Indoor air purification method

Abstract

The invention provides an indoor air purification method, which comprises the following steps of: comparing and analyzing airflow organization characteristics and particulate matter distribution conditions under different ventilation strategies, evaluating the effectiveness of various ventilation modes for controlling infection risks in a hospital, and screening out a target ventilation system design scheme, so that the target ventilation system design scheme meets the requirements of hospital air quality standards; on the basis of the screened target ventilation system design scheme, the arrangement of an air supply outlet, the air speed and the temperature are further optimized, and a large vortex simulation technology is adopted to conduct refined simulation on the airflow turbulence characteristics of a local area so as to verify the stability and reliability of the ventilation effect; and comprehensively evaluating the influence of the optimized ventilation system design scheme on the overall energy consumption of the hospital, carrying out economic analysis, balancing the energy-saving benefit and the sensing control effect, finally determining an enforceable airflow organization optimization strategy and supporting measures, and providing a customized sensing control solution for the hospital.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an indoor air purification method. Background Art

[0002] The design of airflow organization in complex spaces of hospitals faces multiple technical contradictions and challenges. First, the requirements for cleanliness levels in different functional areas vary greatly. For example, operating rooms require high cleanliness, while general wards and waiting areas have relatively low cleanliness requirements. How to achieve the optimization of airflow organization in the entire space while meeting the cleanliness requirements of each area is a major problem. Secondly, the movement trajectories of particles of different particle sizes in the airflow vary greatly. Large particles are easy to settle under the action of gravity, while small particles are more likely to drift and diffuse with the airflow. A single ventilation method is difficult to control the movement of particles of various particle sizes at the same time. Furthermore, in addition to being affected by the ventilation method, airflow organization is also closely related to many parameters such as wind speed, temperature gradient, humidity, etc. Parameter changes will significantly change the movement trajectory of particles, but the parameters are mutually influenced and restricted, and it is difficult to find the optimal parameter combination. In addition, the clean airflow barrier surrounding the operating area must be complete and stable, but the frequent entry and exit of surgical personnel will disturb the airflow field and destroy the integrity of the barrier. In summary, the design of airflow organization in complex spaces of hospitals must seek the best balance between meeting functional requirements, controlling particle movement, optimizing multiple parameters, and maintaining airflow stability. It is necessary to conduct in-depth research on airflow patterns under different design conditions to guide the optimal design of the ventilation system. Summary of the invention

[0003] The present invention provides an indoor air purification method, which mainly comprises:

[0004] Obtain the hospital's spatial layout and functional zoning information, and use 3D modeling software to construct geometric models of each functional area of ​​the hospital;

[0005] According to the geometric models and equipment parameters of each functional area of ​​the hospital, the model is meshed in the computational fluid dynamics software, boundary conditions and initial conditions are set, and a numerical model of the airflow organization of the hospital space is constructed;

[0006] According to different ventilation methods, set the air outlet position, wind speed and temperature, simulate and analyze the air flow speed, temperature and humidity distribution, and evaluate the ventilation effect;

[0007] The particle size distribution characteristics of common hospital pathogens and surgical particles were obtained, and the Lagrangian particle tracking method was used to calculate the movement trajectory and deposition distribution of particles under different ventilation strategies;

[0008] By comparing and analyzing the airflow organization characteristics and particle distribution of different ventilation strategies, the infection control effect is evaluated and the optimal ventilation system design is selected;

[0009] Based on the target design plan, large eddy simulation technology is used to conduct detailed simulation of local areas to verify the stability and reliability of the ventilation effect and form a customized hospital infection control solution.

[0010] Furthermore, the construction of the numerical model of hospital space airflow organization includes:

[0011] Structured hexahedral grids are used to divide regular areas, and unstructured tetrahedral grids are used to divide complex areas. Walls, inlets and outlets are defined at the model boundaries, and the initial temperature field, velocity field and pressure field are set. The standard k-ε turbulence model is used to describe the air flow characteristics, and the energy equation and component transport equation are set as control equations, and the finite volume method is used for discrete solution.

[0012] Furthermore, the simulation analysis of air flow velocity, temperature and humidity distribution includes:

[0013] For laminar ventilation and mixed ventilation, different air outlet layout schemes are set, and the air outlet area, wind speed and temperature range are adjusted; the standard k-ε turbulence model or RNGk-ε turbulence model is used to solve the airflow field and obtain the velocity, temperature and humidity distribution data; the airflow velocity cloud map, temperature contour map and humidity distribution map of cross-sections at different heights are drawn, the average wind speed, temperature uniformity index and relative humidity standard deviation of each area are calculated, and the weighted scoring method is used to calculate the comprehensive score to screen the optimal ventilation strategy.

[0014] Furthermore, the Lagrangian particle tracking method is used to calculate the movement trajectory and deposition distribution of particles under different ventilation strategies, including:

[0015] According to the particle size distribution data of pathogens and surgical particles, the maximum likelihood estimation method was used to fit the lognormal distribution function and determine the average particle size and standard deviation of the particles. The initial release point of the particles was set, the particle tracking solver parameters were configured, the Lagrangian discrete phase method was selected, and the gravity sedimentation module was enabled. Particle tracking simulation was performed for different ventilation schemes, and the spatial concentration distribution was calculated using the grid division method. The average suspension time, sedimentation rate, and spatial distribution differences of particles in each area were statistically analyzed.

[0016] Furthermore, the infection control effects of different ventilation strategies are evaluated by comparing and analyzing the airflow organization characteristics and particle distribution of different ventilation strategies, including:

[0017] The average wind speed, temperature uniformity index, relative humidity standard deviation, particle concentration, sedimentation rate and residence time in each area were calculated. The weight of each indicator was determined by using the analytic hierarchy process, and the eigenvector was calculated and consistency test was performed by constructing a judgment matrix. The thresholds of each indicator were set according to the hospital air quality standards, and the candidate solutions that met the requirements were screened, the comprehensive scores were calculated, and the solution with the highest score was selected as the optimal design solution.

[0018] Furthermore, the use of large eddy simulation technology to perform a refined simulation of the local area includes:

[0019] The wind speed, temperature and angle parameters of the target design are used as boundary conditions to define a fine computational grid in the key area;

[0020] Specify a sub-grid turbulence model and perform transient large eddy simulation to obtain turbulent pulsation data; analyze turbulence intensity, turbulent kinetic energy and Reynolds stress to determine the fluctuation characteristics and stability of the airflow field; if there is a stagnation zone in a local area or the turbulence intensity exceeds the standard, adjust the ventilation parameters until the design requirements are met.

[0021] Furthermore, the formation of a customized hospital infection control solution includes:

[0022] Establish an overall energy consumption model for the hospital, use EnergyPlus software to simulate dynamic energy consumption throughout the year, and evaluate the energy-saving benefits of the ventilation optimization solution; consider the initial investment cost and operation and maintenance costs to determine the economic feasibility; weigh the energy-saving benefits and sensor control effects, establish a dual-objective optimization model, and use the NSGA-II algorithm to solve and obtain a balanced solution; formulate an airflow organization optimization strategy, including fresh air unit selection, frequency conversion control and sensor layout, to form an overall solution covering equipment selection, control strategy and operation management.

