Indoor pathogen aerosol transmission risk quantitative evaluation and closed-loop control method

Abstract

The invention provides an indoor pathogen aerosol transmission risk quantitative evaluation and closed-loop control method, and relates to the technical field of public health environment engineering.The indoor pathogen aerosol transmission risk quantitative evaluation and closed-loop control method comprises the steps that multi-dimensional environment parameters such as spatial layout, ventilation operation parameters, pathogen characteristics, personnel activity and protection equipment parameters are obtained; obtaining an air concentration distribution field and a surface concentration distribution field through numerical calculation; respectively constructing dose calculation and dose response models of the contact path and the inhalation path, wherein the inhalation path divides the respiratory tract into at least an upper respiratory tract region, a tracheal bronchus region and a pulmonary alveolar region, and calculating a partitioned deposited dose; monte Carlo simulation is adopted to represent the randomness of key parameters, the contact infection probability, the inhalation infection probability and the comprehensive infection risk are output, a ventilation control set value or a control instruction meeting a risk threshold value is generated, and accurate evaluation and closed-loop control of the indoor pathogen aerosol transmission risk can be achieved.

Description

Technical Field

[0001] This application relates to the field of public health environmental engineering technology, and in particular to methods for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk. Background Technology

[0002] In indoor environments with high requirements for the prevention and control of respiratory infectious diseases, such as negative pressure wards, ICUs, and fever clinics, aerosol transmission of pathogens is a significant route of infection. Accurate assessment and effective control of this transmission risk are crucial for ensuring the safety of healthcare workers and preventing the spread of the epidemic. Relevant quantitative assessment and closed-loop control methods can quantify risks and coordinate prevention and control measures through scientific means, showing broad application prospects in the medical and public health fields.

[0003] Currently, most common indoor pathogen aerosol transmission risk assessment methods rely on numerical simulations to obtain aerosol diffusion and transport patterns and spatial concentration distribution, providing a reference for prevention and control decisions by outputting concentration field data or relative risk indicators. However, this method only focuses on the spatial distribution characteristics of aerosols and does not delve into the complete chain of infection occurrence.

[0004] However, existing methods cannot directly quantify the risks throughout the entire process of concentration deposition, ingested dose, and infection response. They also fail to consider the combined impact of both contact and inhalation transmission routes, and struggle to reflect the differences in deposition across different respiratory tract areas and the randomness of various key factors. This results in inaccurate risk assessments and an inability to provide precise data for coordinated control measures such as ventilation systems. Therefore, existing technologies suffer from insufficient accuracy in assessing indoor pathogen aerosol transmission risks and a lack of coordinated control measures. Summary of the Invention

[0005] The purpose of this application is to provide a quantitative assessment and closed-loop control method for the risk of indoor pathogen aerosol transmission, in order to solve the problems of insufficient accuracy in the risk assessment of indoor pathogen aerosol transmission and lack of coordination in prevention and control in the existing technology.

[0006] To address the aforementioned technical problems, firstly, this application provides a method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk, including:

[0007] Acquire multidimensional environmental parameters of the target room and store the multidimensional environmental parameters in the memory of the computing device;

[0008] Based on the multidimensional environmental parameters, a computational domain is established in the computing device to calculate the airborne pathogen concentration field and the surface pathogen concentration field, and to extract the respiratory zone concentration from the airborne pathogen concentration field.

[0009] Based on the pathogen concentration field on the object surface, combined with the human activity parameters in the multidimensional environmental parameters, the dose ingested via the contact route is calculated, and the dose ingested via the contact route is input into a preset surface risk model to obtain the probability of contact infection.

[0010] Based on the concentration in the respiratory zone and the protective equipment parameters in the multidimensional environmental parameters, the deposition dose in each region of the human respiratory tract is calculated, and the deposition dose is input into a preset inhalation risk model to obtain the probability of inhalation infection.

[0011] Based on the contact infection probability and the inhalation infection probability, a comprehensive risk assessment is performed through coupled calculation, and the statistics of the contact infection probability, the inhalation infection probability, and the comprehensive infection risk are output.

[0012] Based on the statistical data of the comprehensive infection risk and the preset risk threshold, control parameters for the ventilation system are generated, and the indoor ventilation system is controlled according to the control parameters to adjust the operating parameters.

