Respiratory infectious disease transmission risk assessment method based on multi-person dynamic scene

Abstract

The invention relates to a respiratory infectious disease transmission risk assessment method based on a multi-person dynamic scene. The method sequentially comprises the steps of building models of a risk assessment object and a research area, setting analysis parameters according to infection source characteristics, setting crowd movement parameters, carrying out numerical simulation aiming at diffusion of infectious substances, calculating a personnel exposure level and carrying out infectious disease infection risk comprehensive assessment. Building models of the risk assessment object and the research area, to be specific, building a calculation area of a multi-person dynamic scene where the respiratory infectious disease occurs, and performing calculation grid division on the area to build a calculation model; analysis parameters are set according to characteristics of infection sources, release characteristics of the infection substances from different areas of the respiratory system of a patient are considered, different infection source types and exhalation characteristics are set according to different scenes, then the process of exhalation of the infection substances and aerodynamic characteristics of the process are simulated, and a result with universality is constructed to serve as an input module.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel risk assessment in public health emergencies, and in particular to a method for assessing the risk of respiratory infectious disease transmission based on a multi-person dynamic scenario. Background Art

[0002] Among all types of public health emergencies, respiratory infectious diseases are an important type of public health emergency with high frequency, great harm and wide impact range. In recent years, respiratory infectious diseases represented by severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have seriously threatened human health and life safety, and caused huge losses to economic and social development.

[0003] However, in the related technologies, in terms of the exhalation process of infectious substances from the patient's respiratory system and the particle size distribution, there has not yet been a generally applicable research result for different production areas and different exhalation processes of infectious substances in the respiratory tract as an input module; in terms of the study of the law of propagation and diffusion of infectious substances, there is a lack of technology for comprehensive analysis of the law of diffusion of infectious substances in multi-person motion scenes using a human numerical model with real human form. At the same time, the relevant experiments are difficult and it is difficult to obtain comprehensive data, and research is usually carried out in combination with numerical simulation technology; in terms of infection risk assessment, traditional respiratory infectious disease transmission risk assessment methods usually assume that infectious substances are evenly distributed in space and the scene is in a steady state, without considering the impact of human movement in the environment.

[0004] In fact, in crowded public places such as hospitals, waiting rooms, terminals, schools, etc., there are often scenes where multiple people move at the same time (i.e., "multi-person dynamic scenes"). The spatial flow field in this scene is complex, which will significantly affect the diffusion process of infectious substances. However, there is currently a lack of infection risk assessment systems and risk assessment methods suitable for multi-person dynamic scenes. The risk assessment of dynamic scenes needs to be combined with dynamic calculations to correct the assessment results in order to achieve a more accurate quantitative assessment of regional risks and susceptible population risks. Therefore, it is necessary to construct a multi-person dynamic scene respiratory infectious disease risk assessment system and then establish a risk assessment method. Summary of the invention

[0005] The present invention aims to solve the related technical problems.

[0006] To this end, the present invention proposes a respiratory infectious disease transmission risk assessment system for multi-person dynamic scenarios, which can make up for the shortcomings of existing risk assessment systems in considering the dynamic behaviors of multiple people occurring simultaneously or successively in actual scenarios, improve the accuracy of risk assessment, and better ensure public safety.

[0007] The present invention is based on a multi-person dynamic scene respiratory infectious disease transmission risk assessment method, which specifically includes:

[0008] Build models of risk assessment objects and study areas, set analysis parameters based on the characteristics of infection sources, set crowd movement parameters, conduct numerical simulations on the spread of infectious agents, calculate personnel exposure levels, and conduct comprehensive assessments of infectious disease risks.

[0009] Specifically, the model of the risk assessment object and the research area is used to establish the calculation area of ​​the multi-person dynamic scene where the respiratory infectious disease occurs, and the calculation grid is divided into the above area to build the calculation model;

[0010] Set analysis parameters according to the characteristics of the infectious source, consider the release characteristics of infectious substances from different areas of the patient's respiratory system, set different types of infectious sources and exhalation characteristics according to different scenarios, and then simulate the process of exhaling infectious substances and their aerodynamic characteristics, and construct universal results as input modules;

[0011] In setting the analysis parameters according to the characteristics of the infectious source, the analysis parameters are to set the corresponding infectious substance release position and particle size distribution according to the pathological characteristics of the patients with the infectious disease to be analyzed, and select a suitable model to simulate the process of the patient exhaling infectious substance droplets and aerosols, and quantify their aerodynamic characteristics; the model is a discrete phase model or a component transport model.

[0012] In setting the analysis parameters according to the characteristics of the infectious source, the release characteristics include release site, release concentration, particle size distribution and release time.

[0013] The analysis parameters are set according to the characteristics of the infection source:

[0014] According to the characteristics of the infection source, the component transport model (STM, hereinafter referred to as STM) and the discrete phase model (DPM, hereinafter referred to as DPM) are used to simulate the spread of virus droplets.

