Method and application of risk assessment of cross-regional aerosol transmission and infection in medical buildings

Abstract

This application discloses a method and application for assessing the cross-regional transmission and infection risk of aerosols within medical buildings, relating to the field of public health technology. The method comprises: constructing a medical building model and a computational human model; meshing the medical building model and the computational human model to obtain a gridded medical building model and a gridded computational human model; performing a steady-state simulation based on the gridded medical building model and real-world environmental data to obtain an initial airflow field within the gridded medical building model; performing a transient simulation based on the gridded medical building model, the gridded computational human model, the initial airflow field, and a resident motion function to obtain an aerosol distribution within the gridded medical building model; and performing an infection risk assessment based on the aerosol distribution to obtain an assessment result. This application can accurately simulate the cross-regional transmission of aerosols within a medical building and further perform an infection risk assessment.

Description

Technical Field

[0001] The present application relates to the field of public health technology, and in particular to a method and application for assessing the cross-regional transmission and infection risk of aerosols in medical buildings. Background Art

[0002] Respiratory infectious diseases, due to their high contagiousness and rapid spread, have always occupied an extremely important position in the field of global public health technology. These diseases are usually transmitted through droplets, especially in closed or semi-closed environments. The virus can be suspended and spread for a long time through aerosols in the air, thereby increasing the risk of infection. As a special public service venue with important functions, medical buildings are the main places for the spread of respiratory infectious diseases. In medical buildings, aerosol transmission involves not only the direct exhaled droplets of personnel (patients), but also the secondary aerosols generated when personnel move between different areas. Therefore, understanding and simulating the aerosol diffusion and transmission mechanisms in medical buildings is crucial for formulating effective infection control measures.

[0003] At present, computational fluid dynamics (CFD), as an important numerical simulation tool for studying indoor airflow fields and aerosol distribution, can effectively simulate the airflow environment and aerosol transmission in medical buildings. The Wells-Riley model, as an important method for assessing infection risk, can also perform preliminary calculations on the probability of infection in medical buildings. Although existing research has provided a certain basis for the calculation of the spread of respiratory infectious diseases in medical buildings, there are still many problems that need to be solved: (1) Most studies focus on single enclosed areas such as operating rooms and wards, while multiple regional spaces in medical buildings (such as registration areas, waiting areas, diagnosis rooms, pharmacies, and wards) are interconnected, and the cross-regional transmission mechanism of aerosols in different spaces is still unclear. (2) Most studies often assume that people are stationary or simply walk in a straight line, which is relatively limited in simulating the movement of people and cannot represent the actual movements of people in medical buildings. Therefore, it is particularly important to develop a technology that can accurately simulate the cross-regional transmission of aerosols and infection risk assessment in medical buildings. Summary of the Invention

[0004] The purpose of this application is to provide a method and application for assessing cross-regional aerosol transmission and infection risk in medical buildings, which can accurately simulate the cross-regional aerosol transmission in medical buildings and further complete the infection risk assessment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for assessing the risk of aerosol transmission and infection across regions within a medical building. The method comprises:

[0007] Constructing a medical building model and a computational human body model; the medical building model is a model of multiple areas in a real medical building, the areas including at least two of a registration area, a waiting area, a diagnosis room, a pharmacy, and a ward;

[0008] Meshing the medical building model and the computational human body model respectively to obtain a gridded medical building model and a gridded computational human body model;

[0009] Performing a steady-state simulation based on the gridded medical building model and real environment data to obtain an initial airflow field inside the gridded medical building model; the real environment data includes wind speed and temperature; and the initial airflow field includes wind speed distribution and temperature distribution;

[0010] A transient simulation is performed based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and a personnel motion function to obtain an aerosol distribution inside the gridded medical building model; the personnel motion function is a function generated based on the walking route and movement of personnel walking from the entrance to the exit of a real medical building, and is used to simulate the actual walking process of personnel;

[0011] An infection risk assessment is performed based on the aerosol distribution to obtain an assessment result.

[0012] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for assessing cross-regional transmission and infection risks of aerosols in medical buildings.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for assessing the risk of aerosol cross-regional transmission and infection in medical buildings.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] The present application provides a method and application for assessing the cross-regional transmission and infection risk of aerosols in a medical building, which constructs a medical building model and a computational human body model. The medical building model is a model of multiple areas in a real medical building, including at least two of the registration area, waiting area, diagnosis room, pharmacy and ward; the medical building model and the computational human body model are gridded to obtain a gridded medical building model and a gridded computational human body model; a steady-state simulation is performed based on the gridded medical building model and real environmental data to obtain an initial airflow field inside the gridded medical building model; a transient simulation is performed based on the gridded medical building model, the gridded computational human body model, the initial airflow field and the personnel motion function to obtain an aerosol distribution inside the gridded medical building model, where the personnel motion function is a function generated based on the walking route and personnel movement conditions of personnel walking from the entrance to the exit of the real medical building, and is used to simulate the real personnel walking process; an infection risk assessment is performed based on the aerosol distribution to obtain an assessment result. This application establishes a multi-area medical building model in a real medical building, and then combines it with subsequent grid division, steady-state simulation and transient simulation to complete the simulation of aerosol distribution in multiple areas of the medical building, solving the problem that the cross-regional transmission mechanism of aerosols in different spaces is still unclear. In addition, by designing personnel motion functions, it can complete the simulation of actual personnel movements in the medical building, solving the relatively limited problem in simulating personnel movement, thereby accurately simulating the cross-regional transmission of aerosols in the medical building and further conducting infection risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is an application environment diagram of a method for assessing cross-regional aerosol transmission and infection risk in a medical building provided in Example 1 of the present application.

[0018] Figure 2 This is a flow chart of a method for assessing cross-regional aerosol transmission and infection risk in a medical building, as provided in Example 1 of the present application.

[0019] Figure 3 A detailed flowchart of a method for assessing cross-regional aerosol transmission and infection risk in a medical building, as provided in Example 1 of the present application.