[0013] Optionally, based on the multidimensional environmental parameters, a computational domain is established in the computing device to calculate the airborne pathogen concentration field and the surface pathogen concentration field, respectively, including:

[0014] Based on the spatial layout parameters in the multidimensional environmental parameters, a computing domain is established in the computing device;

[0015] Based on the ventilation system operating parameters and boundary condition parameters in the multidimensional environmental parameters, the computational domain is discretized into a grid, and corresponding boundary conditions are set according to the air supply outlets, exhaust outlets, pressure differentials, or leakage channels in the ventilation system.

[0016] Using the boundary conditions as constraints, and by coupling pathogen characteristic parameters in the multidimensional environmental parameters, the aerosol convection, diffusion, transport, and deposition processes are simulated to obtain the preliminary concentration field of pathogens in the air and on object surfaces.

[0017] The characteristics of pathogen activity decaying over time are further incorporated into the initial concentration field to obtain an airborne pathogen concentration field and an object surface pathogen concentration field that include the decay effect.

[0018] Optionally, based on the pathogen concentration field on the object surface and combined with the human activity parameters in the multidimensional environmental parameters, the exposure route intake dose is calculated, and the exposure route intake dose is input into a preset surface risk model to obtain the contact infection probability, including:

[0019] Based on the pathogen concentration field on the object surface, a transfer model between the surface and the hand based on the concentration gradient is used to update the pathogen surface density on the hand after contact. The update needs to refer to the pathogen surface density on the hand before contact and the pathogen concentration on the object surface in the contact area.

[0020] Combining the human activity parameters in the multidimensional environmental parameters, based on the updated hand pathogen areal density, and in conjunction with the transfer characteristics between the surface and the hand, the transfer characteristics between the hand and the mucous membrane, the contact area, the contact frequency between the hand and the mucous membrane, the exposure duration, and the pathogen decay characteristics, the dose ingested via the contact route is calculated.

[0021] The portion of the ingested dose that is transferred to the facial mucosa via the contact route is substituted into a preset surface risk model to obtain the probability of contact infection; wherein, the preset surface risk model is in exponential form and requires the import of response-related parameters corresponding to the target pathogen during construction.

[0022] Optionally, based on the concentration in the respiratory zone and combined with the protective equipment parameters in the multidimensional environmental parameters, the deposition dose in each region of the human respiratory tract is calculated, and the deposition dose is input into a preset inhalation risk model to obtain the probability of inhalation infection, including:

[0023] The human respiratory tract is divided into at least three regions: the upper respiratory tract, trachea and bronchi, and alveoli. The filtration effect is determined based on the protective equipment parameters in the multidimensional environmental parameters. The filtration effect is the product of the efficiency of the filter material itself and the tightness correction coefficient.

[0024] Based on the concentration in the respiratory zone and the filtration effect, combined with the respiratory ventilation and the deposition characteristics of each region of the human respiratory tract, the deposition dose corresponding to each region is calculated respectively.

[0025] The deposition dose of each region is substituted into a preset inhalation risk model to obtain the probability of inhalation infection. The preset inhalation risk model is in the form of a partitioned superposition. When constructing it, the corresponding reaction-related parameters of each respiratory region need to be imported. The superposition calculation is performed by superimposing the correlation results of the deposition dose of each region with the corresponding reaction-related parameters.

[0026] Optionally, based on the contact infection probability and the inhalation infection probability, a comprehensive risk assessment is performed through coupled calculation, and statistics of the contact infection probability, the inhalation infection probability, and the comprehensive infection risk are output, including:

[0027] From the multidimensional environmental parameters and the relevant calculation parameters of the contact route and inhalation route, some parameters are selected as random variables. The random variables include at least one or more of the following: surface-to-hand transfer characteristics, hand-to-mucous membrane transfer characteristics, hand-to-mucous membrane contact frequency, pathogen attenuation-related characteristics, surface risk model response-related parameters, and inhalation risk model zonal response-related parameters.

[0028] A probability distribution is set for the random variable, and sampling is performed according to the preset probability distribution using the Monte Carlo simulation method;

[0029] For each combination of parameters obtained from sampling, the ingested dose via the contact route, the probability of contact infection, the deposition dose in each area, and the probability of inhalation infection are calculated sequentially. Then, the comprehensive infection risk corresponding to a single sampling is obtained through coupled calculation.