[0015] A tracer gas was selected in the STM to represent the virus and its mass fraction in the patient's exhaled airflow was set.

[0016] In DPM, droplets of different sizes are released from the patient's mouth surface, and the droplet density, droplet release frequency, and single release quantity are set. The droplet trajectory is tracked using the Lagrangian model, and the discrete random walk model (DRW) is used to simulate the turbulent diffusion characteristics of the droplets. Calculated using Newton's second law:

[0017]

[0018] Where m p is the mass of the droplet, u p,i is the velocity of the droplet in the i direction. F g,i is gravity, F d,iis the resistance, F b,i It is Brownian force, F s,i is the Saffman lift. ρ i and ρ are the density of the droplet and the air respectively. D is the Stokes drag correction function for the Reynolds number. τ p is the characteristic response time.

[0019] The tracer gas is CO2.

[0020] By setting crowd motion parameters and using dynamic mesh technology in computational fluid dynamics (CFD), multi-person motion simulation in different scenarios can be realized by writing user-defined functions (UDF), thereby achieving reliable simulation of multi-person motion and its influence on the airflow field and the diffusion process of infectious substances.

[0021] The set crowd movement parameters include the number of people moving, the speed of people, the distance between people, and the relative position distribution of people.

[0022] The specific steps to set crowd motion parameters are:

[0023] The motion of the object is simulated using the dynamic mesh model, specifically using the smoothing method (Smoothing) and mesh reconstruction method (Remeshing) in the dynamic mesh model to perform dynamic mesh simulation.

[0024] The spring approximation smoothing (Spring-based) in the ANSYS Fluent system is used. The nodes are treated as ideal springs and the node positions are updated by calculating the balance of the spring forces. Spring force The calculation formula is:

[0025]

[0026] in and is the displacement of node i and its adjacent node j; n i is the number of nodes connected to node i; k ij is the spring stiffness, defined as:

[0027]

[0028] where k facIt is a configurable Spring Constant Factor parameter. When the node is in equilibrium, the spring force acting on the node should be 0, and the iterative relationship for position calculation is obtained based on this. There are three parameters that need to be set when calculating the spring smoothing method: spring constant factor, convergence tolerance, and number of iterations; spring constant factor, the value range is 0 to 1.

[0029] Dynamic mesh reconstruction is achieved by jointly using the reconstruction of internal grid cells (Local Cell) and the reconstruction of triangular surface meshes on the deformation boundary (Local Face).

[0030] For a scalar φ in the moving control volume V, the integral form of the moving mesh conservation equation is:

[0031]

[0032] Where ρ is the fluid density, is the flow velocity vector, is the grid velocity of the moving grid, Γ is the diffusion coefficient, S φ is the source term of φ. Here It is used to represent the boundary of the control volume V. In the numerical calculation process, the DEFINE_CG_MOTION macro used to define rigid body motion in the ANSYS Fluent system is called by writing a user-defined function (UDF) to realize the interactive motion of people at different speeds and directions. Due to the complex surface structure of the human body, it is easy to generate negative volume grids when simulating the motion of people. Therefore, in the pre-processing, ANSYS ICEM is used to generate a tetrahedral grid with strong adaptability, encrypt the grid around the human body, and add a prism layer to the boundary. In the calculation process, the boundary layer grid on the surface of the human body is fixed so that this part of the grid is not updated during the interactive motion of people, so as to reduce the degradation of the grid quality during the motion and improve the calculation accuracy.

[0033] Numerical simulation is carried out for the diffusion of infectious agents. According to the actual scenario constructed, corresponding boundary conditions are set, and the diffusion and transport process of infectious agents in multi-person dynamic scenes is analyzed to output the analysis results of the diffusion and transport of infectious agents.

[0034] The specific steps for numerical simulation of the spread of infectious agents are as follows:

[0035] Numerical simulations were carried out to study in detail the impact of the number of people in interactive motion, motion speed, personnel position distribution, droplet size and ventilation on the multi-person motion airflow, the diffusion and concentration distribution of droplets exhaled by patients, and regional dynamic risk assessments were carried out to provide scientific basis and technical support for the formulation of prevention and control strategies for respiratory infectious diseases in crowded places. The RNGk-ε turbulence model and SIMPLE algorithm were used to calculate the indoor flow field of multi-person interactive motion scenes, using a second-order headwind format. The Boussinesq model was used to simulate the buoyancy effect. The air density and ambient relative humidity were set. Multiple sets of numerical simulation conditions were designed to deeply study the impact of the number of people in motion, speed, personnel position distribution, ventilation and related factors of droplet size. In addition, multiple monitoring points were set to record transient airflow velocity.

[0036] The exposure level calculation of susceptible persons is used to obtain the spatiotemporal distribution evolution law of infectious substance concentration based on the corresponding infectious substance diffusion analysis results, and then obtain the spatiotemporal evolution characteristics of the exposure level of susceptible people.