[0020] Figure 4This is a principle block diagram of a method for assessing cross-regional aerosol transmission and infection risk in a medical building, as provided in Example 1 of the present application.

[0021] Figure 5 Schematic diagram of the medical building model and computational human body model provided in Example 1 of the present application; wherein, Figure 5 (a) is a medical building model. Figure 5 (b) in the figure is the computational human body model.

[0022] Figure 6 This is a schematic diagram of the background grid, component grid, and overlapping grid provided in Example 1 of the present application; wherein, Figure 6 (a) is the background grid. Figure 6 (b) in the figure is the component mesh. Figure 6 (c) in the figure is an overlapping grid.

[0023] Figure 7 This is a schematic diagram of the initial airflow field provided in Example 1 of the present application; wherein, Figure 7 (a) is the wind speed distribution, Figure 7 (b) in the figure is the temperature distribution.

[0024] Figure 8 Schematic diagram of aerosol distribution provided in Example 1 of the present application.

[0025] Figure 9 Schematic diagram of infection risk distribution provided in Example 1 of the present application.

[0026] Figure 10 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] Example 1

[0029] The method for assessing the risk of aerosol cross-regional transmission and infection in medical buildings provided in the embodiments of the present application can be applied to Figure 1In the application environment shown, the terminal communicates with the server via a network. The data storage system can store data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the real data to be processed (including data related to the real medical building, data related to personnel movement, and environmental data) to the server. After receiving the real data to be processed, the server constructs a medical building model and a computational human body model for the real data to be processed; meshes the medical building model and the computational human body model to obtain a gridded medical building model and a gridded computational human body model; performs a steady-state simulation based on the gridded medical building model and the real environmental data to obtain the initial airflow field within the gridded medical building model; performs a transient simulation based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and the personnel motion function to obtain the aerosol distribution within the gridded medical building model; and performs an infection risk assessment based on the aerosol distribution to obtain an assessment result. The server can feedback the obtained aerosol distribution and assessment result to the terminal.

[0030] In addition, in some embodiments, the method for assessing cross-regional aerosol transmission and infection risk in medical buildings can also be implemented independently by a server or a terminal. For example, the terminal can directly process the real data to be processed, or the server can obtain the real data to be processed from the data storage system and process the real data to be processed.

[0031] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or as a cloud server.

[0032] like Figure 2 As shown, a method for assessing the risk of aerosol transmission and infection across regions in a medical building is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 Taking the server in FIG. 1 as an example, the aerosol cross-area transmission and infection risk assessment in a medical building includes the following steps:

[0033] Step S1, constructing a medical building model and a computational human body model; the medical building model is a model of multiple areas in a real medical building, and the areas include at least two of a registration area, a waiting area, a diagnosis room, a pharmacy, and a ward.

[0034] Step S2 , meshing the medical building model and the computational human body model respectively to obtain a gridded medical building model and a gridded computational human body model.

[0035] Step S3, performing steady-state simulation based on the gridded medical building model and real environment data to obtain an initial airflow field inside the gridded medical building model; the real environment data includes wind speed and temperature; the initial airflow field includes wind speed distribution and temperature distribution.

[0036] Step S4: performing transient simulation based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and the personnel motion function to obtain the aerosol distribution inside the gridded medical building model; the personnel motion function is a function generated based on the walking route and personnel movement conditions of personnel walking from the entrance to the exit of a real medical building, and is used to simulate the real personnel walking process.

[0037] Step S5: performing infection risk assessment based on the aerosol distribution to obtain an assessment result.

[0038] By implementing the above-mentioned steps S1 to S5, this embodiment establishes a multi-region medical building model in a real medical building, and then combines it with subsequent grid division, steady-state simulation and transient simulation to complete the simulation of aerosol distribution in multiple regions of the medical building, and solves the problem that the cross-regional transmission mechanism of aerosols in different spaces is still unclear. In addition, by designing a personnel movement function, the personnel movement function is a function generated based on the walking route and personnel movement conditions of personnel walking from the entrance to the exit in the real medical building, which is used to simulate the real personnel walking process. It can complete the simulation of actual personnel movements in the medical building, solve the problem of being relatively limited in simulating personnel movement, and thus accurately simulate the cross-regional transmission of aerosols in the medical building, and further conduct infection risk assessment.

[0039] Specifically, the method for dynamic aerosol cross-regional transmission and infection risk assessment in a real medical building provided in this embodiment includes the following steps:

[0040] (1) Obtain various parameters of various areas in real medical buildings

[0041] Obtaining various parameters for each area within a real medical building includes obtaining data related to building data, material data, ventilation system design data, and personnel flow design data. Building data refers to the actual structure and dimensions of each area that constitutes a real medical building. Areas include registration areas, waiting areas, examination rooms, pharmacies, wards, and other areas. Building data includes the location and spatial dimensions of each area within the real medical building, as well as the location and dimensions of doors and windows, passageways, partitions, and equipment within each area. Passageways refer to corridors for personnel to walk through. Partitions refer to components that separate two areas, typically walls or glass. Equipment refers to tables, chairs, office equipment, medical equipment, etc. located within an area. Material data refers to the building materials used in the components within each area of a real medical building. Material data includes floor materials, ceiling materials, door and window materials, partition materials (i.e., wall materials), and equipment materials. Ventilation system design data refers to data related to ventilation systems designed to ensure air quality within real medical buildings. This data includes the location and dimensions of vents, ventilation ducts, ventilation equipment, ventilation strategies, air purification systems, and control systems. The location and dimensions of vents include the location and dimensions of air inlets and exhaust vents, while the location and dimensions of ventilation ducts reflect the duct layout. The ventilation strategies, air purification systems, and control systems provide parameters such as ventilation rate and volume, which are subsequently used to set wind speed within the gridded medical building model during steady-state simulations. Personnel flow design data is derived from the actual spatial layout and actual pedestrian paths of real medical buildings. This data includes the pedestrian routes and movement patterns of people walking from the entrance to the exit of a real medical building. Movement patterns include information such as speed and direction of people walking along the routes, including uniform linear motion, variable-speed linear motion, curved motion, and rotational motion. This data is subsequently used to generate the personnel movement function.