[0023] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0024] The present invention discloses a method for indoor air purification. Aiming at the complex spatial layout and infection control requirements of hospitals, a three-dimensional CFD numerical model is constructed to simulate the airflow distribution and particle movement under different ventilation strategies. The propagation trajectory of pathogens and surgical particles in space is analyzed by the Lagrangian particle tracking method. Parameter research and large eddy simulation technology are used to optimize the layout, wind speed and temperature of air outlets, identify high-risk areas and take targeted measures. The present invention comprehensively considers the infection control effect and energy consumption balance, provides hospitals with customized airflow organization solutions, effectively reduces the risk of nosocomial infection, and takes into account energy-saving needs. This method significantly improves the scientificity and effectiveness of hospital ventilation system design through refined simulation and multi-faceted optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flow chart of an indoor air purification method.

[0026] Figure 2 It is a schematic diagram of an indoor air purification method of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1-2 In this embodiment, an indoor air purification method may specifically include:

[0029] Step S101, according to the layout and functional zoning of the hospital space plane, three-dimensional modeling of different areas is carried out using computational fluid dynamics software, and boundary conditions and initial conditions are set to construct a CFD numerical model of the hospital space airflow organization. The numerical model is constructed to collect spatial geometric parameters, vent positions and air volume, heat source positions and heat source power.

[0030] The hospital space plan layout and functional zoning information are obtained, and the geometric models of each functional area of ​​the hospital are constructed using three-dimensional modeling software; according to the geometric models of each functional area of ​​the hospital and the equipment parameters, the model is meshed in the computational fluid dynamics software, wherein structured hexahedral meshes are used to divide regular areas, and unstructured tetrahedral meshes are used to divide complex areas; the wall, inlet and outlet are defined at the model boundary, the initial temperature field, velocity field and pressure field are set, and the mesh model is imported into the solver; the standard k-ε turbulence model is used to describe the air flow characteristics, and the control equation is set, the control equation includes the energy equation and the component transport equation, and the finite volume method is used to discretize the control equation; the calculation result data is exported, and a distribution cloud map is drawn, the distribution cloud map includes the temperature field, velocity field and pressure field in the space, the airflow organization characteristics of different functional areas are analyzed, and the average temperature and average wind speed of the key areas are calculated using the volume weighted average method.

[0031] Specifically, the floor plan and functional zoning information of the hospital space were obtained, and the geometric model of each functional area of ​​the hospital was constructed using 3D modeling software. The actual positions of equipment such as vents and heat sources were marked in the model, and the air supply speed, direction and heating power data of each vent were recorded. According to the 3D geometric model and equipment parameters, the structured hexahedral grid was used to divide the regular area, and the unstructured tetrahedral grid was used to divide the complex area in the computational fluid dynamics software. The boundary conditions such as the wall, inlet and outlet were defined at the model boundary, and the initial conditions such as the initial temperature field, velocity field and pressure field were set. The grid model was imported into the solver, and the standard k-ε turbulence model was used to describe the air flow characteristics. The control equations such as the energy equation and the component transport equation were set. The control equations were discretized using the finite volume method. The SIMPLE algorithm was selected to solve the coupling of the pressure field and the velocity field, and the discrete equation group was iteratively solved. The residual convergence criterion was set to 1e-4, and the residual curve during the solution process was monitored. When the residuals of all variables were less than the set value, the solution converged. Export the calculation result data, draw the distribution cloud map of temperature field, velocity field and pressure field in the space, analyze the airflow organization characteristics of different functional areas, use the volume weighted average method to calculate the average temperature and average wind speed of key areas, and use the room volume and air supply volume to calculate the ventilation times and other indicators. When constructing the hospital space geometry model, use AutoCAD software to import the floor plan, convert the two-dimensional plane into three-dimensional space through the stretch command, and set the floor height to 3 meters. When marking the position of the vents, use the coordinate positioning method, such as the coordinates of the air outlet are (2.5, 3.0, 2.8), and the coordinates of the return air outlet are (5.0, 4.5, 0.3). When recording the air supply parameters, the measured air supply speed is 2.5m / s and the air supply angle is 45°. The heat source power data is obtained by consulting the equipment nameplate, such as the heating power of the air conditioner outdoor unit is 2000W. During the meshing process, structured hexahedral meshes are used for regular areas such as wards, with a mesh size of 0.1m; unstructured tetrahedral meshes are used for complex areas such as corridor corners, with a minimum mesh size of 0.05m. When setting boundary conditions, the wall is defined as a no-slip wall, the air supply vent is defined as a velocity inlet, and the return air vent is defined as a pressure outlet. The initial conditions are set as: temperature 20°C, velocity 0m / s, and pressure 101325Pa. During the solution process, the standard k-ε turbulence model is selected, and the initial value of the turbulent kinetic energy k is set to 0.001m 2 / s 2 , the initial value of the turbulent dissipation rate ε is set to 0.0001m 2 / s 3. Heat conduction and convection heat transfer are considered in the energy equation, and the component transport equation includes the CO2 concentration field. The SIMPLE algorithm is used to solve the pressure-velocity coupling, and the pressure correction relaxation factor is set to 0.3, and the velocity relaxation factor is set to 0.7. The mass, momentum and energy residuals are monitored during the iteration process, and convergence is determined when the residuals are all less than 1e-4. In the result analysis stage, ParaView software is used to draw temperature field, velocity field and pressure field cloud maps. The temperature field range is 18-28℃, the velocity field range is 0-3m / s, and the pressure field range is -10 to 10Pa. When calculating the average temperature, the room is divided into multiple small volume units. The temperature of each unit is multiplied by its volume, the sum is then divided by the total volume. The average wind speed is calculated in a similar way. The ventilation rate is obtained by dividing the room volume by the air supply volume. For example, the volume of a ward is 60m 3 , air supply volume is 300m 3 / h, the ventilation rate is 5 times / h.

[0032] Step S102, based on the constructed CFD numerical model of hospital space airflow organization, several ventilation modes are set in the numerical model for operating rooms and open nursing stations, including laminar ventilation and mixed ventilation. By adjusting the position, wind speed and temperature of the air supply outlet, the air flow velocity, temperature gradient and humidity distribution under different ventilation strategies are simulated.

[0033] The positions of the air supply vents and the return air vents are marked according to the three-dimensional geometric model, and multiple air supply vent position combinations are set on the wall through parametric modeling; corresponding boundary conditions are set for the two ventilation modes of laminar ventilation and mixed ventilation, and the boundary conditions include the area, wind speed range and temperature range of the air supply vents; the boundary conditions are used to solve the airflow field through the standard k-ε turbulence model or the RNGk-ε turbulence model to obtain the airflow velocity, temperature and humidity distribution data; the airflow velocity cloud map, temperature contour map and humidity distribution map of the cross-sections at different heights are drawn using the distribution data, and the airflow velocity cloud map, temperature contour map and humidity distribution map are used to intuitively display the ventilation effect; the average wind speed, temperature uniformity index and relative humidity standard deviation of each area are calculated according to the airflow velocity cloud map, temperature contour map and humidity distribution map, and the weighted scoring method is used to calculate the comprehensive score of each ventilation strategy; if the comprehensive score is the highest, the ventilation strategy is determined to be the optimal ventilation strategy, and its corresponding air supply vent position, wind speed and temperature parameters are obtained.