[0037] The exposure level of susceptible persons includes instantaneous exposure dose and cumulative exposure dose.

[0038] The specific steps for calculating the exposure level of susceptible personnel are:

[0039] According to the corresponding infectious material diffusion analysis results, the spatiotemporal distribution evolution law of infectious material concentration is obtained, and then the spatiotemporal evolution characteristics of the exposure level of susceptible people are obtained. In this embodiment, by setting multiple real-time monitoring points, the spatiotemporal distribution evolution law of respiratory infectious materials in multi-person dynamic scenes is obtained. Specifically, for personal risk assessment, according to the Wells-Riley model, the infection probability P of a susceptible person depends on the number of inhaled infection quanta N quanta :

[0040]

[0041] Assuming that the virus is evenly distributed in the patient's exhaled gas, the relative concentration C r and infection quantum generation rate Q IP Calculate the infection probability of a susceptible person:

[0042]

[0043] Here C a is the mass fraction of CO2 in the patient's exhaled gas (4%), and C is the mass fraction of CO2 in the susceptible person's inhaled gas. The inhalation ratio IF is:

[0044]

[0045] Here M in Usually it can be set to 30L / min (susceptible people), Mex It can usually be set to 10L / min (patient), after exposure time T:

[0046]

[0047] Then the infection probability P of susceptible people is:

[0048]

[0049] For regional risk assessment, different height planes are considered to represent the breathing height planes of standing and sitting personnel respectively. The plane is divided into regions of corresponding areas. By calculating the average concentration of infectious substances in each region, equivalent to C in the individual risk assessment method, and substituting it into the above formula, the exposure and risk of each region can be calculated.

[0050] Comprehensive assessment of infectious disease risks is used to evaluate the dynamic risk distribution characteristics in space according to flow field evolution patterns, infectious substance diffusion processes and results, and exposure levels of susceptible populations, quantify the evolution laws of high-risk areas, and output risk distribution data.

[0051] The present invention constructs an indoor non-uniform risk assessment algorithm, which can be used for individual infection risk assessment and regional risk assessment. This method combines the significant differences in the distribution of infectious substance concentrations in different areas in actual multi-person dynamic scenes, and provides a feasible method for accurately conducting indoor risk assessment and correcting it in a non-uniform environment in combination with CFD simulation results.

[0052] The present invention is based on a respiratory infectious disease transmission risk assessment method for multi-person dynamic scenes. Dynamic exposure risk assessment of susceptible populations is performed through the spatial characteristics of the scene, typical motion parameters such as the number of people and relative position distribution of multiple movers and motion speed, and key environmental parameters such as temperature, humidity and ventilation conditions. This evaluates the dynamic evolution of regional risks of respiratory infectious diseases in typical crowded places in different scenes (such as hospitals, waiting rooms, subway stations, terminals or schools), solves the need for accurate prediction of infection risks of people in corresponding scenes, provides a scientific basis for formulating prevention and control strategies for crowded places, provides technical support for risk assessment and emergency management of public health emergencies, and ensures public safety.

[0053] The tool constructed based on the present invention can be used for infectious disease risk assessment in typical crowded places, and can help guide the comprehensive design of regional ventilation, filtration, air curtain and other measures to reduce the risk of respiratory infectious diseases. For transportation hubs, it can help design reasonable scheduling plans to reduce passenger exposure time and reduce the risk of cross infection.

[0054] In summary, the embodiment of the present invention is based on CFD technology, combined with a three-dimensional numerical human model, and uses a numerical simulation method of multi-person motion. It can carry out detailed research on the potential influencing factors of the number of people moving, the position distribution of people, speed, droplet size of infectious substances and ventilation in multi-person dynamic scenes, analyze the airflow characteristics of multi-person motion and its impact on the transmission of respiratory infectious diseases, and carry out regional dynamic risk assessment, and finally establish a non-uniform risk assessment method for indoor multi-person dynamic scenes, providing a scientific basis and technical support for the formulation of infectious disease prevention and control strategies in crowded places.

[0055] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention.

[0056] The risk assessment method of this patent invention can be used to optimize the existing respiratory infectious disease transmission risk assessment technology, improve the accuracy and reliability of risk assessment, and use CFD technology to supplement and replace high-cost field experiments. It can be used for respiratory infectious disease transmission risk assessment in crowded places such as hospitals, waiting rooms, subways, terminals, schools, etc., and guide the formulation of prevention and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0058] Figure 1 It is a flowchart of a method for risk assessment of respiratory infectious diseases in a multi-person dynamic scenario according to an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of setting a calculation area according to an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the risk assessment area division setting according to an embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram of a quantitative calculation result of infection risk of susceptible people in a space according to an embodiment of the present invention;

[0062] Figure 5 The present invention is a flowchart of a method for risk assessment of respiratory infectious diseases in a multi-person dynamic scenario according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0064] Please refer to the attached Figure 1-5 The multi-person dynamic scene respiratory infectious disease risk assessment method proposed according to an embodiment of the present invention is described. First, the respiratory infectious disease risk assessment system proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0065] Figure 1 It is a flow chart of the multi-person dynamic scenario respiratory infectious disease risk assessment method of the present invention.

