Aerosol cross-regional transmission and infection risk assessment method in medical building and application

Abstract

The invention discloses an aerosol cross-regional transmission and infection risk assessment method in a medical building and application, and relates to the technical field of public health, and the method comprises the steps: constructing a medical building model and calculating a human body model; performing grid division on the medical building model and the calculation human body model to obtain a grid medical building model and a grid calculation human body model; performing steady-state simulation based on the gridding medical building model and the real environment data to obtain an initial airflow field in the gridding medical building model; performing transient simulation based on the gridding medical building model, the gridding calculation human body model, the initial airflow field and the personnel motion function to obtain aerosol distribution in the gridding medical building model; and performing infection risk assessment based on aerosol distribution to obtain an assessment result. According to the invention, cross-regional aerosol transmission in a medical building can be accurately simulated, and infection risk assessment is further carried out.

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 cross-regional transmission and infection risk of aerosols in medical buildings. Background Art

[0002] Respiratory infectious diseases have always occupied an extremely important position in the field of global public health technology due to their highly contagious and rapid spread. Such 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 place with an important role, medical buildings are the main places for the spread of respiratory infectious diseases. In medical buildings, aerosol transmission not only involves the direct exhaled droplets of personnel (patients), but also includes secondary aerosols generated when personnel move in different areas. Therefore, understanding and simulating the aerosol diffusion and transmission mechanism in medical buildings is crucial to the formulation of effective infection control measures.

[0003] At present, computational fluid dynamics (CFD) is an important numerical simulation tool for studying indoor airflow fields and aerosol distribution. It can effectively simulate the airflow environment and aerosol transmission in medical buildings. The Wells-Riley model is an important method for assessing infection risk and can also make preliminary calculations on the infection probability in medical buildings. Although existing studies have provided a certain basis for the calculation of the spread of respiratory infectious diseases in medical buildings, there are still many problems to be solved: (1) Most studies focus on single closed 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 transmission and infection risk of aerosols in medical buildings, which can accurately simulate the cross-regional transmission of aerosols 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 in a medical building, the method comprising:

[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, and the areas include 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 meshed medical building model and a meshed computational human body model;

[0009] A steady-state simulation is performed based on the grid medical building model and real environment data to obtain an initial airflow field inside the grid medical building model; the real environment data includes wind speed and temperature; 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 the personnel movement function to obtain the aerosol distribution inside the gridded medical building model; the personnel movement function is a function generated based on the walking route and movement of personnel from the entrance to the exit of a real medical building, and is used to simulate the walking process of a real person;

[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 cross-regional transmission and infection risks of aerosols 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 risk of cross-regional transmission and infection of aerosols in a medical building, constructing a medical building model and a computational human body model, wherein 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; 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 a personnel motion function to obtain an aerosol distribution inside the gridded medical building model, wherein the personnel motion function is a function generated based on the walking route and movement of personnel from the entrance to the exit of a real medical building, and is used to simulate the walking process of real personnel; an infection risk assessment is performed based on the aerosol distribution to obtain an assessment result. This application establishes a multi-region medical building model in a real medical building, and then combines subsequent grid division, steady-state simulation and transient simulation to complete the simulation of aerosol distribution in multiple regions of the medical building, and solve the problem that the cross-regional transmission mechanism of aerosols in different spaces is still unclear. In addition, by designing personnel motion functions, it is possible to simulate the actual movements of personnel in the medical building, and solve the problem of limitations in simulating personnel movements, so as to accurately simulate the cross-regional transmission of aerosols in medical buildings and further conduct 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

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

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

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

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

[0021] Figure 5 A schematic diagram of a medical building model and a 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 A schematic diagram of a background grid, a component grid, and an overlapping grid provided in Example 1 of the present application; wherein: Figure 6 (a) is the background grid. Figure 6 (b) is the component mesh. Figure 6 (c) in the figure is an overlapping grid.

[0023] Figure 7 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] Fig. 9 Schematic diagram of infection risk distribution provided in Example 1 of the present application.