[0042] This embodiment further collects real environment data, which includes indoor initial temperature, wall temperature, wind speed and temperature at different heights of indoor points.

[0043] (2) Complete the construction of the medical building model and the computational human body model, corresponding to step S1

[0044] The construction of the medical building model and computational human body model was completed, including the use of ANSYS Space Claim software, a 3D direct solid modeling software, to create the medical building model and computational human body model. The medical building model was constructed based on the architectural data and ventilation system design data for various parameters of each area of the real medical building. Areas were connected by doors and windows. Cube entities were created in the pedestrian areas corresponding to pedestrian routes. This was used for subsequent local mesh refinement and volume extraction of the medical building's fluid domain to generate the medical building model. A high-fidelity human body model was created using open-source 3D modeling software. The human body model took into account the actual geometric characteristics of the human body to more accurately simulate the human body's thermal plume and microenvironment. ANSYS Space Claim software was then used to mesh and shell the human body model to obtain the computational human body model. Meshing refers to segmenting the human body model into multiple meshes, and shelling refers to creating a cube entity that completely encloses the human body model. The size of the cube entity that completely encloses the human body model is smaller than the size of the cube entity corresponding to the pedestrian area. The human body subsequently walks within the pedestrian area.

[0045] In this embodiment, the medical building model and the computational human body model are further grouped and named, and a .scdoc file is exported. Group naming refers to naming the individual components of the medical building model and the computational human body model. For example, for the medical building model, the floor, ceiling, doors and windows, partitions, equipment, air inlets, air outlets, ventilation ducts, and ventilation equipment are named; for the computational human body model, the housing, human body, and human mouth are named.

[0046] In step S1, a medical building model and a computational human body model are constructed, specifically including:

[0047] 1) Acquire architectural data and ventilation system design data of a real medical building, where the architectural data includes the position and size of each of multiple areas in the real medical building, as well as the position and size of doors and windows, passages, partitions, and equipment within each area; the ventilation system design data includes the position and size of vents, ventilation ducts, and ventilation equipment; construct a physical model of the real medical building based on the architectural data and ventilation system design data; establish a cube entity in the personnel walking area in the physical model to obtain an intermediate physical model, where the personnel walking area is an area determined based on the personnel walking route; perform volume extraction of the fluid domain on the intermediate physical model to obtain a medical building model.

[0048] 2) Obtain a human body model, perform faceting on the human body model, and establish a shell to obtain a computational human body model.

[0049] (3) Complete grid division, corresponding to step S2

[0050] The mesh division is completed, including: using Ansys FluentMeshing software to divide the meshes of the medical building model and the computational human body model to obtain a meshed medical building model and a meshed computational human body model, wherein the meshed medical building model is a background mesh, that is, the background mesh is a mesh obtained by dividing the medical building model, and the meshed computational human body model is a component mesh, that is, the component mesh is a mesh obtained by dividing the computational human body model.

[0051] When dividing the mesh, choose meshing based on the watertight geometry workflow, which includes: importing the geometric model, adding local dimensions, generating surface mesh, describing the geometric structure, updating boundaries, triggering quality improvement, adding boundary layers, generating volume mesh, and exporting .msh files.

[0052] In step S2, the medical building model and the computational human body model are meshed respectively to obtain a meshed medical building model and a meshed computational human body model, specifically comprising: using a meshing method based on a watertight workflow to mesh the medical building model and the computational human body model respectively to obtain a meshed medical building model and a meshed computational human body model.

[0053] The meshing method based on the watertight workflow includes the following steps: importing a geometric model, adding local dimensions, generating a surface mesh, describing a geometric structure, updating boundaries, triggering quality improvement, adding boundary layers, and generating a volume mesh. Importing a geometric model refers to importing a medical building model or a computational human body model; adding local dimensions refers to selecting a local area that requires special generation of a local surface mesh or a local volume mesh; generating a surface mesh refers to generating meshes for other surfaces in the medical building model or the computational human body model except for the local area; describing a geometric structure refers to setting the properties of the geometric structure. In this embodiment, the geometric structure can be selected as a geometric structure consisting of only fluid areas without gaps; updating boundaries refers to setting the boundary properties in the medical building model or the computational human body model; triggering quality improvement refers to improving the quality of the generated surface mesh; adding boundary layers refers to generating multiple boundary layers on the wall surface. In this embodiment, at least four boundary layers can be generated; generating a volume mesh refers to generating a volume mesh for other areas in the medical building model or the computational human body model except for the local area. In this embodiment, the volume mesh can be selected as poly-hexore.

[0054] For medical building models, meshes in pedestrian areas, doors, windows, and vents require local encryption. Adding local dimensions involves performing local face encryption on doors, windows, and vents, and local volume encryption on the cubic entities in the pedestrian areas. The resolution of the meshes generated by these local face and volume encryptions is smaller than the resolution of the meshes generated by generating the face and volume meshes, thus achieving local encryption. Updating boundaries involves setting the air inlets in vents as velocity inlets, the air outlets in doors, windows, and vents as pressure outlets, and all other boundaries except for the air inlets, doors, windows, and exhaust vents as walls. Boundaries refer to edges in medical building models, such as walls and equipment.

[0055] For the calculation of the human body model, updating the boundary includes: setting the human body mouth as the velocity inlet, setting the remaining boundaries of the human body except the human body mouth as the wall surface, and setting the outer shell as the inner surface. The boundary refers to the edge in the human body.

