Dynamic space optimization method for peacetime and urgency conversion of medical building

Abstract

The invention discloses a dynamic space optimization method for peacetime and urgency conversion of a medical building, and belongs to the field of medical building design, and the method specifically comprises the steps: constructing a medical building information database, and collecting historical data; functional areas are divided according to use requirements, and a dynamic requirement prediction model is constructed in combination with historical and real-time monitoring data; utilizing the dynamic demand prediction model to predict space demands under different emergency levels, and generating an optimal space conversion scheme; after the space conversion scheme is implemented, real-time tracking and color marking are carried out on the use condition of the medical building and patient circulation; according to tracking and marking results, continuously improving and optimizing a space conversion scheme; the optimization of the medical building layout and the use process is realized, and the medical efficiency is improved.

Description

Technical Field

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

[0002] Medical buildings mainly undertake routine medical service functions, and their spatial layout is relatively fixed. However, when faced with emergencies such as public health emergencies, the demand for medical resources, especially space, changes dramatically. 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 failure to meet emergency response requirements. Although there are some temporary renovation measures at present, there is a lack of systematic and efficient dynamic space optimization methods, which urgently need to be improved.

[0003] For example, a Chinese patent application with publication number CN111967090A discloses a method for dynamically improving the optimization design space, including: using variance analysis combined with Pareto frontier to achieve dynamic expansion of the design space, dynamically adjusting the design space range according to the spatial distribution of population individuals in the Pareto frontier after optimizing a certain number of steps, and using the results of variance analysis to guide the expansion of the design space for design variables at the boundary of the design space, which to a certain extent makes up for the problem that the initial design space is not complete. The method for dynamically improving the optimization design space disclosed in this technical solution can reduce the range of the initial design space, thereby improving the optimization efficiency and accuracy under the same population size and reducing the calculation cost; at the same time, the population of the Pareto frontier in this technical solution provides the position information of the design space adjustment, and the results of variance analysis can give guiding suggestions on whether the design space adjustment is to expand or reduce, thereby improving the pertinence of the design space adjustment.

[0004] The above existing technologies have the following problems: they are not customized for specific fields, which limits their application areas; they lack real-time and dynamic response capabilities; and they lack specific implementation plans. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a dynamic space optimization method for the normal-emergency conversion of medical buildings, 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 space requirements under different emergency levels and generate an optimal space conversion plan; after implementing the space conversion plan, the use of the medical building and the flow of patients are tracked and color-marked in real time; based on the tracking and marking results, the space conversion plan is continuously improved and optimized, thereby optimizing the layout and use process of the medical building and improving medical efficiency.

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

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

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

[0009] 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;

[0010] 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;

[0011] 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;

[0012] S5: Improve and optimize the optimal space conversion plan based on real-time tracking and color marking, and optimize the layout and use process of medical buildings based on 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 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.

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

[0015] 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;

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

[0017] 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.

[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 pre-processed historical medical building space usage data The covariance matrix of , and the covariance matrix Perform eigenvalue decomposition and obtain the eigenvalue λ = [ λ 1 , … , λ m ] and the corresponding eigenvectors, where represents the nth preprocessed historical medical building space usage data, represents the mth eigenvalue;

[0021] 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 level, number of patients and medical equipment demand;

[0022] 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.

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

[0024] 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;

[0025] 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;

[0026] 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;

[0027] S3.8: Based on the evaluation results, select the optimal space conversion scheme, and verify and test the selected optimal space conversion scheme.

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

[0029] 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;

[0030] 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;

[0031] S4.3: Develop color coding rules based on the use of medical buildings and patient flow, and assign different colors to different functional areas;

[0032] 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;

[0033] 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;

[0034] 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.

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

[0036] 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;

[0037] 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;

[0038] S5.3: Redesign the patient flow and use process based on the optimized medical building layout, and simulate and test the new use process;

[0039] 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;

[0040] S5.5: Based on the monitoring results, evaluate and provide feedback on the implementation effects and adjust the space conversion plan.

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

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

[0043] Compared with the prior art, the present invention has the following beneficial effects:

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

[0045] 2. The present invention proposes a dynamic space optimization method for the normal and emergency conversion of medical buildings, and also realizes real-time tracking and color marking of the use of medical buildings and patient flow, which is convenient for timely discovery and improvement of deficiencies in space conversion plans. By continuously optimizing the layout and use 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 A schematic diagram of a dynamic space optimization method for flat and emergency conversion of medical buildings according to the present invention;

[0047] Figure 2 This is a principle flow chart of a dynamic space optimization method for flat and emergency conversion of medical buildings according to the present invention;

[0048] Figure 3 The present invention is a flow chart of a space conversion scheme implementation of a dynamic space optimization method for flat and emergency conversion of medical buildings. DETAILED DESCRIPTION

[0049] Example 1

[0050] See also Figure 1 and Figure 2 , an embodiment provided by the present invention: a dynamic space optimization method for flat and emergency conversion of medical buildings, comprising the following steps:

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

[0052] 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.