[0034] Specifically, the three-dimensional geometric model of the operating room and the open nursing station was imported, the positions of the air supply and return air outlets were marked in the numerical model, and multiple possible air supply outlet position combinations were set on the wall through parametric modeling. The boundary conditions of the two ventilation modes, laminar ventilation and mixed ventilation, were set, including the area, wind speed range, and temperature range of the air supply outlet. The initial relative humidity condition was set to 50%, and the supply humidity was set to 40%. For the laminar ventilation mode, a large-area air supply outlet was set on the ceiling, the wind speed range was set to 0.2-0.3m / s, and the temperature range was set to 19-21℃. The standard k-ε turbulence model was used to solve the airflow field to obtain the airflow velocity, temperature, and humidity distribution data. After completing the laminar ventilation simulation, the air supply outlet settings in the model were modified, and the large-area air supply outlet on the ceiling was changed to multiple small-area air supply outlets on the wall. For the mixed ventilation mode, multiple small-area air supply outlets were set on the wall, the wind speed range was set to 0.4-0.6m / s, and the temperature range was set to 17-19℃. The RNGk-ε turbulence model was used to solve the airflow field and obtain the airflow velocity, temperature and humidity distribution data. Multiple simulations were performed for different air outlet position combinations to compare the effects of different air outlet position combinations. The airflow velocity cloud map, temperature contour map and humidity distribution map of the cross-section at different heights were drawn using the numerical simulation results. The average wind speed, temperature uniformity index and relative humidity standard deviation of each area were calculated. Taking these indicators into consideration, the weighted scoring method was used to calculate the comprehensive score of each ventilation strategy. According to the ranking of the comprehensive score, the optimal ventilation strategy and its corresponding air outlet position, wind speed and temperature parameters were determined. The geometric models of the operating room with a size of 8m×6m×3m and the open nursing station with a size of 10m×8m×3m were imported into the three-dimensional modeling software, and 5×4 grid points were set on the wall as potential air outlet locations through parametric modeling. For laminar ventilation, a large-area air outlet of 4m×3m was set in the center of the ceiling; for mixed ventilation, 3 to 5 small-area air outlets of 0.5m×0.5m were selected on the wall. The initial relative humidity was set to 50% and the supply air humidity was 40%. Computational fluid dynamics software was used for grid division, and the number of grids was about 1 million. For laminar ventilation, the wind speed varied between 0.2-0.3m / s with a step size of 0.02m / s, and the temperature varied between 19-21℃ with a step size of 0.5℃; for mixed ventilation, the wind speed varied between 0.4-0.6m / s with a step size of 0.04m / s, and the temperature varied between 17-19℃ with a step size of 0.5℃. The standard k-ε and RNGk-ε turbulence models were used to solve the laminar and mixed ventilation conditions respectively. The number of iterations was set to 1000, and the residual convergence criterion was 1e-4. For each ventilation mode, 25 different combinations of supply air parameters were generated for simulation. Post-processing software was used to draw the air flow velocity cloud map at a height of 1.5 m, ranging from 0 to 0.5 m / s, the temperature contour map, with an interval of 0.2°C, and the relative humidity distribution map, ranging from 30 to 60%.Calculate the average wind speed, temperature uniformity index, standard deviation divided by average temperature, and relative humidity standard deviation for each area. Use weighted scoring method to score each ventilation strategy, with weights of 30% for average wind speed, 40% for temperature uniformity, and 30% for humidity standard deviation. According to the comprehensive score ranking, select the ventilation strategy with the highest score as the optimal solution, and determine its corresponding air outlet location, wind speed, and temperature parameters.

[0035] Step S103, obtain the particle size distribution characteristics of common bacteria, viruses and surgical particles in the hospital, combine the simulation of different ventilation strategies, set the initial position, particle size and density of the particles in the CFD numerical model, and use the Lagrangian particle tracking method to calculate the movement trajectory and sedimentation distribution of the particles in space under different ventilation modes.

[0036] The particle size distribution characteristics of common bacteria, viruses and surgical particles in hospitals are obtained, and the particle size distribution curves of different types of particles are fitted using the log-normal distribution function to determine the average particle size and standard deviation of the particles. According to the average particle size and standard deviation of the particles, the airflow velocity field, temperature field and humidity field data are extracted in the CFD numerical model. The airflow velocity field, temperature field and humidity field data are obtained from the simulation results of two strategies: laminar ventilation and mixed ventilation. The Lagrangian discrete phase model is used to set the coupling force between the particles and the airflow, and the coupling force includes drag force, gravity and Brownian force; the particle motion equation is solved by the fourth-order Runge-Kutta method, and the position and velocity changes of the particles in each time step are calculated. The movement trajectory of the particles in space is counted, and the number of collisions between the particles and the wall, the ground and the equipment surface is recorded; if the particles collide with the wall, the ground and the equipment surface, it is determined whether the particles have settled. The particle concentration distribution and sedimentation rate in different areas are calculated, and the particle concentration distribution and sedimentation rate are obtained by the movement trajectory and collision number of the particles; the average suspension time, sedimentation rate and spatial distribution difference of the particles under laminar ventilation and mixed ventilation are compared to determine the particle removal efficiency of each area.