[0026] Fig.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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 a medical building provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the 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 relevant data of the real medical building, relevant data of personnel walking and environmental data) to the server. After the server receives 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; the medical building model and the computational human body model are meshed respectively 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 the real environment data to obtain the 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 the aerosol distribution inside the gridded medical building model; an infection risk assessment is performed based on the aerosol distribution to obtain an assessment result. The server can feed back the obtained aerosol distribution and assessment results to the terminal.

[0030] In addition, in some embodiments, the method for assessing cross-regional transmission and infection risk of aerosols 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 terminal may be, but is 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 may be 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, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. 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-regional transmission and infection risk assessment in the medical building includes the following steps:

[0033] Step S1, constructing a medical building model and calculating a 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 meshed medical building model and a meshed 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 in 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 can complete the simulation of aerosol distribution in multiple areas of the medical building by establishing a medical building model of multiple areas in the real medical building, and then combining subsequent grid division, steady-state simulation and transient simulation, so as to solve 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 movement of personnel from the entrance to the exit of the real medical building, which is used to simulate the real walking process of personnel, and can complete the simulation of actual personnel movements in the medical building, solve the problem of limited simulation of personnel movement, so as to 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] Acquiring various parameters of various areas in a real medical building includes: acquiring relevant data on building data, material data, ventilation system design data, and personnel flow line design data. Building data refers to the actual structure and size of each area that constitutes a real medical building. The areas include registration areas, waiting areas, diagnosis rooms, pharmacies, wards, and other areas. Building data includes the location and spatial size of each area in multiple areas of a real medical building, as well as the location and size of doors and windows inside each area, the location and size of passages, the location and size of partitions, and the location and size of equipment. Passages refer to corridors for people to walk. Partitions refer to components used to separate two areas, generally walls or glass, etc. Equipment refers to tables, chairs, office equipment, medical equipment, etc. located in the area. Material data refers to the building materials used by components in various areas that constitute 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 the relevant data of the ventilation system designed to ensure the air quality in the real medical building. The ventilation system design data includes the location and size of the vents, the location and size of the ventilation ducts, the location and size of the ventilation equipment, ventilation strategies, air purification systems and control systems. The location and size of the vents include the location and size of the air inlet and the location and size of the exhaust. The location and size of the ventilation ducts reflect the layout of the ventilation ducts. The ventilation strategy, air purification system and control system are used to provide parameters such as ventilation rate and ventilation volume, which are subsequently used to set the wind speed inside the gridded medical building model in steady-state simulation. The personnel streamline design data is obtained from the actual spatial layout of the real medical building and the actual walking path of the personnel. The personnel streamline design data includes the walking route and movement of personnel from the entrance to the exit of the real medical building. The movement of personnel includes the speed, direction and other information of personnel when walking along the walking route, including uniform linear motion, variable speed linear motion, curved motion, rotational motion, etc., which are subsequently used to generate the personnel motion 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 the computational human body model is completed, including: using ANSYS Space Claim software to build the medical building model and the computational human body model. ANSYS Space Claim software is a three-dimensional solid direct modeling software. The medical building model is constructed according to the building data and ventilation system design data in various parameters of each area of ​​the above-mentioned real medical building. The areas are connected by doors and windows. A cubic entity is established in the personnel walking area corresponding to the personnel walking route, which is used for subsequent local mesh encryption, volume extraction of the fluid domain of the medical building, and generation of the medical building model. A high-fidelity human body model is created using online open source three-dimensional modeling software. The human body model should take into account the real geometric characteristics of the human body to more accurately simulate the human body thermal plume and microenvironment. Then, the human body model is faceted and the shell is established using ANSYS Space Claim software to obtain a computational human body model. Faceting refers to dividing the human body model into multiple facets, and shell establishment refers to establishing a cubic entity that completely wraps the human body model. The size of the cubic entity that completely wraps the human body model is smaller than the size of the cubic entity corresponding to the personnel walking area. The human body walks in the personnel walking area later.

[0045] In this embodiment, the medical building model and the computational human model are further group-named and the .scdoc file is exported. Group naming refers to naming each component in the medical building model and the computational human model. For example, for the medical building model, the ground, ceiling, doors and windows, partitions, equipment, air inlet, air outlet, ventilation duct, ventilation equipment, etc. are named; for the computational human model, the shell, human body, human mouth, etc. are named.