[0053] 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;

[0054] S3: predicting the medical building space demand under different emergency levels according to the analysis results of the dynamic demand prediction model, and generating an optimal space conversion plan in combination with the medical building information database;

[0055] 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;

[0056] S5: Improve and optimize the optimal space conversion plan based on real-time tracking and color marking, and optimize the layout and use process of medical buildings based on the optimized space conversion plan.

[0057] For example, during the planning and design or daily operation and maintenance of medical buildings, the hardware installation and software debugging of the data acquisition module are completed to ensure that the sensors collect data stably, enter complete basic building information into the database, and train professionals to operate the demand analysis model; when an early warning signal of a public health emergency is received, the demand analysis model is started, and emergency needs are predicted in combination with real-time data. The model outputs a preliminary space conversion plan, and the construction team is organized to prepare movable facilities and spare medical equipment, waiting for further instructions; according to the increase in confirmed cases, a formal conversion order is issued, and the construction personnel follow the space conversion strategy to quickly install movable partitions and deploy temporary ventilation systems. Medical staff simultaneously adjust the use process and activate temporary diagnosis and treatment points. The monitoring platform records the conversion process and initial operating status in real time; during the duration of the emergency, the monitoring platform analyzes the efficiency of space utilization at regular intervals, such as 1 hour. Once a problem is found, the on-site management personnel are immediately notified and the layout is re-arranged according to the adjustment mechanism to ensure smooth medical treatment until the emergency is lifted, and the reverse operation is performed to restore the original state of the building or reserve experience data for the next emergency.

[0058] The specific steps of S2 include:

[0059] 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;

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

[0061] (1) Demand analysis and positioning:

[0062] Understand the use requirements of medical buildings, including hospital types, such as general hospitals and specialized hospitals, scale, such as the number of beds, department settings, and service scope;

[0063] Determine the spatial layout principles of medical buildings, such as humanized design, streamline optimization, and clear functional zoning;

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

[0065] (3) Use space syntax algorithm to carry out preliminary planning of functional areas. The space syntax algorithm will consider multiple factors, such as the functional requirements of the room, flow efficiency, space utilization, etc., to generate a reasonable functional area layout plan;

[0066] (4) Use AutoCAD to conduct simulation analysis to evaluate the effects of different layout schemes, including personnel flow simulation and medical equipment configuration simulation, and optimize and adjust the layout scheme based on the simulation results to improve the efficiency and comfort of functional areas;

[0067] (5) Before finalizing the layout plan, conduct on-site verification and evaluation to ensure that the plan meets actual needs and is feasible;

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

[0069] The specific steps of using the space syntax algorithm to conduct preliminary planning of functional areas include:

[0070] (1) Obtain basic information about medical buildings and organize the basic information 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) According to the spatial layout of the medical building, select the axis model and set the model parameters, such as the topological radius, which will affect the results of the space syntax analysis;

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

[0074] (5) Based on the results of space syntax analysis, key spatial nodes and flow lines in medical buildings are identified. Combined with the use requirements of medical buildings, such as the functional requirements of different departments, patient flow lines, medical staff flow lines, etc., preliminary functional areas are planned to ensure that the layout of functional areas is reasonable and convenient for patients to receive medical treatment and medical staff to work.

[0075] S2.2: According to the functional area division results, collect historical data in each functional area and pre-process the collected historical data;

[0076] 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, wherein the time series analysis model is the prior art content in this field and is not the creative solution of this application, and will not be elaborated here.

[0077] Example 2

[0078] See also Figure 3 In this embodiment, the specific steps of S3 include:

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

[0080] S3.2: Calculate pre-processed historical medical building space usage data The covariance matrix of M cor = [cov( x l , x j )] l , j = 1 n , and the covariance matrix Perform eigenvalue decomposition and obtain the eigenvalue λ = [ λ 1 , … , λ m ] and the corresponding eigenvector B = [ b 1 , … , b m ] ,in, represents the lth preprocessed historical medical building space usage data, express and The covariance of represents the mth eigenvalue, represents the mth eigenvector;

[0081] S3.3: Based on α k = λ k ∑ i = 1 m λ i , k ∈ [ 1 , m ] The size of , select the first M eigenvalues ​​as the principal components, and , and cluster analysis was performed on the selected principal components to identify the core factors affecting the space demand of medical buildings, among which, 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 existing technology content in this field and is not an inventive solution of this application, so it will not be elaborated here.