[0037] Specifically, the particle size distribution characteristics of common bacteria, viruses and surgical particles in hospitals are obtained from the medical database, and the log-normal distribution function is used to fit the particle size distribution curves of different types of particles to determine the average particle size and standard deviation of the particles. The density of different types of particles is determined by the table lookup method, such as the density of bacteria is 1.1g / cm 3 , the virus density is 1.3g / cm 3. The density parameters were set according to the physical properties of the particles. In the CFD numerical model, the airflow velocity field, temperature field and humidity field data were extracted according to the simulation results of the two strategies of laminar ventilation and mixed ventilation. The key positions of the operating area and the nursing station were selected as the initial release points of the particles, and the initial velocity of the particles was set to be the same as the surrounding airflow velocity. According to the actual pollution source intensity, the initial number of particles at each release point was set to 1000. The Lagrangian discrete phase model was used to set the coupling forces between the particles and the airflow, including drag force, gravity and Brownian force. The particle motion equation was solved by the fourth-order Runge-Kutta method, and the time step was set to 0.01 seconds. The position and velocity changes of the particles in each time step were calculated. Considering the collision and agglomeration effects between particles, the hard sphere collision model was used to simulate the interaction between particles. The motion trajectory of particles in space was counted, and the number of collisions between particles and walls, ground and equipment surfaces was recorded. The particle concentration distribution and sedimentation rate in different areas were calculated, and the particle concentration cloud map and sedimentation distribution heat map were drawn using Tecplot software. The average suspension time, sedimentation rate and spatial distribution of particles under laminar ventilation and mixed ventilation were compared, and the particle removal efficiency of each area was calculated. The particle size data of common hospital bacteria, such as Staphylococcus aureus, viruses, such as influenza virus, and surgical particles were extracted from the PubMed database. The maximum likelihood estimation method was used to fit the log-normal distribution function, and the average particle size of Staphylococcus aureus was 1 μm, with a standard deviation of 0.3 μm; the average particle size of influenza virus was 0.1 μm, with a standard deviation of 0.02 μm. The bacterial density of 1.1 g / cm was obtained by looking up the table. 3 , virus density 1.3g / cm 3 . The airflow field data of laminar ventilation, top air supply, wind speed 0.25m / s, and mixed ventilation, side wall air supply, wind speed 0.5m / s were imported into ANSYS Fluent. The center of the operating table and the nursing station workbench were selected as the release points, and 1000 particles were released at each point. The discrete random walk model was used to simulate the movement of particles, considering the Schiller-Naumann drag force model, gravity (9.81m / s 2 ) and Cunningham-slip-factor modified Brownian force. The fourth-order Runge-Kutta method was used to solve the equation of motion, with a time step of 0.01s and a total simulation time of 300s. The particle collision used an elastic sphere model with a restitution coefficient of 0.9. The particle trajectories were recorded and the number of collisions with the solid surface was counted. The spatial concentration distribution was calculated using the grid division method with a grid size of 0.1m×0.1m×0.1m. The concentration cloud map was drawn in Tecplot with a range of 0-100 / m 3, and the sedimentation thermodynamic diagram. Comparing the two ventilation modes, the average suspension time of particles under laminar ventilation is 150s, and the sedimentation rate is 80%; the average suspension time under mixed ventilation is 200s, and the sedimentation rate is 70%. Calculate the particle removal efficiency: (initial concentration-final concentration) / initial concentration×100%, the laminar ventilation efficiency is 85%, and the mixed ventilation efficiency is 75%.

[0038] The particle size distribution, typical concentration and density data of common pathogens and surgical particles in hospitals were collected, and the particle tracking model was enabled in the CFD software. The particle release properties were defined, including setting the initial position of the particles at specific locations, including the air inlet and the surgical site, inputting the particle size distribution and density, configuring the settings of the particle tracking solver, enabling the modeling options of gravity sedimentation and the interaction effects with airflow turbulence, and performing particle simulation for different ventilation schemes.

[0039] The particle size distribution data of pathogens and surgical particles were obtained from the medical literature database, and the lognormal distribution function of the particle size distribution data was fitted using the maximum likelihood estimation method to determine the average particle size and standard deviation of the particles. According to the average particle size and standard deviation of the particles, the three-dimensional geometric model of the operating room was imported into the CFD software, and the specific coordinate positions of the air inlet and the surgical site were marked according to the geometric model, and the initial release point of the particles was set. According to the initial release point, the particle tracking solver parameters were configured, the Lagrangian discrete phase method was selected, the gravity sedimentation module was enabled and the gravity acceleration was set. If the laminar ventilation scheme is selected, the laminar ventilation parameters are used for particle tracking simulation; if the mixed ventilation scheme is selected, the mixed ventilation parameters are used for particle tracking simulation. According to the simulation results of the laminar ventilation scheme and the mixed ventilation scheme, the spatial concentration distribution is calculated using the grid division method. The grid division method divides the space into a number of grid units, calculates the particle concentration in each grid unit, and obtains the spatial concentration distribution.

[0040] Specifically, the particle size distribution data of common hospital pathogens and surgical particles were extracted from the medical literature database, and the log-normal distribution function was fitted using the maximum likelihood estimation method to determine the average particle size and standard deviation of the particles. The density value of the particles was obtained by querying the physical property database. Typical concentration data of pathogens and surgical particles in the hospital environment were collected, such as the bacterial concentration in the operating room air was 100 CFU / m 3. Import the 3D geometric model of the operating room into the CFD software, and mark the specific coordinate positions of the air inlet and the surgical site. Enable the particle tracking function in the discrete phase model, and set the initial release points of the particles at the air inlet and the surgical site. Enter the particle size distribution parameters and density values ​​of the particles. According to the typical concentration data collected, set the initial release quantity of the particles, such as releasing 100 particles per second at the air inlet. Configure the particle tracking solver parameters and select the Lagrangian discrete phase method. Enable the gravity sedimentation module and set the gravity acceleration to 9.81m / s 2 . The random walk model was turned on to simulate the interaction between particles and turbulence, and the time scale constant of the random walk model was set to 0.15. The interaction force between particles and airflow was set, including the consideration of the Schiller-Naumann drag force model. The collision type between particles and walls was set to rebound, and the rebound coefficient was 0.9. Particle tracking simulations were performed for laminar ventilation and mixed ventilation schemes respectively. The simulation time step was set to 0.01 seconds, and the total simulation time was 300 seconds. The solver was run to calculate the particle trajectory, and the position and velocity of the particles in space were recorded over time. The spatial concentration distribution was calculated using the grid division method, and the grid size was set to 0.1m×0.1m×0.1m. The distribution characteristics and sedimentation of particles under different ventilation schemes were compared. The PubMed database was used to retrieve the data of common hospital pathogens and surgical particles, and the particle size distribution of Staphylococcus aureus was extracted. The log-normal distribution function was fitted by the maximum likelihood estimation method, and the average particle size was 1μm and the standard deviation was 0.3μm. Query the physical properties database to obtain its density as 1.1g / cm 3 The typical concentration of bacteria in the operating room air is 100 CFU / m 3 Import the 10m×8m×3m 3D model of the operating room into ANSYS Fluent, mark the ceiling air outlet (2m×2m) at the center coordinate (5,4,3), and the operating table at (5,4,0.8). Enable the discrete phase model, set the particle release at the air outlet, and release 100 particles per second. Configure the Lagrangian discrete phase method and set the gravity acceleration to 9.81m / s 2 , the random walk model is enabled, and the time scale constant is 0.15. The Schiller-Naumann drag force model is used to calculate the force between particles and airflow, and the rebound coefficient of particle-wall collision is 0.9. Laminar ventilation, air supply speed 0.25m / s, and mixed ventilation, air supply speed 0.5m / s, are simulated respectively, with a time step of 0.01s and a total time of 300s. The particle trajectory is recorded using a user-defined function (UDF), and the space is divided into a grid of 0.1m×0.1m×0.1m. The number of particles in each grid is counted to calculate the concentration distribution. The results show that the average suspension time of particles under laminar ventilation is 150s and the sedimentation rate is 80%; the average suspension time under mixed ventilation is 200s and the sedimentation rate is 70%.

[0041] Step S104, by comparing and analyzing the airflow organization characteristics and particle distribution under different ventilation strategies, the effectiveness of various ventilation methods in controlling the risk of hospital infection is evaluated, and the target ventilation system design scheme is screened out to meet the requirements of the hospital air quality standards.