[0056] (4) Construct the initial airflow field in the medical building, corresponding to step S3

[0057] This example uses the CFD software Ansys Fluent to simulate the airflow field, selects an appropriate turbulence model and wall function, sets the parameters of each vent, and completes the establishment of the initial airflow field in the medical building. The construction of the initial airflow field in the medical building includes: importing the background grid, selecting the model, setting the material, setting the unit area conditions, setting the boundary conditions, setting the solution method, initializing, and running the calculation.

[0058] Model selection includes selecting the turbulence model (used to analyze and calculate the heat transfer process), the energy equation (used to simulate the thermal convection of heat conduction), and the component transport model (used to simulate the transport behavior of components in the fluid). The turbulence model selects the Realizable k-ε model, the energy equation is selected to be open, and the component transport model selects the component transport model.

[0059] Material settings include settings for solid materials, fluid materials, and mixed materials, specifically adding materials and defining material physical parameters. Adding materials means selecting the building materials used for each component in the medical building model based on the material data used. Defining material physical parameters means setting the fluid domain to air properties and setting the floor, ceiling, doors and windows, partitions, equipment, vents, ventilation ducts, and ventilation equipment to solid properties according to actual conditions.

[0060] The unit area condition settings include the settings of fluid domain materials and source items.

[0061] The boundary condition settings include the settings of wall material and temperature, outlet momentum, inlet wind speed and temperature.

[0062] Select the pressure-based coupled algorithm in the solution method settings.

[0063] A hybrid initialization method was selected for initialization of the wind speed and temperature inside the gridded medical building model.

[0064] Run the calculation selecting the Steady-State Iterative algorithm.

[0065] In step S3, a steady-state simulation is performed based on the gridded medical building model and the real environment data to obtain the initial airflow field inside the gridded medical building model. Specifically, the steady-state simulation is performed based on the gridded medical building model and the real environment data as input, and the steps of importing the background grid, selecting the model, setting the material, setting the unit area condition, setting the boundary condition, setting the solution method, initializing, and running the calculation are followed to obtain the initial airflow field inside the gridded medical building model.

[0066] Importing the background grid includes: importing a gridded medical building model.

[0067] Model selection includes: selecting turbulence model, energy equation and component transport model. The turbulence model selects the Realizable k-ε model, and the component transport model selects the component transport model.

[0068] Material settings include: setting the ground, ceiling, doors and windows, partitions, equipment, vents, ventilation ducts and ventilation equipment of the gridded medical building model to solid properties, setting the fluid domain of the gridded medical building model to air properties, and the fluid domain is the space where the air is located in the gridded medical building model.

[0069] The boundary condition settings include: setting the material and temperature of the wall, setting the outlet momentum, and setting the wind speed and temperature of the inlet. The outlet is the exhaust vent in the doors, windows and vents of the gridded medical building model, and the inlet is the air inlet in the vents of the gridded medical building model. The wall is the remaining boundaries in the gridded medical building model except the outlet and inlet. The material of the wall is the real material of the wall in the real medical building. The outlet momentum is selected to suppress backflow. The wall temperature, inlet wind speed and temperature are all determined based on real environmental data. The wind speed can also be manually determined with reference to the ventilation system design data.

[0070] The solution method settings include: setting the solution method and selecting the pressure-based coupling algorithm.

[0071] Initialization includes: setting the wind speed and temperature inside the gridded medical building model, and selecting a hybrid initialization method.

[0072] Run the calculation selecting the Steady-State Iterative algorithm.

[0073] (5) Complete grid independence verification and simulation result verification

[0074] Complete grid independence verification and simulation result verification, including:

[0075] (5.1) Grid independence verification

[0076] Five sets of gridded medical building models with different resolutions (i.e., different numbers of grids) were established, and steady-state airflow fields were established for each model to construct the initial airflow field within the medical building. For each gridded medical building model, the airflow velocity (i.e., wind speed) and temperature at six points at different heights were checked through the initial airflow field. If three heights were selected, 18 airflow velocities and temperatures were obtained. The average values of the 18 airflow velocities and the average values of the 18 temperatures were calculated to obtain the average airflow velocity and average temperature values. The gridded medical building model with the largest number of grids was used as the benchmark, and the relative differences in the average airflow velocity and average temperature values of the gridded medical building models with different numbers of grids were compared with the benchmark in descending order of the number of grids. When the relative difference was less than 1%, in order to reduce processing costs and time requirements, the gridded medical building model with a smaller number of grids was selected for subsequent calculation and simulation. The relative difference calculation formula is as follows:

[0077]

[0078] In formula (1), RD is the relative difference; x1 and x2 are the average airflow velocity or average temperature corresponding to the baseline and gridded medical building models with different grid numbers, respectively. The relative difference of less than 1% is considered to be met only when the relative difference of the average airflow velocity and the average temperature is less than 1%.

[0079] (5.2) Verification of simulation results

[0080] Use sensors to measure the airflow velocity and temperature at six points at different heights as experimental data. Compare the simulation results with the experimental data to verify the simulation results and ensure accuracy. If the verification fails, adjust the temperature and wind speed inputs in the steady-state simulation.

[0081] (6) Constructing an aerosol particle simulation model

[0082] Constructing an aerosol particle simulation model involves simulating the motion of aerosols using the Euler-Lagrangian method, taking into account additional forces such as particle evaporation, thermophoretic force, and Saffman lift. During simulation, the discrete phase model (DPM) is used to track the trajectories of exhaled aerosols. In the discrete phase model settings, the stochastic tracking model is selected to account for the effects of turbulence on particle trajectories, thereby determining the distribution of exhaled aerosols in various areas within the medical building.

[0083] The Euler-Lagrangian method describes the continuous phase using the Euler method and tracks the discrete phase using the Lagrangian method. The Lagrangian method obtains the particle motion trajectory by solving the particle force equation. By tracking the trajectories of a large number of particles, the diffusion and distribution of particles can be accurately predicted. The Euler method focuses on the overall flow of particles in the air to study their operating laws and treats particles as continuous phase fluids.