[0066] Among them, the model construction of risk assessment objects and research areas (specifically called the model construction of risk assessment objects and research areas in the multi-person dynamic scene respiratory infectious disease transmission risk research area) is used to establish the calculation area of ​​the multi-person dynamic space where respiratory infectious diseases occur, and to grid the calculation area to build a calculation model.

[0067] The analysis parameters are set according to the characteristics of the infectious source. Mainly, the corresponding infectious substance release position and particle size distribution are set according to the pathological characteristics of the patients with the infectious disease to be analyzed, and a suitable model (discrete phase model, component transport model, etc.) is selected to simulate the process of patients exhaling infectious substance droplets and aerosols, and quantify their aerodynamic characteristics.

[0068] The crowd motion parameters are set mainly according to the scene environment conditions, and the computational fluid dynamics (CFD) dynamic mesh method of multi-person motion is set to realize multi-person dynamic scenes.

[0069] Numerical simulations are carried out on the diffusion of pollutants to set scenario modes according to current conditions, and the scenario modes are used as input conditions to numerically simulate the flow propagation process in order to output the results of the diffusion and transport analysis of infectious substances.

[0070] The calculation of personnel exposure level is used to obtain the spatial and temporal distribution of infectious substance concentration based on the results of infectious substance diffusion and transport analysis and to obtain the exposure level of susceptible population.

[0071] The comprehensive assessment of infectious disease infection risk is used to output the results of infectious substance diffusion and transport analysis, the exposure level of susceptible population and the risk distribution in the assessment space according to the airflow movement pattern, the process of exhaling infectious substances and its aerodynamic characteristics, and output the risk distribution results.

[0072] The evaluation system of the embodiment of the present invention can solve the need for accurate prediction of human infection levels in the risk assessment of infectious disease transmission in multi-person dynamic scenarios, and can be applied to the design of respiratory infectious disease protection programs and emergency plans for susceptible populations, thereby improving the accuracy of risk assessment and better ensuring public safety.

[0073] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0074] The following describes a multi-person dynamic scenario respiratory infectious disease risk assessment method according to an embodiment of the present invention with reference to the accompanying drawings. First, a respiratory infectious disease risk assessment system according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0075] Figure 1 FIG. 1 is a schematic diagram of the process structure of a respiratory infectious disease risk assessment system according to an embodiment of the present invention; Figure 1 As shown, the respiratory infectious disease risk assessment system includes: model building of risk assessment objects and research areas, setting analysis parameters according to the characteristics of the source of infection, numerical simulation of the spread of infectious agents, quantitative calculation modules and infectious disease risk assessment modules.

[0076] A specific embodiment is described in detail below with reference to the accompanying drawings.

[0077] In an embodiment of the present invention, Figure 1 As shown, the system of the embodiment of the present invention includes the model building of the risk assessment object and the study area; the source of infection characteristic module, which takes into account the factors such as the generation and release site, concentration, particle size distribution, and release time of the infectious substance; and carries out numerical simulation for the spread of infectious substances; the environmental condition control module, which takes into account the factors such as ventilation, temperature and humidity, and the location of the source of infection; the multi-person interactive motion numerical simulation module, which takes into account the factors such as the number of people exercising, speed, spacing, and relative position distribution; the exposure level quantitative calculation module of the susceptible population, which obtains the instantaneous exposure dose, cumulative exposure dose and other characteristics of the susceptible population; and the multi-person dynamic scene respiratory infectious disease transmission risk assessment module, which is used to carry out quantitative assessment of the transmission risk of respiratory infectious diseases in multi-person dynamic scenes.

[0078] Risk assessment objects and model construction of the research area for the risk of respiratory infectious diseases in multi-person dynamic scenarios:

[0079] like Figure 2 As shown in the figure, considering the typicality of the scene, a multi-person indoor scene of 10m×10m×3m (length×width×height) is constructed, which is similar to the scale of a small railway station waiting room, a subway platform, and a small and medium-sized hospital treatment area. The ventilation modes corresponding to different air inlets are displacement ventilation (DV), natural ventilation (NV), and mixed ventilation (MV). The area of ​​the air inlet and outlet is 1m 2. There are 7 people in the room, a static standing patient (red) and six moving persons M01 to M06 (blue). The patient's face is parallel to the moving person, the body width is 0.58m, and the spacing is set to 1.0m. According to the relative positions of the moving person and the patient, three scenarios were established to compare the influence of the relative position distribution of the personnel on the airflow results of multi-person interactive motion. These position distributions are named "triangular distribution" (scenario 1), "step distribution" (scenario 2) and "side-by-side distribution" (scenario 3). An unstructured tetrahedral mesh is generated in ANSYSICEM software, the mesh around the human body is encrypted, and a prismatic layer boundary is added.