[0042] The airflow velocity field, temperature field and humidity field data in the CFD simulation results are obtained, and the average wind speed, temperature uniformity index and relative humidity standard deviation of each area are calculated based on the data; wherein the temperature uniformity index is obtained by dividing the standard deviation by the average temperature. The distribution of particles in the space is statistically analyzed, and the particle concentration, sedimentation rate and residence time of each area are calculated; wherein the particle residence time is determined by tracking the time from the release of a single particle to the sedimentation or leaving the space. An evaluation index system is established, and the evaluation index system includes airflow uniformity, temperature and humidity control effect, particle removal efficiency and energy consumption level; wherein the energy consumption level is determined by calculating the power consumption of the blower per unit time. The weight of each indicator is determined by the hierarchical analysis method, and the hierarchical analysis method includes constructing a judgment matrix, calculating the eigenvector, and performing a consistency test; the comprehensive score of different ventilation strategies is calculated based on the weight of each indicator. According to the hospital air quality standard requirements, the thresholds of each evaluation indicator are set, and the thresholds include airflow velocity and particle concentration; if the ventilation system design scheme meets the threshold requirements, it is screened as a candidate scheme; the scheme with the highest comprehensive score is selected from the candidate schemes as the target design scheme.

[0043] Specifically, the airflow velocity field, temperature field and humidity field data under different ventilation strategies were extracted from the CFD simulation results, and the average wind speed, temperature uniformity index and relative humidity standard deviation of each area were calculated. The temperature uniformity index was calculated by dividing the standard deviation by the average temperature. The airflow velocity vector diagram and temperature and humidity contour map were drawn to intuitively display the characteristics of airflow organization. The distribution of particles in space was statistically analyzed, and the particle concentration, sedimentation rate and residence time of each area were calculated. The particle residence time was calculated by tracking the time from the release to the sedimentation or leaving the space of a single particle. The particle distribution thermodynamic map and trajectory map were drawn to compare and analyze the diffusion and sedimentation characteristics of particles under different ventilation strategies. Based on the particle concentration and residence time, the hospital infection risk index was established to directly evaluate the prevention and control effects of different ventilation strategies. An evaluation index system was established, including airflow uniformity, temperature and humidity control effect, particle removal efficiency and energy consumption level. The energy consumption level was evaluated by calculating the power consumption of the fan per unit time. The hierarchical analysis method was used to determine the weight of each indicator. The specific steps included constructing a judgment matrix, calculating the eigenvector, and performing a consistency test. Calculate the comprehensive scores of different ventilation strategies. According to the hospital air quality standard requirements, set the thresholds of various evaluation indicators, such as air flow velocity less than 0.25m / s and particle concentration less than 100CFU / m 3 . Screen out ventilation system design schemes that meet the threshold requirements, sort them by comprehensive scores, and select the scheme with the highest score as the target design scheme. Combine the comprehensive score with the threshold requirements to ensure that the selected scheme meets the basic standards and has optimal performance. Extract the simulation results of laminar ventilation and mixed ventilation strategies in ANSYS Fluent, and obtain the airflow velocity field, temperature field and humidity field data of the 1.5m height plane. The average wind speed under laminar ventilation is calculated to be 0.22m / s, the temperature uniformity index is 0.015, and the relative humidity standard deviation is 2.5%; the average wind speed under mixed ventilation is 0.35m / s, the temperature uniformity index is 0.025, and the relative humidity standard deviation is 3.8%. Use Tecplot software to draw airflow velocity vector diagrams and temperature and humidity contour diagrams. Particle matter analysis shows that the average concentration under laminar ventilation is 50CFU / m 3 , sedimentation rate 80%, average residence time 150s; average concentration under mixed ventilation is 75CFU / m 3, sedimentation rate 70%, average residence time 200s. The product of particle concentration and residence time is used as the risk index of hospital infection. The risk index of laminar flow ventilation is 7500, and that of mixed ventilation is 15000. An evaluation index system is constructed, including airflow uniformity, temperature and humidity control effect, particle removal efficiency and energy consumption level. The weights are calculated by analytic hierarchy process, a 4x4 judgment matrix is ​​constructed, and the eigenvector is calculated. The consistency ratio CR=0.05<0.1 passes the consistency test. The energy consumption calculation shows that the laminar flow ventilation power is 2kW and the mixed ventilation power is 3kW. Comprehensive scoring results: 85 points for laminar flow ventilation and 78 points for mixed ventilation. According to the "Technical Specifications for Hospital Clean Operating Room Construction", the threshold is set: airflow velocity

[0044] <0.25m / s, particle concentration <100CFU / m 3 The laminar flow ventilation scheme met the requirements and obtained the highest comprehensive score, and was selected as the target design scheme.

[0045] Step S105, based on the selected target ventilation system design, further optimize the air outlet layout, wind speed and temperature, and use large eddy simulation technology to perform a refined simulation of the airflow turbulence characteristics in the local area to verify the stability and reliability of the ventilation effect.

[0046] According to the position and size parameters of the air outlet, different geometric models are automatically generated by using the parameterization function of ANSYS Design Modeler; for the geometric model, the L9 (3^4) orthogonal table is used to design the air outlet layout plan, and the position, size and number of the air outlet are adjusted to obtain the geometric model and grid in the CFD software; according to the geometric model and grid in the CFD software, the airflow organization characteristics and temperature distribution under different parameter combinations are calculated through CFD simulation, and the weighted sum of the airflow uniformity index and the temperature uniformity index is used as the optimization objective function to obtain the optimal wind speed and temperature parameters; the optimal wind speed and temperature parameters are applied to the boundary condition setting of large eddy simulation, and local key areas are selected, and the Smagorinsky-Lilly sub-grid scale model is used to perform high-precision grid division and refined simulation calculations; the large eddy simulation results are analyzed in time series, the turbulence intensity, turbulent kinetic energy and Reynolds stress of the local area are calculated, and it is judged whether the fluctuation characteristics and stability of the airflow field meet the design requirements.