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

[0047] 1) Acquire architectural data and ventilation system design data of a real medical building, wherein 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 inside each area, and 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, wherein 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, and perform faceting and shell building on the human body model 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 the meshing based on the watertight workflow (Watertight Geometry), including: importing the geometric model, adding local dimensions, generating surface meshes, describing the geometric structure, updating boundaries, triggering quality improvement, adding boundary layers, generating volume meshes, 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] Among them, the meshing method based on the watertight workflow includes the steps of 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 of 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 selected to be generated; generating a volume mesh refers to generating a volume mesh of 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 the medical building model, the meshes of the walking area, doors, windows and vents need to be partially encrypted. Then adding local dimensions includes: local face encryption of doors, windows and vents, local body encryption of the cube entity in the walking area, and the resolution of the mesh generated by local face encryption and local body encryption is smaller than the resolution of the mesh generated in the generated face mesh and generated body mesh, so as to achieve local encryption. Updating boundaries includes: setting the air inlet in the vent as the velocity inlet, setting the air outlet in the doors, windows and vents as the pressure outlet, and setting the remaining boundaries except the air inlet, doors, windows and exhaust vents as the wall. The boundary refers to the edge in the medical building model, such as walls, equipment, etc.

[0055] For calculating 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, where the boundary refers to the edge in the human body.

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

[0057] This embodiment uses CFD software Ansys Fluent to simulate the airflow field, selects a suitable 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 initial airflow field in the medical building is constructed, including: importing background grids, model selection, material settings, unit area condition settings, boundary condition settings, solution method settings, initialization, and running calculations.

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

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

[0060] The unit region 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] Initialization A hybrid initialization method is selected to initialize the wind speed and temperature inside the gridded medical building model.

[0064] Run the calculation selecting the Steady-State Iteration 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 an initial airflow field inside the gridded medical building model, specifically including: taking the gridded medical building model and the real environment data as input, and performing a steady-state simulation according to the steps of importing background grids, model selection, material settings, unit area condition settings, boundary condition settings, solution method settings, initialization, and running calculations to obtain an initial airflow field inside the gridded medical building model.

[0066] Wherein, 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 as solid properties, setting the fluid domain of the gridded medical building model as 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 vents 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 selects the real material of the wall in the real medical building, and the outlet momentum selects to suppress backflow. The wall temperature, inlet wind speed and temperature are all determined based on real environmental data, and the wind speed can also be manually determined with reference to the ventilation system design data.

[0070] The solution method setting includes: 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 Iteration 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 grid medical building models with different resolutions (i.e. different numbers of grids) were established, and steady-state airflow fields were established respectively to construct the initial airflow field in the medical building. For each grid medical building model, the airflow velocity (i.e. wind speed) and temperature at 6 points at different heights were checked through the initial airflow field. If 3 heights were selected, 18 airflow velocities and temperatures could be obtained, and the average values ​​of 18 airflow velocities and 18 temperatures were calculated to obtain the average airflow velocity and average temperature. The grid medical building model with the largest number of grids was used as the benchmark, and the relative differences between the average airflow velocity and average temperature of the grid medical building models with different numbers of grids and the benchmark were compared in order from large to small. When the relative difference was less than 1%, in order to reduce the processing cost and time requirements, the grid 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 of the gridded medical building model with the benchmark and different numbers of grids, respectively. Only when the relative difference of the average airflow velocity and the average temperature is less than 1%, it is considered that the relative difference is less than 1%.

[0079] (5.2) Verification of simulation results

[0080] Use sensors to measure the airflow velocity and temperature at 6 points at different heights as experimental data, and compare the simulation result data with the experimental data to verify the simulation result data to ensure accuracy. If the verification fails, adjust the two input quantities of temperature and wind speed in the steady-state simulation.

[0081] (6) Construction of aerosol particle simulation model

[0082] The construction of the aerosol particle simulation model includes: using the Euler-Lagrangian method to simulate the movement of aerosols, considering the evaporation of particles, thermophoretic force, Saffman lift and other additional forces. During the simulation, the discrete phase model (Discrete Phase Model, DPM) is used to track the trajectory of the aerosols exhaled by personnel. In the discrete phase model settings, the stochastic tracking model is checked to consider the influence of turbulence on the trajectory of particles, and the distribution of aerosols exhaled by personnel in various areas of the medical building is obtained.

