each functional area of ​​the medical building based on the adjusted color marking rules and data from the real-time tracking system, and update and display the color marking status of each functional area in real time.

7. A dynamic space optimization method for medical building flat and emergency conversion as claimed in claim 6, characterized in that: The specific steps of S5 include: S5.1: Based on the data analysis and color-coded evaluation results, identify the parts of the space conversion plan that need to be improved and optimized; S5.2: Improve the space conversion plan for the improved and optimized space conversion plan parts identified, and adjust the layout of the medical building according to the improved space conversion plan; S5.3: Redesign the patient flow and use process based on the optimized medical building layout, and simulate and test the new use process; S5.4: Implement space conversion plans and optimization of usage processes according to the improvement and optimization plan, and conduct continuous monitoring during the implementation process; S5.5: Based on the monitoring results, evaluate and provide feedback on the implementation effects and adjust the space conversion plan.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a dynamic space optimization method for flat and emergency conversion of medical buildings as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the steps of a dynamic space optimization method for flat and emergency conversion of medical buildings as described in any one of claims 1 to 7 are executed.

Images