[0047] Specifically, based on the design of the target ventilation system, the parameterization function of ANSYS Design Modeler is used to define the position and size of the air outlet as variables, and different geometric models are automatically generated through scripts. The air outlet layout scheme is designed using the L9 (3^4) orthogonal table, the position, size and number of the air outlet are adjusted, and the corresponding geometric model and grid are established in the CFD software. For each air outlet layout scheme, the range of wind speed and temperature is set, and the second-order polynomial response surface model is used for parameter optimization. The weighted sum of the airflow uniformity index and the temperature uniformity index is used as the optimization objective function, and the airflow organization characteristics and temperature distribution under different parameter combinations are calculated through CFD simulation to obtain the optimal wind speed and temperature parameters. The optimized optimal wind speed and temperature parameters are applied to the boundary condition setting of large eddy simulation. Local key areas, such as the area around the operating table or the nursing station work area, are selected, the large eddy simulation module is enabled in the CFD software, the Smagorinsky-Lilly subgrid scale model is used, the boundary layer grid is set, and high-precision grid division and refined simulation calculation are performed on the area. The results of large eddy simulation were analyzed in time series, and the parameters such as turbulence intensity, turbulence kinetic energy and Reynolds stress in the local area were calculated to evaluate the fluctuation characteristics and stability of the airflow field. 1000 sets of randomly disturbed boundary conditions were generated for large eddy simulation, and the distribution characteristics of key parameters were statistically analyzed to analyze the reliability of ventilation effects under different working conditions. Full-scale CFD simulation was performed on the optimized scheme to verify whether its performance under different working conditions met the design requirements. In ANSYS Design Modeler, the air outlet of the operating room model was parameterized, and the position variables and size variables (length l, width w) were defined. Nine air outlet layout schemes were designed using the L9 (3^4) orthogonal table, such as Scheme 1: (2m, 3m, 3m, 1.5m, 1m), Scheme 2: (4m, 3m, 3m, 2m, 1.5m). Hexahedral meshes were generated using ICEMCFD, and the number of meshes was about 1 million. In ANSYS Fluent, the wind speed range was set to 0.2-0.4m / s, the temperature range was 18-22℃, and the second-order polynomial response surface model was used for optimization. The objective function f = 0.6×airflow uniformity index + 0.4×temperature uniformity index. The optimal parameters were obtained through 100 iterations: air outlet position (3.5m, 3m, 3m), size 1.8m×1.2m, wind speed 0.28m / s, temperature 20.5℃. The optimization results were applied to large eddy simulation, and a 2m×2m×2m area around the operating table was selected, with a grid size of 0.01m and a total number of grids of 8 million. The Smagorinsky-Lil ly model was used, with a constant Cs = 0.1, a time step of 0.01s, and a total simulation time of 60s. The analysis results show that the average turbulence intensity is 5%, and the turbulent kinetic energy is 0.002m 2 / s 2 , Reynolds stress peak value 0.01m2 / s 2 . 1000 sets of boundary conditions were generated by Monte Carlo method, and the wind speed and temperature fluctuated within the range of ±5%. Statistical analysis showed that the air flow velocity fluctuation was <0.03m / s and the temperature fluctuation was <0.5℃ within the 95% confidence interval, which met the design requirements.

[0048] Taking the selected ventilation design scheme as the starting point, high-fidelity large eddy simulation is performed in key areas. High-fidelity large eddy simulation analyzes transient turbulence, defines the computational grid for the target area in the CFD software, specifies the preset sub-grid turbulence model and boundary conditions for the high-fidelity large eddy simulation, performs transient LES simulation and analyzes the results. Based on the analysis results, check whether there are stagnation areas or excessive turbulence intensity problems that affect pollutant transport. If so, make adjustments.

[0049] A preset ventilation design scheme is obtained, wherein the ventilation design scheme includes air outlet position, wind speed, temperature and angle parameters; for the ventilation design scheme, a plurality of different ventilation parameter combination schemes are constructed by changing the air outlet position, wind speed, temperature and angle parameters; a computational fluid dynamics method is used to simulate a plurality of ventilation parameter combination schemes to obtain ventilation effect data corresponding to each ventilation parameter combination scheme; an optimal ventilation parameter combination scheme is determined according to the ventilation effect data; for the optimal ventilation parameter combination scheme, a high-fidelity large eddy simulation is performed in a preset key area, wherein the high-fidelity large eddy simulation includes: defining a computational grid in a preset key area, specifying a preset sub-grid turbulence model and boundary conditions for the high-fidelity large eddy simulation, performing a transient large eddy simulation, and obtaining transient turbulence data; according to the transient turbulence data, determining whether there is a stagnation area or an area where the turbulence intensity exceeds a preset threshold in the preset key area; if so, adjusting the optimal ventilation parameter combination scheme to obtain an adjusted ventilation parameter combination scheme; if not, determining the optimal ventilation parameter combination scheme as the target ventilation design scheme.

[0050] Specifically, the selected ventilation design scheme is imported into the CFD software, and the variation range of the air outlet position, wind speed, temperature and angle is set by parametric modeling method. The parameter combination scheme is designed by L16 (4^5) orthogonal table, and steady-state RANS simulation is performed on each scheme to calculate the airflow velocity field and temperature field in the key area. According to the weighted sum of the airflow uniformity index and the temperature uniformity index, the first three groups of optimal parameter combinations are selected for subsequent analysis. For the selected parameter combination, the target area is meshed in the CFD software, and a structured hexahedral mesh is used. A mesh independence analysis is performed, and the simulation results of three mesh sizes of 0.02m, 0.01m and 0.005m are compared to select the balance point between calculation accuracy and calculation efficiency. A body-fitting mesh is used near the solid wall to ensure that y+<1 to accurately capture the boundary layer flow characteristics. A large eddy simulation module is set in the CFD software, the dynamic Smagorinsky subgrid model is selected, and the boundary conditions and initial conditions are defined. The time step was set to 0.01s and the total simulation time was 60s to perform transient LES simulation calculations. Tracer particles were added to the LES simulation to track their motion trajectory and analyze the diffusion and sedimentation characteristics of pollutants in different areas. The LES simulation results were post-processed and analyzed to calculate the time-averaged velocity field, turbulence intensity and vorticity distribution of the target area. Stagnation areas and high turbulence intensity areas were identified by drawing streamlines and contour maps. If the average velocity in a local area is less than 0.1m / s, it is defined as a stagnation area, and if the turbulence intensity is greater than 20%, it is defined as a high turbulence intensity area. If these problem areas exist, a response surface model is established based on the orthogonal test results, and the parameters are adjusted using the gradient descent method to perform a new round of optimization iterations. A 10m×8m×3m operating room model was imported into ANSYS Fluent, and the air outlet position (x:2-8m, y:1-7m, z:2.5-3m), wind speed of 0.1-0.5m / s, temperature of 18-24℃ and angle of 0-45° were parameterized. The L16 (4^5) orthogonal table was used to generate 16 sets of parameter combinations for RANS simulation using the RNGk-ε turbulence model. The airflow uniformity index, ranging from 0 to 1, and the temperature uniformity index, ranging from 0 to 1, with weights of 0.6 and 0.4 respectively, were calculated, and the top three groups with the highest weighted sum were selected. For these three sets of parameters, independence analysis was performed for grid sizes of 0.02m, 0.01m and 0.005m, respectively. The results showed that the 0.01m grid, about 2 million grids, was the best. 10 layers of body-fitting grids were set near the wall, with the first layer thickness of 0.0001m to ensure that y+<1. The large eddy simulation module was enabled, the dynamic Smagorinsky model was selected, and the initial value of the constant Cs was 0.1. Boundary conditions were set: inlet velocity 0.25m / s, temperature 20℃; outlet pressure 0Pa. Time step 0.01s, total time 60s. Release 1000 tracer particles with a diameter of 1 μm and a density of 1000 kg / m 3. Post-processing analysis showed that the time-averaged velocity field ranged from 0 to 0.3 m / s, the turbulence intensity was 1 to 25%, and the maximum vorticity was 200 s-1. Two stagnation regions with velocity < 0.1 m / s and one high turbulence region with intensity > 20% were identified. Based on 16 sets of orthogonal test results, a quadratic polynomial response surface model was established, and the gradient descent method was used for optimization to obtain new optimal parameters: air outlet position (5.5 m, 4 m, 2.8 m), wind speed 0.28 m / s, temperature 21.5 ° C, and angle 15 °.