[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-Lagrangian model that works based on the Euler-Lagrangian 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 turns on the interaction between the discrete phase and the continuous phase, the particle processing is unsteady particle tracking, and the particles are sprayed at the particle time step. The particle time step size is synchronized with the breathing frequency of the personnel and can be set to 2s. Thermophoresis, Saffman lift, and pressure gradient force are turned on. The spray source type is surface spray, and the surface spray is the mouth of the person. The specific discrete phase properties are the same as those of the aerosol.

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

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

[0088] Based on the background grid and the component grid, the Overset module in Ansys Fluent is used to connect the two sets of 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 kind of dynamic grid technology. By establishing the background grid and the component grid, the movement of the component grid in the background grid can be completed. Overlapping grid is composed of overlapping background grid and component grid. Each grid area overlaps in space, but there is no connectivity relationship. They exist independently. 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, and solves them on the background grid and the component grid respectively. The interpolation unit constitutes the internal boundary condition, which is used to transmit data, and finally obtains the flow field information in the entire calculation domain, thereby 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 cross-area walking route of personnel in the medical building is determined, and 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, the discrete phase model and overlapping grids are further 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 including: taking the gridded computational human body model and the personnel motion function as inputs of the gridded medical building model with the initial airflow field, performing transient simulation according to the steps of additional component gridding, model selection, material setting, boundary condition setting and running calculation 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, which is used to drive the component grid to move in the background grid during transient simulation.

[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 spray particles at the particle time step, selecting the particle time step size to be synchronized with the breathing frequency of the personnel, turning on thermophoresis, Saffman lift, and pressure gradient force, selecting surface spray as the spray source type, and the surface spray is the human mouth. The discrete phase property selection is the same as the aerosol.

[0094] Material settings include: setting the human body of the meshed human body model to be 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 an 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 Iteration 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 is based on the assumption that the air in the space is fully mixed and the aerosol concentration will not change over time. In reality, aerosols are often non-uniformly distributed. Therefore, this embodiment further designs an improved Wells-Riley model, and uses the improved Wells-Riley model to assess infection risk, including: dividing the gridded medical building model into multiple calculation areas, counting the number of aerosol particles in the breathing zone in each calculation area, and 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 achieve a quantitative assessment of the infection risk of each calculation area. The infection risk of each calculation area is assessed by the improved Wells-Riley model, and targeted suggestions and opinions are put forward for improving hospital ventilation strategies, improving functional zoning layout, and optimizing the flow of medical personnel.

[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 is the total volume of aerosol particles in the breathing zone of calculation area i. 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 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 area 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 aerosol distribution to obtain an assessment result, specifically including: taking aerosol distribution as input, using an improved Wells-Riley model to perform infection risk assessment to obtain an assessment result.

[0109] This embodiment provides a method for assessing the cross-regional transmission and infection risk of aerosols in medical buildings based on computational fluid dynamics and an improved Wells-Riley model. The cross-regional walking process of respiratory infectious disease personnel in medical buildings and the corresponding aerosol transmission characteristics are studied by numerical simulation, and the infection risk is assessed based on the simulation results to guide the design and management of the medical environment. Compared with the prior art, the beneficial effects of this embodiment are as follows: by establishing a physical model of a real hospital scene, combining the path of personnel walking across regions, using Ansys Fluent to simulate the dynamic process of exhaled aerosols when personnel walk, and the model can be verified and improved according to the environmental data in the real scene, wherein the personnel walking uses a method combining overlapping grids and user-defined functions, and overlapping grids can not only simplify the grid division of complex geometry, but also facilitate the grid generation of relatively moving parts. Combined with the improved Wells-Riley model, the infection risk of each calculation area is assessed, which can improve the hospital ventilation strategy, improve the functional zoning layout, and optimize the flow line of medical personnel.

[0110] like Figure 3 and Figure 4 As shown, this embodiment describes the method proposed in this embodiment in detail in combination with a fever clinic of a hospital in Wuhan, and 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 line design data.