[0080] Infection source feature module:

[0081] According to the characteristics of the infection source, the component transport model (STM) and discrete phase model (DPM) were used to simulate the spread of virus droplets. In STM, CO2 was selected as the tracer gas to represent the virus, and the mass fraction in the patient's exhaled airflow was set to 4%. In DPM, droplets with particle sizes of 1, 5, 10, 20, 50, and 100 μm were released from the patient's mouth surface, and the density was set to 1100 kg / m 3 The particles were released at a rate of 0.2s / time, 412 particles each time, for a total of 1500 times. The trajectory was tracked using the Lagrangian model, and the discrete random walk model (DRW) was used to simulate the turbulent diffusion characteristics of the droplets. Calculated using Newton's second law:

[0082]

[0083] Where m p is the mass of the droplet, u p,i is the velocity of the droplet in the i direction. F g,i is gravity, F d,i is the resistance, F b,i It is Brownian force, F s,i is the Saffman lift. ρ i and ρ are the density of the droplet and the air respectively. D is the Stokes drag correction function for the Reynolds number. τ p is the characteristic response time.

[0084] Multi-person interactive motion numerical simulation module:

[0085] Furthermore, in the numerical simulation process of personnel interaction movement, simulating personnel movement and its impact on airflow is a technical difficulty. The dynamic mesh model (Dynamic Mesh Model) in CFD simulates the movement of objects by defining the movement of boundaries or mesh nodes. It can be used to simulate the flow of domain shape changing over time due to the movement of the computational domain boundary. The reliability of this method has also been widely verified by experiments and has good adaptability. The core technology of the dynamic mesh method is the way to deal with the mesh movement caused by the movement of objects, that is, the mesh update method. The commonly used mesh update methods in the dynamic mesh method mainly include smoothing method (Smoothing), mesh reconstruction method (Remeshing) and dynamic layering method (Layering). The three-dimensional numerical human body model constructed in this embodiment has rich surface details and a complex generated mesh structure. Therefore, a smoothing method and a mesh reconstruction method are used in combination for dynamic mesh simulation. The smoothing method is a basic dynamic mesh method, which is mainly suitable for scenes with small deformations and the topological relationship of mesh nodes does not change during the deformation process. Three smoothing methods are provided in the ANSYSFluent system used: spring-based smoothing (Spring-based), diffusion smoothing (Diffusion), and linearly elastic solid (Linearly Elastic Solid). The present invention uses the spring approximate smoothing method. The basic idea of ​​this method is to regard the nodes as ideal springs and update the node positions by calculating the balance of the spring force. The calculation formula is:

[0086]

[0087] in and is the displacement of node i and its adjacent node j; n i is the number of nodes connected to node i; k ij is the spring stiffness, defined as:

[0088]

[0089] where k facIt is a configurable Spring Constant Factor parameter. When the node is in equilibrium, the spring force acting on the node should be 0, and the iterative relationship for position calculation is obtained based on this. There are three main parameters that need to be set when calculating the spring smoothing method: Spring stiffness coefficient (Spring Constant Factor), the value range is 0 to 1, the larger the value, the wider the impact range, and it is set to 0.7 in this embodiment; Convergence Tolerance, which is set to 0.001 in this embodiment; Number of Iterations, that is, the number of iterations for solving the iterative equation. In this embodiment, the initial value is set to 20, and it is adjusted according to the convergence situation during the calculation process.

[0090] The mesh reconstruction method is a dynamic mesh method that can solve arbitrary motion. The basic principle is to continuously monitor the mesh quality during the calculation process, mark the mesh with quality lower than the requirement and re-divide it. In practical applications, the mesh reconstruction method is mainly used for unstructured meshes, and is often used together with the smoothing method. In the ANSYS Fluent system, there are 5 mesh reconstruction methods: reconstruction of internal mesh cells (Local Cell), reconstruction of triangular face meshes on deformation boundaries (LocalFace), reconstruction of face meshes adjacent to motion boundaries (Region Face), reconstruction of mesh cells and face meshes of all regions (CutCell Zone), and reconstruction of prismatic layer units in 3D regions (2.5D). In this embodiment, the LocalCell and Local Face methods are used together to achieve dynamic mesh reconstruction.

[0091] For a scalar φ in the moving control volume V, the integral form of the moving mesh conservation equation is:

[0092]

[0093] Where ρ is the fluid density, is the flow velocity vector, is the grid velocity of the moving grid, Γ is the diffusion coefficient, S φ is the source term of φ. Here It is used to represent the boundary of the control volume V. In the numerical calculation process, the DEFINE_CG_MOTION macro used to define rigid body motion in the ANSYS Fluent system is called by writing a user-defined function (UDF) to realize the interactive motion process of people at different speeds and directions. Due to the complex surface structure of the human body, it is easy to generate negative volume grids when simulating the motion of people. Therefore, in the pre-processing, ANSYS ICEM is used to generate a tetrahedral grid with strong adaptability, encrypt the grid around the human body, and add 6 prism layers to the boundary. In the calculation process, the boundary layer grid on the surface of the human body is fixed so that this part of the grid is not updated during the interactive motion of people, so as to reduce the degradation of the grid quality during the motion and improve the calculation accuracy.