[0084] The discrete phase model is a type of Euler-Lagrange model based on the Euler-Lagrange method. The discrete phase model requires that the particle phase volume is small (volume fraction <10%) and is evenly distributed in the continuous phase. During the calculation process, the continuous phase is calculated to obtain information such as the velocity, pressure, and turbulent kinetic energy of the entire flow field. Considering the force conditions and turbulent diffusion phenomena of the particles in the continuous phase, the trajectory of a single particle is integrated to obtain the trajectory of the particle.

[0085] In this embodiment, the discrete phase model enables the interaction between the discrete phase and the continuous phase, particle processing is unsteady particle tracking, and particles are ejected at the particle time step. The particle time step size is synchronized with the person's breathing rate and can be set to 2s. Thermophoretic force, Saffman lift force, and pressure gradient force are enabled. The injection source type is surface injection, and the surface injection is at the person's mouth. The specific discrete phase properties are the same as those of aerosols.

[0086] (7) Complete aerosol distribution simulation, corresponding to step S4

[0087] In this example, the CFD software Ansys Fluent is used to simulate the aerosol distribution.

[0088] Based on the background grid and component grid, the Overset module in Ansys Fluent is used to connect the two grids, that is, to connect the background grid and the component grid together, and set the movement of the component grid by compiling a user-defined function (i.e., the personnel movement function). Overlapping grid is a type of dynamic grid technology. By establishing the background grid and the component grid, the movement of the component grid within the background grid can be completed. Overlapping grid is composed of the overlapping background grid and the component grid. Each grid area overlaps in space, but there is no connectivity relationship. They exist independently of each other. The pre-processor must complete operations such as digging holes and matching interpolation points to establish a connectivity relationship. The pre-processor processes the overlapping grid into hole units, discrete calculation units, and interpolation units, each of which is solved on the background grid and the component grid. The interpolation units constitute the internal boundary conditions and are used to transmit data. Ultimately, the flow field information within the entire calculation domain is obtained, thus completing the dynamic grid setting.

[0089] User-defined functions are used to realize the linear variable speed motion and rotation of the component grid. Through the personnel flow line design data, the function of the personnel walking route across areas in the medical building is determined. The regional motion function corresponding to the component grid is imported to simulate the cross-area walking of personnel in the medical building.

[0090] Based on the steady-state simulation results, a discrete phase model and overlapping grids are used to perform transient simulation to obtain the aerosol distribution.

[0091] In step S4, a transient simulation is performed based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and the personnel motion function to obtain the aerosol distribution inside the gridded medical building model. Specifically, the transient simulation is performed according to the steps of additional component gridding, model selection, material setting, boundary condition setting, and running calculations, using the gridded computational human body model and the personnel motion function as inputs to the gridded medical building model with the initial airflow field, to obtain the aerosol distribution inside the gridded medical building model.

[0092] Among them, the additional component grid includes: using the gridded medical building model as the background grid, using the gridded computational human body model as the component grid, using overlapping grids to connect the background grid and the component grid, and importing the personnel motion function. The personnel motion function is used to drive the component grid to move in the background grid during the transient simulation process.

[0093] Model selection includes: selecting a discrete phase model. The settings of the discrete phase model include: turning on the interaction between the discrete phase and the continuous phase, selecting unsteady particle tracking for particle processing, selecting to inject particles at the particle time step, selecting the particle time step size to be synchronized with the breathing frequency of the person, turning on thermophoresis, Saffman lift, and pressure gradient force, selecting surface injection as the injection source type, and the surface injection is the human mouth. The discrete phase properties are selected to be the same as those of the aerosol.

[0094] Material settings include: setting the human body of the meshed calculation human body model to solid properties.

[0095] The boundary condition settings include: setting the material and temperature of the wall, setting the wind speed and temperature of the inlet, and setting the outer shell of the gridded calculation human body model as the overlapping boundary, the inlet as the human mouth of the gridded calculation human body model, and the wall as the remaining boundaries of the human body of the gridded calculation human body model except the human mouth.

[0096] Run the calculation selecting the Steady-State Iterative algorithm.

[0097] (8) Use the improved Wells-Riley model to assess infection risk, corresponding to step S5

[0098] When assessing infection risk, the traditional Wells-Riley model assumes that air in a space is fully mixed and that aerosol concentration does not change over time. However, in reality, aerosols are often non-uniformly distributed. Therefore, this embodiment further designs an improved Wells-Riley model and uses it to assess infection risk. This includes dividing the gridded medical building model into multiple calculation areas, counting the number of aerosol particles in the breathing zone of each calculation area. The breathing zone is a spherical space centered on each person's nose with a radius of r = 0.2m, and then using the improved Wells-Riley model to quantitatively assess the infection risk of each calculation area. By assessing the infection risk of each calculation area through the improved Wells-Riley model, targeted suggestions and opinions are provided for improving hospital ventilation strategies, improving functional zoning layouts, and optimizing medical personnel flow lines.

[0099] The improved Wells-Riley model is as follows:

[0100]

[0101] In formula (2), P i is the infection probability of area i in the gridded medical building model; k is the infection factor; D i is the inhaled dose of personnel in calculation area i, which also refers to the inhaled dose of personnel in the breathing zone of calculation area i; Min is the filtration efficiency, which is determined by the filtration efficiency of the mask worn by the personnel. If no mask is worn, the filtration efficiency is 0. If an N95 mask is worn, the filtration efficiency is 95%.

[0102] D i =AC i t (3)

[0103] In formula (3), A is the activity level of personnel in calculation area i (L / s); C i is the virus concentration in calculation area i, which also refers to the virus concentration in the breathing zone of calculation area i; t is the stay time of the personnel in calculation area i.

[0104]

[0105] In formula (4), Vp i The total volume of aerosol particles in the breathing zone of calculation area i is calculated. The breathing zone is a spherical space area with the nose of the person as the center and the set radius as the radius in the calculation area i; V i is the volume of calculated area i; L is the known viral load.