[0030] Summarize the contact infection probability, inhalation infection probability, and overall infection risk corresponding to all samples, calculate and output the statistics of the contact infection probability, the inhalation infection probability, and the overall infection risk, whereby the statistics include the mean, quantiles, or confidence intervals.

[0031] Optionally, based on the statistical measures of the comprehensive infection risk and a preset risk threshold, control parameters for the ventilation system are generated, and the indoor ventilation system is controlled according to the control parameters to adjust the operating parameters, including:

[0032] Under the constraint that the statistical quantity of the comprehensive infection risk does not exceed the preset risk threshold, the control parameters of the ventilation system are generated with at least one of the following as the optimization target: energy consumption index, pressure difference fluctuation index, or noise index. The control parameters include at least one of the following: air change rate set value, supply and exhaust air volume set value, pressure difference set value, supply and return air ratio set value, air outlet condition set value, or operating period strategy.

[0033] The control parameters are converted into control commands for the indoor ventilation system, and the control commands are sent to the indoor ventilation system through the ventilation system interface, so that the indoor ventilation system can automatically adjust its operating parameters to reduce the overall risk of infection.

[0034] Optionally, before obtaining the multidimensional environmental parameters of the target room, the following steps are also included:

[0035] The system receives real-time data from monitoring points of the indoor ventilation system via a ventilation system interface. The real-time data includes at least one of temperature, humidity, pressure difference, air volume, and wind speed. Based on the real-time data, the boundary condition parameters in the multidimensional environmental parameters are updated.

[0036] Secondly, this application provides a quantitative assessment and closed-loop control system for indoor pathogen aerosol transmission risk, comprising:

[0037] The ventilation system data interface module is used to obtain operating parameters and real-time data from monitoring points of the indoor ventilation system, and to send control commands to the indoor ventilation system.

[0038] The data acquisition module is used to acquire multi-dimensional environmental parameters of the target room and store the multi-dimensional environmental parameters in the memory of the computing device;

[0039] The concentration field construction module is used to establish a computing domain in the computing device based on the multidimensional environmental parameters, calculate the airborne pathogen concentration field and the surface pathogen concentration field respectively, and extract the respiratory zone concentration from the airborne pathogen concentration field.

[0040] The contact risk calculation module is used to calculate the exposure dose based on the pathogen concentration field on the object surface and the human activity parameters in the multidimensional environmental parameters, and input the exposure dose into a preset surface risk model to obtain the contact infection probability.

[0041] The inhalation risk calculation module is used to calculate the deposition dose of each region of the human respiratory tract based on the concentration in the breathing zone and the protective equipment parameters in the multidimensional environmental parameters, and input the deposition dose into a preset inhalation risk model to obtain the probability of inhalation infection.

[0042] The comprehensive assessment module is used to perform a comprehensive risk assessment based on the contact infection probability and the inhalation infection probability through coupled calculation, and output the statistics of the contact infection probability, the inhalation infection probability and the comprehensive infection risk;

[0043] The control output module is used to generate control parameters for the ventilation system based on the statistics of the comprehensive infection risk and the preset risk threshold, and to control the indoor ventilation system according to the control parameters to adjust the operating parameters.

[0044] Thirdly, this application provides an electronic device, comprising:

[0045] Memory, used to store computer programs;

[0046] A processor is configured to execute the computer program to implement the steps of the method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk as described in the first aspect above.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control method described in the first aspect above.

[0048] The method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk provided in this application can provide comprehensive and accurate data support for subsequent risk assessment and control by acquiring and storing multi-dimensional environmental parameters of the target indoor environment; it can accurately characterize the spatial distribution characteristics of pathogens by establishing a computational domain and calculating the concentration field of pathogens in the air and on object surfaces, and extracting the concentration in the respiratory zone; it can quantify the infection risk caused by contact transmission by calculating the dose ingested through contact routes and obtaining the probability of contact infection by combining personnel activity parameters; it can accurately assess the infection risk of inhalation transmission by calculating the deposition dose in each area of ​​the respiratory tract and obtaining the probability of inhalation infection by combining protective equipment parameters; it can provide comprehensive risk reference for prevention and control decisions by performing comprehensive risk assessment through coupled calculations and outputting relevant statistics; and it can achieve closed-loop risk management by generating ventilation control parameters based on comprehensive infection risk and preset thresholds and controlling the adjustment of the ventilation system.