[0051] Step S106, based on the simulation results of the particle movement trajectory, high-risk areas inside the operating room and nursing station are identified, and targeted airflow organization optimization measures are taken. The optimization measures include locally increasing the fresh air volume and adjusting the air supply angle, thereby reducing the residence time and sedimentation probability of particles in key areas.

[0052] The simulation results of the particle motion trajectory are obtained, and the distribution of particles in space is analyzed using the K-means clustering algorithm to calculate the particle concentration and residence time in the grid unit; the high-risk area is judged based on the particle concentration and residence time. If the particle concentration exceeds the preset concentration threshold and the residence time exceeds the preset time threshold, the grid unit is determined to be a high-risk area; for the high-risk area, the position, size, number, fresh air volume and air supply angle of the air outlet are set as variables using a parametric design tool to generate multiple groups of optimization schemes; computational fluid dynamics simulation is performed on the multiple groups of optimization schemes to obtain the air flow velocity, temperature distribution and particle concentration in the high-risk area; according to the weighted scores of the air flow velocity, temperature distribution and particle concentration, the scheme with the highest score is selected as the final optimization scheme; the final optimization scheme is applied to the overall model to perform a full-scale particle motion trajectory simulation, and calculate the residence time and sedimentation probability of particles in key areas.

[0053] Specifically, based on the simulation results of particle movement trajectories, the K-means clustering algorithm was used to analyze the distribution of particles in space, with the K value set to 5, and the particle concentration and residence time in each grid cell were calculated. 3Areas where the residence time exceeds 60s are defined as high-risk areas and marked in the three-dimensional model. The 1m range around the operating table and the 0.5m range in front of the nurse's workbench are marked. For the identified high-risk areas, ANSYS Design Xplorer is used for parametric design, and the position, size, number, fresh air volume and air supply angle of the air supply outlet are set as variables, and the variable range and constraints are defined. According to the existing fresh air volume, 5 increment levels are set with a step size of 10%. The position, size and number of local air supply outlets are adjusted, and the air supply angle is optimized while increasing the fresh air volume. Multiple groups of optimization schemes are generated, and the corresponding geometric models and grids are established in the CFD software. CFD simulation is performed on each group of optimization schemes to calculate the air flow velocity, temperature distribution and particle concentration in the high-risk area. The weighted scoring method is used to comprehensively evaluate each scheme, and the weights of air flow velocity, temperature distribution and particle concentration are set to 0.4, 0.3 and 0.3 respectively. The scheme with the highest score is selected as the final optimization scheme. The final optimization scheme was applied to the overall model, and the full-scale particle motion trajectory simulation was performed again to calculate the residence time and sedimentation probability of particles in key areas. The optimization effect was quantitatively compared by calculating the percentage reduction in the area of ​​high-risk areas and the percentage reduction in the average particle concentration before and after optimization. The optimization effect was calculated and reported separately for the key areas around the operating table and in front of the nurse's workbench. A 10m×8m×3m operating room model was imported into ANSYS Fluent, and 10,000 tracer particles with a diameter of 1μm were released and tracked for 300s. The K-means clustering algorithm was used to analyze the particle distribution, and the space was divided into a grid of 0.1m×0.1m×0.1m. Three high-risk areas were calculated: around the operating table, with a concentration of 150CFU / m 3 , residence time 80s, nurse workstation, concentration 120CFU / m 3 , residence time 70s and room corners, where the concentration is 200CFU / m 3 , residence time 90s. In ANSYSDesign Xplorer, set the air outlet position variables (x:2-8m,y:1-7m,z:2.5-3m), size variables, ranging from 0.5-2m, quantity variables from 1 to 4, fresh air volume increment, ranging from 10%-50% and air supply angle, ranging from 0-45°. Generate 50 sets of optimization schemes and simulate them in Fluent. Use weighted scoring method to evaluate, air flow velocity, with weight 0.4, target 0.25m / s, temperature distribution, with weight 0.3, target 20-22℃, particle concentration, with weight 0.3, target <100CFU / m 3Optimal solution: 2 1.5m×1m air outlets, located at (3m, 4m, 2.8m) and (7m, 4m, 2.8m), 30% increase in fresh air volume, 15° air supply angle. After optimization, the high-risk area was reduced by 60% and the average particle concentration was reduced by 45%. The concentration around the operating table was reduced to 80 CFU / m 3 , the residence time was reduced to 40s; the concentration at the nurse workstation was reduced to 70CFU / m 3 , the residence time is reduced to 35s.

[0054] Step S107, comprehensively evaluate the impact of the optimized ventilation system design on the overall energy consumption of the hospital, conduct an economic analysis, weigh the balance between energy-saving benefits and infection control effects, and ultimately determine the implementable airflow organization optimization strategy and supporting measures to provide the hospital with a customized infection control solution.

[0055] Obtain hospital building parameters and system operation data, and establish an overall energy consumption model for the hospital; based on the overall energy consumption model of the hospital, use Energy Plus software to conduct dynamic energy consumption analysis throughout the year to obtain energy consumption comparison data before and after optimization; based on the energy consumption comparison data, calculate the initial investment cost and annual operation and maintenance cost of the optimization plan to determine the economic feasibility of the optimization plan; for the optimization plan, establish a dual-objective optimization model of energy-saving benefits and infection control effects; use the NSGA-II algorithm to solve the dual-objective optimization model to obtain the optimal solution set under different weights; based on the optimal solution set, determine the most suitable balance plan for the actual situation of the hospital; for the balance plan, formulate an airflow organization optimization strategy, including fresh air unit selection parameters, inverter control strategy and temperature and humidity sensor layout plan; based on the airflow organization optimization strategy, form a customized infection control solution for the hospital, and the infection control solution includes equipment selection, control strategy and operation management details.