[0094] Numerical simulation of the spread of infectious agents:

[0095] Numerical simulations were carried out to study in detail the impact of the number of people in interactive motion, motion speed, personnel position distribution, droplet size and ventilation on the multi-person motion airflow, the diffusion and concentration distribution of droplets exhaled by patients, and regional dynamic risk assessments were carried out to provide scientific basis and technical support for the formulation of prevention and control strategies for respiratory infectious diseases in crowded places. The RNGk-ε turbulence model and SIMPLE algorithm were used to calculate the indoor flow field of multi-person interactive motion scenes, using a second-order upwind format. The Boussinesq model was used to simulate the buoyancy effect. The air density was set to 1.18kg / m3. The relative humidity of the environment was set to 50%. Multiple sets of numerical simulation conditions were designed to deeply study the impact of the number of people in motion, speed, personnel position distribution, ventilation and related factors of droplet size. In addition, multiple monitoring points were set to record transient airflow velocities.

[0096] Calculation of personnel exposure level:

[0097] According to the corresponding infectious material diffusion analysis results, the temporal and spatial distribution evolution law of the infectious material concentration is obtained, and then the temporal and spatial evolution characteristics of the exposure level of the susceptible population are obtained. In this embodiment, by setting multiple real-time monitoring points, the temporal and spatial distribution evolution law of respiratory infectious materials in a multi-person dynamic scene is obtained. Specifically, for personal risk assessment, the infection probability P of a susceptible person depends on the number of inhaled infection quanta N qua :

[0098]

[0099] Assuming that the virus is evenly distributed in the patient's exhaled gas, the relative concentration C r and infection quantum generation rate Q IP Calculate the infection probability of a susceptible person:

[0100]

[0101] Here C a is the mass fraction of CO2 in the patient's exhaled gas (4%), and C is the mass fraction of CO2 in the susceptible person's inhaled gas. The inhalation ratio IF is:

[0102]

[0103] Here M in Usually it can be set to 30L / min (susceptible people), M ex It can usually be set to 10L / min (patient), after exposure time T:

[0104]

[0105] Then the infection probability P of susceptible people is:

[0106]

[0107] like Figure 3 As shown in the figure, for regional risk assessment, the 1.55m and 1.25m height planes are considered to represent the breathing height planes of standing and sitting personnel respectively. The plane is divided into 1m×1m areas, and the average concentration of infectious substances in each area is calculated, which is equivalent to C in the individual risk assessment method. Substituting it into the above formula, the exposure and risk of each area can be calculated.

[0108] Infectious Disease Risk Assessment Module:

[0109] Combined with the basic data of setting the analysis parameter 300 according to the characteristics of the infection source, such as Figure 5 As shown in the figure, the area where the risk of infection of susceptible persons is higher than 5% when exposed for 30 minutes (typical waiting time in the waiting room of a train station) is regarded as a high-risk area. The virus release rate of COVID-19 patients can reach 725-2345 quanta / h. Considering the highest source of infection intensity, that is, 2345 quanta / h, C ≥ 1.75e-6 is introduced for the high-risk area. The change pattern of the high-risk area of ​​the 1.55m height plane in the scene of 6 people interacting with each other and the scene without motion over time was compared. It was found that before the interactive motion of multiple people, the high-risk area was concentrated in front of the right side of the patient, and in the 3 minutes after the multi-person motion, the high-risk area in front of the left side and the back of the patient also expanded significantly, which is probably related to the strong indoor convection caused by the multi-person motion, and also related to the follow-up of the virus aerosol exhaled by the patient to the tail of the left-hand moving person. Therefore, it can be seen that the multi-person motion simultaneously increases the range of the high-risk area in the breathing zone of standing and sitting people. For the breathing zone of standing personnel at z = 1.55 m, in the unmanned motion scene, the area of ​​the high-risk area (C ≥ 1.75e-6) gradually increases to about 20 m during 120 s to 300 s. 2Within about 50 seconds after the multi-person movement, the high-risk area expanded rapidly: at t = 120 seconds, the area of ​​the high-risk area was about 3.85m 2 , reaching 19.24m at t=170s 2 , and by t = 300s, the high-risk area has expanded to 34.53m 2 (8.97 times that of t = 120s), and there is still a trend of continued expansion, which is 20.57m higher than that of the non-interactive motion scene. 2 It is also 67.9% higher.