[0106]

[0107] In formula (5), n is the total number of aerosol particles in the breathing zone of the calculation area i, which is determined according to the aerosol distribution; m ij is the mass of the jth aerosol particle in the breathing zone of region i, which is determined according to the aerosol distribution; ρ is the density of the aerosol particles.

[0108] In step S5, infection risk assessment is performed based on the aerosol distribution to obtain an assessment result, specifically including: taking the aerosol distribution as input, using the improved Wells-Riley model to perform infection risk assessment to obtain an assessment result.

[0109] This embodiment provides a method for assessing cross-area aerosol transmission and infection risk within medical buildings based on computational fluid dynamics and an improved Wells-Riley model. This method uses numerical simulation to study the cross-area walking process of personnel with respiratory infectious diseases within a medical building and the corresponding aerosol transmission characteristics. The simulation results are used to assess infection risk, thereby guiding the design and management of medical environments. Compared with existing technologies, this embodiment has the following advantages: by establishing a physical model of a real hospital scene and combining the paths of personnel walking across areas, the dynamic process of exhaled aerosols during walking is simulated using Ansys Fluent. The model can be validated and improved based on environmental data from real-world scenarios. The walking process uses a method combining overlapping grids and user-defined functions. Overlapping grids not only simplify the meshing of complex geometries but also facilitate mesh generation for relatively moving parts. Combined with the improved Wells-Riley model, the infection risk of each computational area is assessed, which can improve hospital ventilation strategies, enhance functional zoning layouts, and optimize patient flow.

[0110] like Figure 3 and Figure 4 As shown, this embodiment describes the method proposed in this embodiment in detail with reference to a fever clinic of a hospital in Wuhan. The method includes the following steps:

[0111] Step 101: Obtain various parameters of various areas in a real medical building, including building data, material data, ventilation system design data, and personnel flow design data.

[0112] Step 102: Use Ansys Space Claim to complete the construction and volume extraction of the medical building model and computational human body model.

[0113] Step 103: Based on the medical building model and computational human body model, use Ansys FluentMeshing to perform meshing and dynamic mesh settings.

[0114] Step 104: Based on the grid, use Ansys Fluent to complete the steady-state airflow field simulation and obtain the initial airflow field in the medical building.

[0115] Step 105: Complete grid independence verification and simulation result verification.

[0116] Step 106: Based on the initial airflow field, a discrete phase model is used to complete a transient simulation of aerosol exhaled by a walking person.

[0117] Step 107: Based on the results of the aerosol transient simulation and the improved Wells-Riley model, the infection risk of each area in the medical building is calculated.

[0118] Step 101 specifically includes:

[0119] Combined with design drawings, on-site measurements and air conditioning supply and return duct layout drawings, the building data, material data, ventilation system design data and personnel flow design data are determined, including space dimensions, door and window dimensions, equipment dimensions, materials used in various parts of the building, and the location, size and model of air inlets and exhaust vents.

[0120] Step 102 specifically includes:

[0121] Based on the various parameters of various areas in the real medical building, the modeling software Ansys Space Claim is used to complete the construction of the medical building model, extract the fluid domain and name the group, and create the entity of the personnel walking area to create a finer mesh in the personnel walking area. Use the open source fine human body model on the Internet and import it into the modeling software Ansys Space Claim to complete the faceting and shell addition. The shell size must match the cube entity size of the personnel walking area and name the group. Figure 5 shown.

[0122] Step 103 specifically includes:

[0123] Use Ansys FluentMeshing for meshing: select meshing based on the watertight workflow, and import the .scdoc files of the medical building model and the computational human body model respectively; add local dimensions: select surface encryption, check the groups corresponding to doors and windows, the group corresponding to vents, and the group corresponding to the human mouth, select volume encryption, and check the entities in the area where people walk; generate surface meshes; describe the geometry as a geometry consisting only of fluid areas without gaps; update boundaries: set the air inlet and the human mouth as velocity inlets, the return air vents and doors and windows as pressure outlets, and the remaining boundaries as walls; trigger quality improvement; add boundary layers: generate at least 4 boundary layers on the wall; generate volume meshes, and select generate poly-hexore meshes.

[0124] The poly-hexcore volume mesh generation method described above enables hexahedral meshes and polyhedral meshes to be connected at common nodes without requiring any additional manual mesh settings. This method can increase the number of hexahedrons in the mesh, thereby improving solution efficiency and accuracy.

[0125] Export two .msh files. The two sets of meshes are background mesh (based on the mesh of medical building model) and component mesh (based on the mesh of computational human body model). Figure 6 As shown in the figure, the background mesh and component mesh are connected using the Overlap Mesh module in Ansys Fluent. The walking route and speed of the moving person are converted into functions. The movement is defined using the DEFINE_ZONE_MOTION macro in a user-defined function to complete the walking setting.

[0126] Step 104 specifically includes:

[0127] Use Ansys Fluent to simulate the airflow field: import the .msh file of the gridded medical building model, then attach the .msh file of the gridded human body model, turn on gravity and set the gravity acceleration, turn on the energy equation, select the Realizable k-ε model as the turbulence model, turn on the component transport model, complete the material setting, unit area condition setting, boundary condition setting and solution method setting, initialize and run the calculation, and establish the steady-state initial airflow field as shown below: Figure 7 shown.

[0128] Material settings include solid, fluid, and mixed materials. Materials for interior and exterior walls, ceilings, floors, tables, chairs, and other equipment are set based on the actual conditions of the medical building. Cell area condition settings include fluid domain materials and source terms. The pressure-based coupled solution algorithm is selected for the solution method.