[0049] Furthermore, a computational domain is established based on spatial layout parameters from the multidimensional environmental parameters. Mesh discretization and boundary condition settings are performed by combining ventilation system operating parameters and boundary condition parameters. Pathogen characteristic parameters are coupled to simulate aerosol convection, diffusion, transport, and deposition processes to obtain a preliminary concentration field. Then, the decay characteristics of pathogen activity over time are incorporated to finally obtain an airborne and surface pathogen concentration field that includes the decay effect. Through refined computational domain construction and concentration field simulation, multiple factors such as environment, ventilation, pathogen characteristics, and decay are fully considered, enabling the acquisition of more accurate concentration field data that closely reflects real-world scenarios, providing a reliable foundation for subsequent infection risk calculations. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating the method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk provided in this application embodiment;

[0052] Figure 2 A flowchart illustrating the specific implementation of the indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control method provided in this application embodiment;

[0053] Figure 3 This is a schematic diagram of a quantitative assessment and closed-loop control system for indoor pathogen aerosol transmission risk provided in an embodiment of this application. Detailed Implementation

[0054] Existing methods for assessing the risk of indoor pathogen aerosol transmission can only output the spatial concentration distribution of aerosols or relative risk indicators. They cannot fully quantify the infection risk throughout the entire process of "concentration deposition—ingested dose—infection response," nor do they take into account the combined impact of the two main transmission routes, contact and inhalation. Furthermore, these methods fail to reflect the differences in deposition in different areas of the human respiratory tract and do not adequately consider the randomness of key factors such as personnel behavior and the efficiency of protective equipment. This results in insufficient accuracy of risk assessment results, failing to provide precise linkage control basis for ventilation systems and other prevention and control measures, and thus failing to meet the needs of precise prevention and control in medical settings.

[0055] To address the aforementioned issues, this invention proposes a quantitative assessment and closed-loop control method for indoor pathogen aerosol transmission risk. The core of this method involves systematically collecting multidimensional environmental and personnel-related parameters to construct pathogen concentration fields in the air and on object surfaces. Then, the infection risks via contact and inhalation routes are calculated separately, ultimately coupling these to obtain a comprehensive infection risk and linking it to the ventilation system for closed-loop control. Specifically, this method first comprehensively acquires key parameters such as indoor spatial layout, ventilation operation, pathogen characteristics, personnel activities, and protective equipment. Based on these parameters, a calculation model is established to accurately characterize the distribution of pathogens in the air and on object surfaces. Subsequently, the infection probabilities of "surface-hand-mucous membrane" contact transmission and respiratory tract inhalation transmission are quantified separately, combining the two pathways to obtain a comprehensive risk. Finally, based on the comprehensive risk and preset thresholds, ventilation system control parameters are automatically generated, and the ventilation operation status is adjusted. This method covers both transmission routes, refines respiratory deposition differences, and incorporates the influence of key factors, fundamentally solving the problems of insufficient accuracy and inadequate linkage in existing technologies. It provides scientific and reliable technical support for the prevention and control of infectious diseases in high-risk indoor medical environments.

[0056] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] The core of this application is to provide a method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk. A flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0058] S101. Obtain multi-dimensional environmental parameters of the target room and store the multi-dimensional environmental parameters in the memory of the computing device.

[0059] Among them, the multidimensional environmental parameters include the spatial layout parameters of the target indoor environment, the operation parameters and boundary condition parameters of the ventilation system, the characteristic parameters of the target pathogen, the personnel activity parameters, and the protective equipment parameters.

[0060] Ventilation system operating parameters may include real-time data and / or preset operating settings from monitoring points such as supply air volume, return / exhaust air volume, air exchange rate, differential pressure setpoint, vent conditions, temperature, humidity, and wind speed; spatial layout parameters cover data such as the geometric dimensions of the target room, beds, partitions, doors, windows, and the location and specifications of supply and return air vents; target pathogen characteristic parameters refer to key characteristic data such as the pathogen's activity decay rate (or half-life), viral load, and dose-response constant; personnel activity parameters include behavioral data such as the activity intensity of indoor personnel, the frequency of contact between hands and objects / facial mucous membranes, contact area, and exposure duration; protective equipment parameters mainly include technical parameters such as the filtration efficiency and fit of equipment such as masks and respirators.