[0083] It is important to understand that after clustering the selected principal components, the characteristics and meanings of the principal components in each cluster are analyzed, and the core factors that affect the demand for medical building space are identified based on 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: 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;

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

[0086] (1) Determine the medical building space demand as the dependent variable Y and the identified core factors as the independent variables ,in, represents the rth independent variable;

[0087] (2) Collect data sets containing dependent and independent variables, ensure the completeness and accuracy of the data sets, and preprocess the data, including cleaning the data and handling missing values ​​and outliers;

[0088] (3) Preliminarily observe the relationship between the independent variable and the dependent variable by drawing a scatter plot;

[0089] (4) Load the pre-built multiple linear regression model according to the characteristics of the research question , and use the Python programming language to input data into the loaded pre-trained multiple linear regression model, train the pre-built multiple linear regression model, and generate a trained association model, where: represents the intercept term, represents the regression coefficient, represents the error term.

[0090] 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;

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

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

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

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

[0095] (4) Obtaining the output results of the dynamic demand forecasting model, including the predicted demand and related forecasting indicators, such as the forecasting error and confidence interval. The calculation formulas for the forecasting error and confidence interval of the forecasting model are the prior art content in this field and are not the inventive solution of the present application, and are not described in detail here.

[0096] (5) Analyze and interpret the forecast results, understand their meaning and impact, and make corresponding decisions and action plans based on the forecast results to cope with changes in demand.

[0097] 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;

[0098] It is important to understand that when calculating medical building space requirements, you need to go through:

[0099] (1) Obtain the forecast value of medical service demand in the future from the established dynamic demand forecasting model, and determine the medical building space demand standard corresponding to each unit of medical service volume based on the type of medical services, demand for medical equipment, workspace requirements of medical staff, etc.;

[0100] (2) Multiply the output of the dynamic demand forecasting model by the medical building space demand standard to obtain the total amount of medical building space demand in the future;

[0101] (3) Based on the actual situation of the medical institution, such as the existing building space, expansion or renovation plans, and financial budget, a preliminary assessment should be conducted on the calculated medical building space demand to determine its feasibility and rationality.

[0102] 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;

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

[0104] (1) Determine the goal of space conversion based on the total space demand of medical buildings and the specific needs of various types of spaces;

[0105] (2) Using the simulated annealing algorithm, combined with the medical building information database, N space conversion schemes are generated. Each space conversion scheme should include detailed information such as space layout, area allocation, and functional configuration. The simulated annealing algorithm is a prior art in this field and is not an inventive solution of the present application, so it will not be described in detail here.

[0106] (3) According to the preset evaluation criteria, such as space utilization efficiency, functional satisfaction, and cost-effectiveness, the N generated space conversion schemes are evaluated. The evaluation results should be able to reflect the advantages and disadvantages of each space conversion scheme to facilitate subsequent selection and decision-making.

[0107] S3.8: Based on the evaluation results, select the optimal space conversion scheme, and verify and test the selected optimal space conversion scheme.

[0108] The specific steps of S4 include:

[0109] 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;

[0110] 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;

[0111] S4.3: Develop color coding rules based on the use of medical buildings, patient flow, etc., and assign different colors to different functional areas;

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

[0113] (1) Collect usage data of various functional areas in medical buildings, including patient flow, medical staff activities, and medical equipment usage, and analyze these data to identify high-flow areas, low-flow areas, and key process nodes;

[0114] (2) Analyze the patient's medical process, including the entire process from pre-examination and triage, waiting, consultation, examination, treatment to discharge or referral;

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

[0116] (4) Develop a set of color coding rules based on the use of medical buildings and patient flow. The color coding rules should be able to clearly distinguish different functional areas and reflect factors such as the importance of the area, the size of the flow, and potential risks;

[0117] (5) Assign colors to different functional areas in medical buildings according to the established color marking rules, ensuring that the color assignment complies with the rules and is easy to identify and remember;

[0118] (6) Implement color coding rules in medical buildings, including making signage and updating wayfinding systems;

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

[0120] 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;

[0121] 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 in a timely manner based on the evaluation results;

[0122] Among them, the adaptability evaluation of the optimal space conversion plan is mainly to check whether the space conversion plan meets the needs of medical processes, whether it improves space utilization, whether it reduces operating costs, etc. If it is found that the space conversion plan has deficiencies, optimization adjustments will be made, such as adjusting the space layout and adding functional areas.

[0123] 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.

[0124] The specific steps of S5 include:

[0125] S5.1: Based on the data analysis and color-coded assessment results, identify the parts of the space conversion plan that need to be improved and optimized;

[0126] The specific process of identifying the space conversion scheme includes:

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

[0128] (2) Apply data analysis tools and methods to analyze the collected data and identify trends, patterns, and outliers in the data to determine which parts of the spatial transformation 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 methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in the field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, which are all 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 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 the flat and emergency conversion of medical buildings 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