[0129] Step 105 specifically includes:

[0130] The local and global grid sizes were modified, and five gridded medical building models with different resolutions were established. Steady-state airflow fields were established for each of them. The airflow velocity and temperature at six points at different heights were checked, and the relative differences in airflow velocity and temperature under different grid numbers were compared. When the relative difference was less than 1%, the gridded medical building model with a smaller number of grids was selected for subsequent calculation and simulation in order to reduce processing costs and time requirements.

[0131] At the same time, sensors are used to measure the airflow velocity and temperature at 6 points at different heights as experimental data, and the simulation results are compared with the experimental data to ensure accuracy. Figure 7shown.

[0132] Step 106 specifically includes:

[0133] After the steady-state airflow field simulation with the above-mentioned appropriate number of grids is completed, the simulation type is switched to transient, the discrete phase model is turned on, the interaction between the discrete phase and the continuous phase is turned on, the particle processing is unsteady particle tracking, and particles are injected at the particle time step. The particle time step size is synchronized with the person's breathing frequency and can be set to 2s. Thermophoretic force, Saffman lift force, and pressure gradient force are turned on. The injection source type is surface injection, and the surface injection is the person's mouth. The specific discrete phase physical properties are the same as those of the aerosol.

[0134] Import the personnel motion function defined by DEFINE_ZONE_MOTION macro into the regional motion function to realize the movement of personnel, and then complete the transient simulation of aerosol exhalation and propagation. The aerosol distribution is as follows: Figure 8 shown.

[0135] Step 107 specifically includes:

[0136] The infection risk of each calculation area is assessed using the improved Wells-Riley model. The gridded medical building model is first divided into multiple calculation areas. The number of aerosol particles in the breathing zone of each calculation area is counted. Then, the improved Wells-Riley model is used to quantitatively assess the infection risk of each calculation area.

[0137] Use programming to quickly process aerosol particle data tables: After the transient simulation is completed, you can export information tables of all aerosol particles. For the required information, select the x-coordinate, y-coordinate, z-coordinate, survival time, particle diameter, particle mass, particle density, and the number of aerosols corresponding to each particle package to export the Excel table; use programming to quickly process the number and total volume of aerosols in each calculation area, and then calculate the infection risk of each calculation area. The infection risk distribution is as follows: Figure 9 shown.

[0138] This embodiment aims to simulate the spread of aerosol particles exhaled by walking personnel with respiratory infectious diseases based on the airflow environment within a real medical building. Based on the distribution of virus-carrying aerosol particles, an improved Wells-Riley model is used to calculate the infection risk of each calculation area within the medical building. Based on the infection risk of each calculation area within the medical building, the hospital ventilation strategy is improved, the functional zoning layout is improved, and the flow of medical personnel is optimized. This dynamic simulation and risk assessment of exhaled virus-containing aerosols while personnel are walking in the medical building are achieved, which is of great significance for improving hospital ventilation strategies, improving functional zoning layout, and optimizing medical personnel flow.

[0139] Compared with the existing method, the main advantages of this embodiment are:

[0140] (1) The pathogen transmission mechanism in medical building spaces is clarified, taking into account personnel behavior;

[0141] (2) Provided recommendations for the design of medical buildings based on exposure risks;

[0142] (3) Achieved comprehensive and full-time identification and control of exposure risks and risk behaviors in key links;

[0143] (4) A multi-area infection risk analysis method in medical buildings focusing on the aerosol transmission of respiratory infectious diseases was developed.

[0144] This embodiment of the present application also provides an application scenario that utilizes the aforementioned method for assessing the cross-regional transmission and infection risk of aerosols within medical buildings. Specifically, the method for assessing the cross-regional transmission and infection risk of aerosols within medical buildings provided in this embodiment can be applied in a transmission and assessment scenario, which includes a simulation calculation phase and a result display phase. The simulation calculation phase is used to complete aerosol transmission simulation and infection risk assessment, and the result display phase is used to display aerosol distribution and assessment results. The method for assessing the cross-regional transmission and infection risk of aerosols within medical buildings provided in this embodiment belongs to the simulation calculation phase.

[0145] Example 2

[0146] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for assessing the cross-regional transmission and infection risk of aerosols in a medical building is implemented.

[0147] Those skilled in the art will understand that Figure 10The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0148] In an exemplary embodiment, a computer device is also provided, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for assessing cross-regional aerosol transmission and infection risk in a medical building as described in Example 1.

[0149] Example 3

[0150] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for assessing the risk of aerosol cross-regional transmission and infection in a medical building as described in Example 1 is implemented.

[0151] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for assessing the risk of aerosol cross-regional transmission and infection in medical buildings, characterized by: The method for assessing cross-regional transmission and infection risk of aerosols in medical buildings includes: Constructing a medical building model and a computational human body model; the medical building model is a model of multiple areas in a real medical building, the areas including at least two of a registration area, a waiting area, a diagnosis room, a pharmacy, and a ward; Meshing the medical building model and the computational human body model respectively to obtain a gridded medical building model and a gridded computational human body model; Performing a steady-state simulation based on the gridded medical building model and real environment data to obtain an initial airflow field inside the gridded medical building model; the real environment data includes wind speed and temperature; and the initial airflow field includes wind speed distribution and temperature distribution; A transient simulation is performed based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and a personnel motion function to obtain an aerosol distribution inside the gridded medical building model; the personnel motion function is a function generated based on the walking route and movement of personnel walking from the entrance to the exit of a real medical building, and is used to simulate the actual walking process of personnel; Performing infection risk assessment based on the aerosol distribution to obtain an assessment result; Performing infection risk assessment based on the aerosol distribution to obtain an assessment result, specifically comprising: using the aerosol distribution as input, performing infection risk assessment using an improved Wells-Riley model to obtain an assessment result; The improved Wells-Riley model is: ； in, P i Calculate areas in gridded medical building models i The probability of infection; k It is an infectious agent; D i For personnel in the calculation area i inhalation dose; Min is the filtration efficiency; ； in, A For personnel in the calculation area i activity level; C i For the calculation area i Virus concentration; t For personnel in the calculation area i duration of stay; ； in, Vp i For the calculation area i The total volume of aerosol particles in the breathing zone, the breathing zone is the calculation area i The spherical space area with the person's nose as the center and the set radius as the radius; V i For the calculation area i volume; L is viral load; ； in, n For the calculation area i The total number of aerosol particles in the breathing zone; m ij For the calculation area i The breathing zone j The mass of an aerosol particle; ρ is the density of aerosol particles.

2. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 1 is characterized in that: Construct medical building models and computational human models, including: Acquiring architectural data and ventilation system design data for a real medical building; the architectural data including the location and dimensions of each of a plurality of areas in the real medical building, as well as the location and dimensions of doors and windows, passageways, partitions, and equipment within each area; and the ventilation system design data including the location and dimensions of vents, ventilation ducts, and ventilation equipment. constructing a physical model of a real medical building based on the building data and the ventilation system design data; A cubic entity is established in the personnel walking area in the physical model to obtain an intermediate physical model; the personnel walking area is an area determined based on the personnel walking route; Extracting the volume of the fluid domain from the intermediate physical model to obtain a medical building model; A human body model is obtained, and the human body model is segmented and a shell is established to obtain a computational human body model.

3. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 1 is characterized in that: Meshing the medical building model and the computational human body model respectively to obtain a meshed medical building model and a meshed computational human body model, specifically comprising: meshing the medical building model and the computational human body model respectively using a meshing method based on a watertight workflow to obtain a meshed medical building model and a meshed computational human body model; The meshing method based on the watertight workflow includes the following steps: importing the geometric model, adding local dimensions, generating surface meshes, describing the geometric structure, updating boundaries, triggering quality improvement, adding boundary layers, and generating volume meshes; For the medical building model, adding local dimensions includes: performing local face encryption on doors, windows, and vents, and performing local body encryption on the cubic entity of the personnel walking area; updating boundaries includes: setting the air inlet in the vents as a velocity inlet, setting the air outlet in the doors, windows, and vents as a pressure outlet, and setting the remaining boundaries except the air inlet, doors, windows, and exhaust as walls; For the computational human body model, updating the boundary includes: setting the human body mouth as a velocity inlet, setting the remaining boundaries of the human body except the human body mouth as wall surfaces, and setting the outer shell as an inner surface.

4. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 1 is characterized in that: Performing a steady-state simulation based on the gridded medical building model and real environment data to obtain an initial airflow field inside the gridded medical building model, specifically comprising: using the gridded medical building model and the real environment data as input, performing a steady-state simulation according to the steps of importing a background grid, selecting a model, setting materials, setting unit area conditions, setting boundary conditions, setting a solution method, initializing, and running a calculation, to obtain the initial airflow field inside the gridded medical building model; Wherein, importing the background grid includes: importing the gridded medical building model; Model selection includes: selecting turbulence model, energy equation and component transport model; The material setting includes: setting the ground, ceiling, doors, windows, partitions, equipment, vents, ventilation ducts and ventilation equipment of the grid medical building model to solid properties, and setting the fluid domain of the grid medical building model to air properties; the fluid domain is the space where the air is located in the grid medical building model; The boundary condition setting includes: setting the material and temperature of the wall, setting the outlet momentum, and setting the wind speed and temperature of the inlet; the outlet is the exhaust vent in the doors, windows, and vents of the gridded medical building model, the inlet is the air inlet in the vents of the gridded medical building model, and the wall is the remaining boundary of the gridded medical building model except the outlet and inlet; Solution method settings include: setting the solution method; Initialization includes: setting the wind speed and temperature inside the gridded medical building model.

5. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 4 is characterized in that: The turbulence model is the Realizable k-ɛ model; the component transport model is the component transport model; the wall material is the real material of the wall in the real medical building; Select Suppress Backflow for the outlet momentum; select Pressure-Based Coupling Algorithm for the solution method; select Hybrid Initialization Method for the initialization; and select Steady-State Iteration Algorithm for the run calculation.

6. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 1, characterized in that: performing a transient simulation based on the gridded medical building model, the gridded computational human body model, the initial airflow field, and the personnel motion function to obtain an aerosol distribution inside the gridded medical building model, specifically comprising: using the gridded computational human body model and the personnel motion function as inputs to the gridded medical building model with the initial airflow field, performing a transient simulation according to the steps of attaching component grids, selecting a model, setting materials, setting boundary conditions, and running a calculation to obtain an aerosol distribution inside the gridded medical building model; The adding component grid includes: using the gridded medical building model as a background grid, using the gridded computational human body model as a component grid, connecting the background grid and the component grid using overlapping grids, and introducing a personnel motion function; the personnel motion function is used to drive the component grid to move in the background grid during transient simulation; Model selection includes: selecting a discrete phase model; The material setting includes: setting the human body of the gridded computational human body model to have solid properties; The boundary condition setting includes: setting the material and temperature of the wall, setting the wind speed and temperature of the inlet, and setting the shell of the gridded calculation human body model as an overlapping boundary; the inlet is the human mouth of the gridded calculation human body model, and the wall is the remaining boundaries of the human body of the gridded calculation human body model except the human mouth.

7. The method for assessing the risk of aerosol cross-regional transmission and infection in a medical building according to claim 6, characterized in that: The settings for the discrete phase model include: turning on the interaction between the discrete phase and the continuous phase, selecting unsteady particle tracking for particle processing, selecting to inject particles at the particle time step, selecting to synchronize the particle time step size with the person's breathing rate, turning on thermophoresis, Saffman lift, and pressure gradient forces, selecting surface injection as the injection source type, selecting the human mouth as the surface injection, and selecting the discrete phase properties to be the same as those for the aerosol.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that the processor executes the computer program to implement the method for assessing cross-regional aerosol transmission and infection risk in a medical building according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assessing cross-regional aerosol transmission and infection risk in a medical building according to any one of claims 1 to 7 is implemented.

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