[0061] In one specific implementation, the above data is acquired collaboratively through multiple methods: spatial layout parameters can be obtained by analyzing architectural drawings or on-site measurements; ventilation system-related parameters are collected in real time from the ventilation system's monitoring modules and sensors, while preset values ​​are retrieved as a supplement; pathogen characteristic parameters are referenced from authoritative experimental data or publicly available literature; personnel activity parameters are determined through on-site observation and statistics or analysis of work records; and protective equipment parameters are directly collected from the corresponding equipment's technical specifications. All acquired data, after being formatted uniformly, is stored in the memory of the computing device for retrieval in subsequent steps.

[0062] S102. Based on the multidimensional environmental parameters, a computing domain is established in the computing device to calculate the airborne pathogen concentration field and the surface pathogen concentration field, and the respiratory zone concentration is extracted from the airborne pathogen concentration field.

[0063] Among them, the computational domain is a three-dimensional virtual simulation area constructed based on the actual spatial characteristics of the target indoor space; the airborne pathogen concentration field refers to the spatial distribution data of pathogens in indoor air; the surface pathogen concentration field is the distribution data of pathogens deposited on various surfaces of indoor objects; and the respiratory zone concentration is pathogen concentration data extracted from the air concentration field and corresponding to the human breathing height, used to accurately calculate the risk of inhalation infection.

[0064] Optionally, step S102 may specifically include the following steps:

[0065] S1021. Based on the spatial layout parameters in the multidimensional environmental parameters, a computing domain is established in the computing device.

[0066] S1022. Based on the ventilation system operating parameters and boundary condition parameters in the multidimensional environmental parameters, the computational domain is discretized into a grid, and corresponding boundary conditions are set according to the air supply outlet, air exhaust outlet, pressure difference or leakage channel in the ventilation system.

[0067] Among them, grid discretization is to divide the constructed three-dimensional computational domain into several small units so that the pathogen concentration can be calculated unit by unit through numerical methods; boundary conditions refer to the environmental settings of the computational domain boundary, including the airflow velocity of the air supply vent, the exhaust volume of the air outlet, the indoor and outdoor pressure difference, etc., to simulate the real indoor airflow environment.

[0068] S1023. Using the boundary conditions as constraints, by coupling the pathogen characteristic parameters in the multidimensional environmental parameters, the aerosol convection, diffusion, transport and deposition process is simulated to obtain the preliminary concentration field of pathogens in the air and on the surface of objects.

[0069] Among them, the aerosol convection diffusion transport and deposition process refers to the dynamic process by which pathogens diffuse with the airflow and are deposited on the surface of objects under the action of gravity and airflow; coupling pathogen characteristic parameters means incorporating the pathogen's specific characteristics such as diffusion coefficient and sedimentation velocity into the simulation process to ensure that the simulation results are close to reality.

[0070] S1024. The characteristic of pathogen activity decaying over time is further incorporated into the preliminary concentration field to obtain an airborne pathogen concentration field and an object surface pathogen concentration field that include the decay effect.

[0071] Among them, the decay of pathogen activity over time refers to the loss of pathogen activity in the air and on object surfaces over time. The degree of decay can be quantified by the half-life. The longer the half-life, the longer the pathogen activity is maintained.

[0072] In one specific implementation, this step involves constructing a 3D simulation environment that closely matches the actual indoor scene of the target room based on spatial layout parameters. This 3D simulation environment is then discretized into a mesh using ventilation system operating parameters and boundary condition parameters. Boundary conditions are set for air supply outlets, exhaust outlets, pressure differentials, and leakage channels. The diffusion coefficient and settling velocity of the target pathogen are coupled to simulate the convective diffusion process of aerosols indoors and their deposition process on object surfaces to obtain a preliminary concentration field. Finally, the characteristics of pathogen activity decaying over time are incorporated to correct the preliminary concentration field. This complete process yields an airborne pathogen concentration field and an object surface pathogen concentration field that include the decay effect, and the concentration in the respiratory zone is extracted. The specific application scenario using a negative pressure ward is explained below:

[0073] First, a computational domain is constructed using S1021. Based on the spatial layout parameters such as the geometric dimensions, bed layout, partition positions, air supply and return vents, and door and window distribution of the negative pressure ward, a three-dimensional virtual area corresponding to the actual ward is built in the computing device at a 1:1 scale to ensure that the simulated environment is consistent with the real scene.