[0112] Step 102: Use Ansys Space Claim to complete the construction and volume extraction of the medical building model and computational human 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 the design drawings, on-site measurements and the layout of the air-conditioning supply and return ducts, 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 various parameters of various areas in real medical buildings, 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 walking area for the personnel to create a finer mesh in the 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 should match the cube entity size of the walking area, and name the group. Medical building model and computational human body model are as follows Figure 5 shown.

[0122] Step 103 specifically includes:

[0123] Use Ansys FluentMeshing for meshing: select meshing based on watertight workflow, 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 groups corresponding to vents, and the groups corresponding to the human mouth, select volume encryption, and check the entities in the area where people walk; generate surface meshes; describe the geometric structure as a geometric structure consisting only of fluid areas without gaps; update boundaries: set the air inlet and the human mouth as velocity inlets, the return air outlets 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 to generate poly-hexore meshes.

[0124] The above-mentioned poly-hexcore volume mesh generation method can realize common node connection between hexahedral mesh and polyhedral mesh, and does not require any additional manual mesh setting. It can increase the number of hexahedrons in the mesh to achieve the purpose of improving solution efficiency and accuracy.

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

[0126] Step 104 specifically includes:

[0127] Use Ansys Fluent to simulate the airflow field: import the .msh file of the gridded medical building model, and 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 for 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 in Figure 7 shown.

[0128] Material settings include solid material, fluid material, and mixed material settings. The materials of internal and external walls, ceilings, floors, tables, chairs, and other equipment are set according to the actual situation of medical buildings. Unit area condition settings include fluid domain material and source item settings. The solution method setting selects the pressure-based coupling algorithm.

[0129] Step 105 specifically includes:

[0130] Modify the local grid size and the global grid size, establish 5 sets of gridded medical building models with different resolutions, establish steady-state airflow fields respectively, check the airflow velocity and temperature at 6 points at different heights, and compare the relative differences of airflow velocity and temperature under different numbers of grids. When the relative difference is less than 1%, in order to reduce the processing cost and time requirements, select the gridded medical building model with a smaller number of grids for subsequent calculation and simulation.

[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 under the above appropriate number of grids is completed, switch the simulation type to transient, turn on the discrete phase model, turn on the interaction between the discrete phase and the continuous phase, treat the particles as unsteady particle tracking, and spray particles at the particle time step. The particle time step size is synchronized with the breathing frequency of the personnel and can be set to 2s. Turn on the thermophoresis force, Saffman lift force, and pressure gradient force. The spray source type is surface spray, and the surface spray is the mouth of the personnel. The specific discrete phase physical properties are the same as those of the aerosol.

[0134] The personnel movement function defined by the DEFINE_ZONE_MOTION macro is imported into the zone movement 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 evaluated by the improved Wells-Riley model. The gridded medical building model is first divided into multiple calculation areas, and the number of aerosol particles in the breathing zone of each calculation area is counted. Then the improved Wells-Riley model is used to achieve a quantitative assessment of 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: Fig. 9 shown.

[0138] This embodiment aims to simulate the spread of aerosol particles exhaled by real respiratory infectious disease personnel when walking according to the airflow environment in a real medical building. Based on the distribution of aerosol particles carrying viruses, the improved Wells-Riley model is used to calculate the infection risk of each calculation area in the medical building. Based on the infection risk of each calculation area in the medical building, the hospital ventilation strategy is improved, the functional zoning layout is improved, and the flow line of medical personnel is optimized. The dynamic simulation and risk assessment of aerosols containing viruses exhaled by people walking in medical buildings are realized, which is of great significance for improving hospital ventilation strategies, improving functional zoning layout, and optimizing medical personnel flow lines.

[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 by considering personnel behavior;

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

[0142] (3) Achieved full-scale, all-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] The embodiment of the present application also provides an application scenario, which applies the above-mentioned method for assessing the risk of cross-regional transmission and infection of aerosols in medical buildings. Specifically, the method for assessing the risk of cross-regional transmission and infection of aerosols in medical buildings provided in this embodiment can be applied in transmission and assessment scenarios, and the transmission and assessment scenarios include simulation calculation links and result display links. The simulation calculation link is used to complete the simulation of aerosol transmission and infection risk assessment, and the result display link is used to display the aerosol distribution and assessment results. The method for assessing the risk of cross-regional transmission and infection of aerosols in medical buildings provided in this embodiment belongs to the simulation calculation link.