[0110] In summary, this embodiment calculates the mixed characteristics of infectious substances in multi-person dynamic scenes, and reveals the airflow characteristics of multi-person movement under different personnel position distributions. The personnel position distribution will affect the airflow pattern and the propagation law of infectious substances, among which the step distribution and side-by-side distribution are conducive to the lateral diffusion of infectious substances. Through regional dynamic risk assessment, the changes in high-risk areas can be quantified. It is revealed that multi-person movement significantly promotes the long-distance air transmission of respiratory infectious diseases and expands high-risk areas. Considering that multi-person movement often occurs frequently in crowded places, if the regional risk is still assessed based on the virus concentration distribution of static scenes, it may lead to large errors. Based on the present invention, dynamic risk assessment can be carried out in an environment with a large number of active personnel. Comprehensively design regional ventilation, filtration, air curtain and other measures to reduce the risk of respiratory infectious disease transmission. For transportation hubs, a reasonable scheduling plan should also be designed to reduce the exposure time of passengers and reduce the risk of cross-infection.

[0111] It should be noted that the above explanation of the embodiment of the multi-person dynamic scene respiratory infectious disease transmission risk assessment system is also applicable to the risk assessment method of this embodiment and will not be repeated here.

[0112] The multi-person dynamic scenario respiratory infectious disease risk assessment method proposed in an embodiment of the present invention performs exposure risk assessment of susceptible populations through measurement parameters such as spatial characteristics, personnel movement characteristics, and environmental characteristics, thereby evaluating the risk level of infectious diseases under different scenario couplings, solving the need for accurate prediction of personnel infection levels in infectious disease transmission risk assessment, providing a theoretical basis for formulating respiratory infectious disease prevention and control plans and emergency rescue, providing decision-making support for emergency management of public health emergencies, improving the accuracy of risk assessment, and better ensuring public safety.

[0113] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0114] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0115] For those skilled in the art, the specific meanings of the terms in the present invention can be understood according to specific circumstances.

[0116] In the description of this specification, reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention.

[0117] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0118] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for assessing the risk of respiratory infectious diseases based on multi-person dynamic scenarios, characterized in that: Including: Model building of risk assessment objects and study areas, setting analysis parameters according to the characteristics of infectious sources, setting crowd movement parameters, numerical simulation of the spread of infectious agents, calculation of personnel exposure levels, and comprehensive assessment of infectious disease infection risks; Model building of risk assessment objects and research areas, used to establish the calculation area of ​​multi-person dynamic scenes where respiratory infectious diseases occur, and divide the above areas into calculation grids to build calculation models; Set analysis parameters according to the characteristics of the infectious source, consider the release characteristics of infectious substances from different areas of the patient's respiratory system, set different types of infectious sources and exhalation characteristics according to different scenarios, and then simulate the process of exhaling infectious substances and their aerodynamic characteristics, and construct universal results as input modules; In setting the analysis parameters according to the characteristics of the infectious source, the analysis parameters are to set the corresponding infectious substance release position and particle size distribution according to the pathological characteristics of the patient of the infectious disease to be analyzed, and select a suitable model to simulate the process of the patient exhaling the infectious substance droplets and aerosols, and quantify the aerodynamic characteristics; the model is a discrete phase model or a component transport model; Set crowd motion parameters, use dynamic mesh technology in computational fluid dynamics (CFD), and write user-defined functions (UDF) to simulate multi-person motion in different scenarios, thereby achieving reliable simulation of multi-person motion and its influence on airflow field and diffusion process of infectious substances; Carry out numerical simulation for the diffusion of infectious substances, set corresponding boundary conditions according to the actual scenario, analyze the diffusion and transport process of infectious substances in multi-person dynamic scenes, and output the analysis results of the diffusion and transport of infectious substances; The exposure level calculation of susceptible persons is used to obtain the temporal and spatial distribution evolution law of infectious substance concentration according to the corresponding infectious substance diffusion analysis results, and then obtain the temporal and spatial evolution characteristics of the exposure level of susceptible people; The comprehensive assessment of infectious disease infection risk is used to evaluate the dynamic risk distribution characteristics in space according to the flow field evolution pattern, the diffusion process and results of infectious substances, and the exposure level of susceptible populations, quantify the evolution law of high-risk areas, and output risk distribution data.

2. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: In setting the analysis parameters according to the characteristics of the infectious source, the release characteristics include release site, release concentration, particle size distribution and release time.

3. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: The set crowd movement parameters include the number of people moving, the speed of people, the distance between people, and the relative position distribution of people.

4. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: The exposure level of susceptible persons includes instantaneous exposure dose and cumulative exposure dose.

5. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: The analysis parameters are set according to the characteristics of the infection source: According to the characteristics of the infectious source, the component transport model and the discrete phase model are used to simulate the spread of virus droplets. Select the tracer gas to represent the virus in the component transport model and set its mass fraction in the patient's exhaled airflow; In the discrete phase model, droplets of different sizes are released from the patient's oral surface, and the droplet density, droplet release frequency, and single release quantity are set. The droplet trajectory is tracked using the Lagrangian model, and the discrete random walk model (DRW) is used to simulate the turbulent diffusion characteristics of the droplets; calculated using Newton's second law: Where m p is the mass of the droplet, u p,i is the velocity of the droplet in the i direction; F g,i is gravity, F d,i is the resistance, F b,i It is Brownian force, F s,i is the Saffman lift, ρ i and ρ are the density of the droplet and the air, respectively, f D is the Stokes drag correction function for the Reynolds number, τ p is the characteristic response time.

6. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 5 is characterized in that: The tracer gas is CO2.

7. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: The specific steps to set crowd motion parameters are: The motion of the object is simulated using the dynamic mesh model, specifically using the smoothing method and mesh reconstruction method in the dynamic mesh model to perform dynamic mesh simulation; The springs in the ANSYS Fluent system used are approximated to be smooth; the nodes are treated as ideal springs, and the node positions are updated by calculating the balance of the spring forces. The calculation formula is: in and is the displacement of node i and its adjacent node j; n i is the number of nodes connected to node i; k ij is the spring stiffness, defined as: where k fac It is a configurable Spring Constant Factor parameter. When the node is in equilibrium, the spring force acting on the node should be 0, and the iterative relationship for position calculation is obtained based on this. There are three parameters that need to be set when calculating the spring smoothing method: spring stiffness coefficient, convergence accuracy, and number of iterations. The value range of the spring stiffness coefficient is 0 to 1. Dynamic mesh reconstruction is achieved by jointly reconstructing the internal mesh cells and the triangular surface mesh on the deformed boundary. For a scalar φ in the moving control volume V, the integral form of the moving mesh conservation equation is: Where ρ is the fluid density, is the flow velocity vector, is the grid velocity of the moving grid, Γ is the diffusion coefficient, S φ is the source term of φ; It is used to represent the boundary of the control volume V. In the numerical calculation process, the DEFINE_CG_MOTION macro used to define rigid body motion in the ANSYS Fluent system is called by writing a user-defined function (UDF) to realize the interactive motion of people at different speeds and directions. Due to the complex surface structure of the human body, negative volume grids are easily generated when simulating the motion of people. Therefore, in the pre-processing, ANSYS ICEM is used to generate a tetrahedral grid with strong adaptability, and the grid around the human body is encrypted, and 6 prism layers are added to the boundary. In the calculation process, the boundary layer grid on the surface of the human body is fixed so that this part of the grid is not updated during the interactive motion of people, so as to reduce the degradation of the grid quality during the motion and improve the calculation accuracy.

8. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1 is characterized in that: The specific steps for numerical simulation of the spread of infectious agents are as follows: Conduct numerical simulations to study in detail the impact of factors such as the number of people doing interactive exercise, exercise speed, distribution of people's positions, droplet size, and ventilation on the airflow of multiple people doing exercise, the diffusion and concentration distribution of droplets exhaled by patients, and conduct regional dynamic risk assessments to provide scientific basis and technical support for the formulation of prevention and control strategies for respiratory infectious diseases in crowded places; The RNG k-ε turbulence model and SIMPLE algorithm are used to calculate the indoor flow field of multi-person interactive sports scenes, using a second-order headwind format. The Boussinesq model is used to simulate the buoyancy effect, and the air density and ambient relative humidity are set. Multiple sets of numerical simulation conditions are designed to deeply study the influence of related factors such as the number of people exercising, speed, distribution of personnel positions, ventilation, and droplet particle size. In addition, multiple monitoring points are set to record the transient airflow velocity.

9. The method for assessing the risk of respiratory infectious diseases spread based on multi-person dynamic scenarios according to claim 1, characterized in that: The specific steps for calculating the exposure level of susceptible personnel are: According to the results of the corresponding infectious substance diffusion analysis, the temporal and spatial distribution evolution law of the infectious substance concentration is obtained, and then the temporal and spatial evolution characteristics of the exposure level of the susceptible population are obtained; By setting up multiple real-time monitoring points, the temporal and spatial distribution evolution of respiratory infectious substances in multi-person dynamic scenes was obtained. For personal risk assessment, the infection probability P of susceptible people depends on the number of inhaled infection quanta N. quanta : Assuming that the virus is evenly distributed in the patient's exhaled gas, the relative concentration C r and infection quantum generation rate Q IP Calculate the infection probability of a susceptible person: Here C a is the mass fraction of CO2 in the patient's exhaled gas (4%), C is the mass fraction of CO2 in the susceptible person's inhaled gas; the inhalation ratio IF is: Here M in Usually it can be set to 30L / min (susceptible people), M ex It can usually be set to 10L / min (patient), after exposure time T: Then the infection probability P of susceptible people is: For regional risk assessment, different height planes are considered to represent the breathing height planes of standing and sitting people respectively; the planes are divided into areas of corresponding areas, and the average concentration of infectious substances in each area is calculated, which is equivalent to C in the individual risk assessment method. Substituting it into the above formula, the exposure and risk of each area can be calculated.

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