[0074] Secondly, the computational domain was processed and boundary conditions were set using S1022. The finite volume method was employed to discretize the three-dimensional computational domain into a mesh, dividing the ward area into several uniform small units. The unit size was set to 0.1m × 0.1m × 0.1m based on the ward space size to ensure computational accuracy. Subsequently, based on the ventilation system operating parameters, the air supply volume of the air outlet was set to 500. The wind speed is 2 m / s, and the exhaust volume of the exhaust vent is 550. The indoor and outdoor pressure difference is -15Pa, and the door gap is defined as a leakage channel to complete the boundary condition configuration, laying the foundation for subsequent airflow simulation.

[0075] Next, the aerosol transport and deposition process was simulated using S1023. With the boundary conditions set in step 1022 as constraints, a CFD (Computational Fluid Dynamics) algorithm was used to solve the indoor airflow field, while simultaneously coupling the diffusion coefficient of SARS-CoV-2. The study simulates the diffusion and transport processes of aerosols under airflow, as well as their deposition processes on surfaces such as hospital beds, walls, and floors, using pathogen characteristic parameters such as a settling velocity of 0.001 m / s. This yields a preliminary concentration field of pathogens in the air and on object surfaces. The above example is merely one illustration of this application; in practical applications, relevant values ​​can be adjusted according to the ventilation parameters and pathogen characteristics of different indoor environments. This application does not impose any limitations on this.

[0076] Finally, the concentration field was corrected using S1024. A pathogen activity decay model was introduced, and based on the half-life data of different material surfaces, such as 6.81 h for plastic surfaces and 0.774 h for copper surfaces, the decay rate was calculated using the formula (1).

[0077] (1)

[0078] In the formula, The attenuation rate, It is the half-life.

[0079] For example, the attenuation rate of plastic surfaces The attenuation characteristics were incorporated into the initial concentration field, and the final airborne pathogen concentration field and object surface pathogen concentration field were obtained after correction considering the effect of time decay. At the same time, the concentration data of the corresponding area at the breathing height of medical staff 1.7m above the ground was extracted as the concentration of the breathing zone.

[0080] In another specific implementation, the LES large eddy simulation algorithm can be used instead of the CFD algorithm to solve the airflow field. This algorithm has higher simulation accuracy for turbulent flow and is suitable for scenarios with more precise requirements for airflow environment, such as ICU wards. Its core process is the same as above, only the details of the numerical calculation algorithm are different.

[0081] This application accurately replicates the distribution of pathogens indoors by coupling digital simulation with actual characteristics, and the resulting concentration field data can truly reflect the actual infection risk scenario.

[0082] S103. Based on the pathogen concentration field on the object surface and the personnel activity parameters in the multidimensional environmental parameters, calculate the exposure dose and input the exposure dose into a preset surface risk model to obtain the contact infection probability.

[0083] Among them, the dose ingested through contact refers to the total amount of pathogens that enter the human body through the transfer process of object surfaces, hands, and facial mucous membranes; the surface risk model is a mathematical model based on the correspondence between pathogen dose and infection probability, used to quantify the possibility of infection caused by contact ingestion dose.

[0084] Optionally, such as Figure 2 As shown, step S103 may specifically include the following steps:

[0085] S1031. Based on the pathogen concentration field on the object surface, a transfer model between the surface and the hand based on the concentration gradient is adopted to update the pathogen surface density on the hand after contact. When updating, the pathogen surface density on the hand before contact and the pathogen concentration on the object surface in the contact area need to be referenced.

[0086] Among them, the surface-to-hand transfer model based on concentration gradient refers to calculating the number of pathogens transferred from the surface to the hand based on the difference in pathogen concentration between the object surface and the hand. The greater the concentration difference, the more pathogens are transferred. Hand pathogen areal density refers to the number of pathogens attached to a unit area of ​​hand surface, which is used to characterize the degree of hand contamination.