[0056] Specifically, according to the optimized ventilation system design scheme, the overall energy consumption model of the hospital was established, and the annual energy consumption of the air conditioning system, the supply and exhaust system, and the lighting system were calculated. The EnergyPlus software was used to conduct a dynamic energy consumption analysis throughout the year, taking into account factors such as local meteorological data, building orientation, enclosure structure, and internal heat load. The simulation time step was set to 1 hour, and the energy consumption comparison data before and after optimization were obtained. An economic analysis was conducted to calculate the initial investment cost of the optimization scheme, including the costs of equipment renewal, pipeline transformation, and control system upgrade, and to estimate the annual operation and maintenance costs and energy cost savings. Assuming an annual inflation rate of 2% and a discount rate of 5%, the net present value for a 10-year period was calculated. The net present value method and the payback period method were used to evaluate the economic feasibility of the scheme and obtain economic evaluation indicators. A multi-objective optimization model was established, with energy-saving benefits and infection control effects as two objective functions. The percentage reduction in particulate matter concentration and the percentage reduction in the area of ​​high-risk areas were used as quantitative indicators of infection control effects. The NSGA-II algorithm was used for multi-objective optimization, and the population size was set to 100, the evolutionary number to 500, the crossover probability to 0.9, and the mutation probability to 0.1. The economic evaluation index was used as a constraint condition to solve the optimal solution set under different weights, generate a decision frontier curve, and provide decision makers with multiple equilibrium options. According to the multi-objective optimization results, combined with factors such as the number of beds, outpatient volume, department distribution, existing equipment status, and annual budget of the hospital, the most suitable equilibrium solution was selected. Detailed airflow organization optimization strategies and supporting measures were formulated, including the selection parameters of the fresh air unit, the control strategy of the frequency converter, the layout of the temperature and humidity sensors, and the installation location of the air purification device. A customized infection control solution for the hospital was formed, including specific implementation details such as equipment selection, control strategy, and operation management. An energy consumption model of a tertiary hospital was constructed in the Energy Plus software, with a building area of ​​50,000m 2 , taking into account local meteorological data, such as an average annual temperature of 15°C, relative humidity of 60%, the building is oriented 15° south-east, and the thermal performance of the enclosure structure is set according to national standards. The internal heat load includes personnel, an average of 20W / person, equipment, 20W / m 2 and lighting, 10W / m 2. The simulation time step is set to 1 hour, and the annual power consumption before optimization is 6 million kWh, and after optimization is 5.5 million kWh, with a power saving rate of 8.3%. The economic analysis shows that with an initial investment of 2 million yuan, the annual operation and maintenance cost increases by 100,000 yuan, and the energy cost is saved by 500,000 yuan / year. Assuming the electricity price is 1 yuan / kWh, the inflation rate is 2%, and the discount rate is 5%, the 10-year net present value is calculated to be 1.5 million yuan, and the investment payback period is 4.5 years. The multi-objective optimization adopts the NSGA-II algorithm, and the objective function is the energy saving rate and the sensor control effect, such as a 40% reduction in particulate matter concentration and a 60% reduction in the area of ​​high-risk areas. After running 500 generations, 30 non-dominated solutions were obtained, among which the best balance solution was: 7.5% energy saving rate and 55% improvement in sensor control effect. According to the plan, specific measures are formulated: select variable frequency fresh air units, adopt a variable air volume control strategy based on CO2 concentration, and install 20 HEPA filters in key areas. The layout density of temperature and humidity sensors is 1

[0057] / 50m 2 , adjust the supply air temperature to 20℃±1℃ and the relative humidity to 50%±5%.

[0058] The above is only a specific implementation method of this specification. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of this specification is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this specification, and these modifications or replacements should be covered within the protection scope of this specification.

Claims

1. A method for purifying indoor air, characterized in that: include: Obtain the hospital's spatial layout and functional zoning information, and use 3D modeling software to construct geometric models of each functional area of ​​the hospital; According to the geometric models and equipment parameters of each functional area of ​​the hospital, the model is meshed in the computational fluid dynamics software, boundary conditions and initial conditions are set, and a numerical model of the airflow organization of the hospital space is constructed; According to different ventilation methods, set the air outlet position, wind speed and temperature, simulate and analyze the air flow speed, temperature and humidity distribution, and evaluate the ventilation effect; The particle size distribution characteristics of common hospital pathogens and surgical particles were obtained, and the Lagrangian particle tracking method was used to calculate the movement trajectory and deposition distribution of particles under different ventilation strategies; By comparing and analyzing the airflow organization characteristics and particle distribution of different ventilation strategies, the infection control effect is evaluated and the optimal ventilation system design is selected; Based on the target design plan, large eddy simulation technology is used to conduct detailed simulation of local areas to verify the stability and reliability of the ventilation effect and form a customized hospital infection control solution.

2. The method according to claim 1, characterized in that The construction of the numerical model of hospital space airflow organization includes: Structured hexahedral meshes are used to divide regular areas, and unstructured tetrahedral meshes are used to divide complex areas. Define the wall, inlet and outlet at the model boundary, and set the initial temperature field, velocity field and pressure field; The standard k-ε turbulence model is used to describe the air flow characteristics. The energy equation and component transport equation are set as the control equations, and the finite volume method is used for discrete solution.

3. The method according to claim 1, characterized in that The simulation analyzes airflow velocity, temperature and humidity distribution, including: For laminar ventilation and mixed ventilation, different air outlet layout schemes are set to adjust the air outlet area, wind speed and temperature range; The standard k-ε turbulence model or RNGk-ε turbulence model is used to solve the airflow field and obtain velocity, temperature and humidity distribution data; Draw air flow velocity cloud maps, temperature contour maps and humidity distribution maps for sections at different heights, calculate the average wind speed, temperature uniformity index and relative humidity standard deviation of each area, use the weighted scoring method to calculate the comprehensive score, and screen the optimal ventilation strategy.

4. The method according to claim 1, characterized in that The Lagrangian particle tracking method is used to calculate the movement trajectory and deposition distribution of particles under different ventilation strategies, including: Based on the particle size distribution data of pathogens and surgical particles, the maximum likelihood estimation method was used to fit the log-normal distribution function to determine the average particle size and standard deviation of the particles. Set the initial release point of the particles, configure the particle tracking solver parameters, select the Lagrangian discrete phase method, and enable the gravity settling module; Particle tracking simulations were performed for different ventilation schemes, and the spatial concentration distribution was calculated using the grid division method. The average suspension time, sedimentation rate and spatial distribution differences of particles in each area were statistically analyzed.

5. The method according to claim 1, characterized in that The infection control effects of different ventilation strategies are evaluated by comparing and analyzing the airflow organization characteristics and particle distribution of different ventilation strategies, including: Calculate the average wind speed, temperature uniformity index, relative humidity standard deviation, particle concentration, deposition rate and residence time in each area; The analytic hierarchy process is used to determine the weight of each indicator, and the judgment matrix is ​​constructed, the eigenvector is calculated and the consistency test is performed; Set thresholds for various indicators according to the hospital air quality standards, screen candidate solutions that meet the requirements, calculate the comprehensive scores, and select the solution with the highest score as the optimal design solution.

6. The method according to claim 1, characterized in that The large eddy simulation technology is used to perform a refined simulation of the local area, including: The wind speed, temperature and angle parameters of the target design are used as boundary conditions to define a fine computational grid in the key area; Specify the subgrid turbulence model, perform transient large eddy simulation, and obtain turbulent pulsation data; Analyze turbulence intensity, turbulence kinetic energy and Reynolds stress to determine the fluctuation characteristics and stability of the airflow field; If there are stagnation zones in local areas or the turbulence intensity exceeds the standard, adjust the ventilation parameters until the design requirements are met.

7. The method according to claim 1, characterized in that The customized hospital infection control solution includes: Establish an overall energy consumption model for the hospital, use EnergyPlus software to simulate dynamic energy consumption throughout the year, and evaluate the energy-saving benefits of ventilation optimization solutions; Consider the initial investment cost and operation and maintenance costs to determine economic feasibility; Weighing the energy-saving benefits and sensor control effects, a dual-objective optimization model was established, and the NSGA-II algorithm was used to solve the problem and obtain a balanced solution. Formulate airflow organization optimization strategies, including fresh air unit selection, frequency conversion control and sensor layout, to form an overall solution covering equipment selection, control strategy and operation management.

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