[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: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the 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 the 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 risk of cross-regional transmission and infection of aerosols in a medical building is implemented.

[0147] Those skilled in the art will understand that Fig.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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0148] In an exemplary embodiment, a computer device is also provided, including: 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 method for assessing cross-regional transmission and infection risk of aerosols 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 described in Example 1 is implemented.

[0151] The technical features of the above embodiments may 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 in that: The aerosol cross-regional transmission and infection risk assessment method in the medical building 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, and the areas include 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 meshed medical building model and a meshed computational human body model; A steady-state simulation is performed based on the grid medical building model and real environment data to obtain an initial airflow field inside the grid medical building model; the real environment data includes wind speed and temperature; 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 the personnel movement function to obtain the aerosol distribution inside the gridded medical building model; the personnel movement function is a function generated based on the walking route and movement of personnel from the entrance to the exit of a real medical building, and is used to simulate the walking process of a real person; An infection risk assessment is performed based on the aerosol distribution to obtain an assessment result.

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: Acquire architectural data and ventilation system design data of a real medical building; the architectural data includes the position and size of each of a plurality of areas in the real medical building, as well as the position and size of doors and windows, the position and size of passages, the position and size of partitions, and the position and size of equipment inside each area; the ventilation system design data includes the position and size of vents, the position and size of ventilation ducts, and the position and size of 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 of the intermediate physical model to obtain a medical building model; A human body model is obtained, and the human body model is faceted 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, 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 steps of importing a geometric model, adding local dimensions, generating surface meshes, describing geometric structures, 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 vent as the velocity inlet, setting the air outlet in the doors, windows and vents as the pressure outlet, and setting the remaining boundaries except the air inlet, doors, windows and exhaust vents as the wall surface; 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 a wall surface, 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, characterized in that: Based on the grid medical building model and the real environment data, a steady-state simulation is performed to obtain an initial airflow field inside the grid medical building model, specifically comprising: taking the grid 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 calculations, to obtain an initial airflow field inside the grid 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 and 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 grid medical building model, the inlet is the air inlet in the vents of the grid medical building model, and the wall is the remaining boundaries of the grid medical building model except the outlet and the inlet; The solution method setting includes: 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, characterized in that: The turbulence model selects the Realizable k-ε model; the component transport model selects the component transport model; the wall material selects 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 initialization; and select Steady-state Iteration Algorithm for running the 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: Based on the grid medical building model, the grid computational human body model, the initial airflow field and the personnel motion function, a transient simulation is performed to obtain the aerosol distribution inside the grid medical building model, specifically comprising: taking the grid computational human body model and the personnel motion function as inputs of the grid medical building model with the initial airflow field, performing a transient simulation according to the steps of additional component grid, model selection, material setting, boundary condition setting and running calculation, to obtain the aerosol distribution inside the grid medical building model; The additional 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 importing 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 computational human body model as an overlapping boundary; the inlet is the human mouth of the gridded computational human body model, and the wall is the remaining boundaries of the human body of the gridded computational 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 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 property selection is the same as the aerosol.

8. 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 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: Among them, 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 the calculation area i; Min is the filtration efficiency; DACit; Where A is the activity level of personnel in calculation area i; C i is the virus concentration in the calculation area i; t is the time the person stays in the calculation area i; Where Vpi is the total volume of aerosol particles in the breathing zone of calculation area i. 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 viral load; Where n is the total number of aerosol particles in the breathing zone of calculation area i; mi j is the mass of the jth aerosol particle in the breathing zone of calculation area i; ρ is the density of aerosol particles.

9. 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 method for assessing cross-regional transmission and infection risk of aerosols in a medical building as described in any one of claims 1 to 8.

10. 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 transmission and infection risk of aerosols in a medical building described in any one of claims 1 to 8 is implemented.

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