[0087] S1032. Combining the personnel activity parameters in the multidimensional environmental parameters, based on the updated hand pathogen areal density, and in conjunction with the transfer characteristics between the surface and the hand, the transfer characteristics between the hand and the mucous membrane, the contact area, the contact frequency between the hand and the mucous membrane, the exposure duration, and the pathogen decay characteristics, the dose ingested via the contact route is calculated.

[0088] Among them, the transfer characteristics between surfaces and hands refer to the efficiency of pathogens transferring from object surfaces to hands, the transfer characteristics between hands and mucous membranes refer to the efficiency of pathogens transferring from hands to facial mucous membranes, and the pathogen attenuation characteristics refer to the characteristics of pathogens losing activity over time during the transfer process, which is quantified by the attenuation rate.

[0089] S1033. Substitute the portion of the ingested dose via the contact route that is transferred to the facial mucosa into a preset surface risk model to obtain the probability of contact infection; wherein, the preset surface risk model is in exponential form and requires the import of response-related parameters corresponding to the target pathogen during its construction.

[0090] Among them, the response-related parameter refers to a constant related to the infectivity of the pathogen, which is used to characterize the difference in the probability of infection caused by different doses of pathogen; the exponential form of the surface risk model is a model that can accurately describe the relationship between pathogen dose and infection probability, verified by a large amount of experimental data.

[0091] In one specific implementation, this step involves using a surface pathogen concentration field on the object surface, combined with the pre-contact hand pathogen areal density and the surface pathogen concentration in the contact area, to update the post-contact hand pathogen areal density using a concentration gradient-based surface-to-hand transfer model to clarify the degree of hand contamination. Then, by combining data such as contact area and hand-to-mucous membrane contact frequency from personnel activity parameters, along with the transfer characteristics of surface-to-hand and hand-to-mucous membrane interactions and pathogen attenuation characteristics, the intake dose transferred to the facial mucous membranes during the contact route is calculated. Finally, this intake dose is substituted into a preset exponential surface risk model, importing response-related parameters corresponding to the target pathogen, thus quantifying the complete process of contact infection probability. A specific explanation using a negative pressure isolation ward as an application scenario is provided below:

[0092] First, the surface density of pathogens on the hand is updated using S1031. A transfer model based on concentration gradient is adopted, and its core formula is shown in equation (2) below:

[0093] (2)

[0094] in, The surface density of pathogens on the hands after contact, in copies. ; The surface density of pathogens on the hand before contact, in copies. ; The transfer efficiency from the surface to the hand is a dimensionless parameter. The concentration of pathogens on the surface of the contact area, in copies / .

[0095] Assuming medical staff come into contact with the plastic bed railings in the negative pressure ward, the surface density of pathogens on their hands before contact... Pathogen concentration on the surface of the bed handrail copies Surface to hand transfer efficiency Take the median The surface density of pathogens on the hands after contact copies .

[0096] Secondly, the dose ingested via the exposure route is calculated using S1032. Combined with personnel activity parameters, the following formula (3) is used:

[0097] (3)

[0098] in, The dose is ingested via exposure, and the unit is copies. The contact area is expressed in units of... ; The frequency of hand-mucous membrane contact is expressed in times per hour. The efficiency of hand-to-mucosal transfer is a dimensionless parameter. Pathogen decay rate, in hours ; Exposure duration, in hours.

[0099] Using the example above, the contact area Pick Frequency of hand-to-mucous membrane contact The average was 15.7 times / hour, indicating the efficiency of manual mucosal transfer. Taking the median value of 0.37, the pathogen attenuation rate on plastic surfaces Hour Exposure duration of medical staff Hours, Ingestion dose via contact route , The above example is merely one illustration of this application. In practical applications, the relevant values ​​can be adjusted according to different contact scenarios and pathogen characteristics, and this application does not limit this.

[0100] Finally, the probability of contact infection is calculated using S1033. The preset surface risk model is in exponential form, as shown in formula (4):

[0101] (4)

[0102] in, The probability of infection through contact is a dimensionless parameter. For the dose of intake transferred to the facial mucosa, compared with Equivalent, unit is copies; The response-related parameters are those corresponding to the target pathogen, in units of... Response parameters related to SARS-CoV-2. Take the median value Substituting into the above calculations, the result is... copies, then probability of infection through contact .

[0103] In another specific implementation, the value of the reaction-related parameter k can be adjusted according to the characteristics of different pathogens. For example, for other respiratory viruses, the corresponding k value can be obtained by consulting authoritative experimental data. The core calculation process is the same as above, only the parameter value is different.

[0104] This application accurately simulates the complete chain of contact transmission and combines multiple key parameters to quantify the intake dose and infection probability, making the calculation of contact infection risk more in line with actual scenarios and providing accurate and reliable support for comprehensive risk assessment.

[0105] S104. Based on the concentration in the respiratory zone and the protective equipment parameters in the multidimensional environmental parameters, calculate the deposition dose in each region of the human respiratory tract, and input the deposition dose into a preset inhalation risk model to obtain the probability of inhalation infection.

[0106] Among them, the deposition dose refers to the total amount of pathogens carried in aerosols that are deposited in different areas of the respiratory tract after entering the human body through respiration; the inhalation risk model is a mathematical model built based on the infection characteristics of different areas of the respiratory tract, used to quantify the possibility of infection caused by the deposition dose.

[0107] Optionally, step S104 may specifically include the following steps:

[0108] S1041. The human respiratory tract is divided into at least three regions: the upper respiratory tract, the trachea and bronchi, and the alveoli. The filtration effect is determined based on the protective equipment parameters in the multidimensional environmental parameters. The filtration effect is the product of the efficiency of the filter material itself and the tightness correction coefficient.

[0109] Among them, the fit correction coefficient is a parameter used to adjust the actual filtration effect of protective equipment. It reflects the tightness of the fit between the equipment and the human face. The tighter the fit, the closer the correction coefficient is to 1, and the better the actual filtration effect. The filtration effect is the actual ability of protective equipment to block pathogens from entering the respiratory tract. It comprehensively reflects the filtration performance of the filter material itself and the fit of the equipment after it is worn.

[0110] S1042. Based on the concentration in the respiratory zone and the filtration effect, combined with the respiratory ventilation and the deposition characteristics of each region of the human respiratory tract, calculate the deposition dose corresponding to each region.

[0111] Among them, respiratory ventilation refers to the volume of air inhaled or exhaled by the human body per unit time, which is related to the intensity of human activity. The higher the activity intensity, the greater the ventilation. Deposition characteristics refer to the probability of pathogen aerosols being deposited in different areas of the respiratory tract, which is determined by the respiratory tract anatomy and airflow characteristics. The deposition rate varies in different areas.

[0112] S1043. Substitute the deposition dose of each region into the preset inhalation risk model to obtain the probability of inhalation infection; wherein, the preset inhalation risk model is in the form of partition superposition, and when constructing it, the corresponding reaction-related parameters of each respiratory region need to be imported, and the superposition calculation is performed by superimposing the correlation results of the deposition dose of each region and the corresponding reaction-related parameters.

[0113] Among them, the inhalation risk model in the form of partition superposition refers to calculating the infection contribution corresponding to the deposition dose in each region of the respiratory tract separately, and then superimposing the contributions of each region to obtain the overall probability of inhalation infection; the response-related parameters corresponding to each respiratory tract region are constants related to the infection receptor characteristics of the mucosal cells in that region, which are used to characterize the difference in the probability of infection caused by pathogens deposited in different regions.

[0114] In one specific implementation, this step first divides the human respiratory tract into three regions—upper respiratory tract, trachea and bronchi, and alveoli—based on its anatomical structure. Simultaneously, it determines the actual filtration effect by combining the efficiency and fit correction coefficient of the protective equipment's filter material. Then, based on the extracted respiratory zone concentration and this filtration effect, along with the respiratory ventilation volume matched to the intensity of personnel activity and the deposition characteristics of each respiratory tract region, it calculates the pathogen deposition dose corresponding to each of the three regions. Finally, it substitutes the deposition dose of each region into a preset partitioned superposition inhalation risk model, imports the corresponding response-related parameters for each region, and obtains the complete process of calculating the overall inhalation infection probability by associating the deposition dose of each region with the corresponding response-related parameters and performing superposition calculations. The specific application scenario using a negative pressure ward is explained below:

[0115] First, the respiratory tract is divided into regions and the filtration effect is determined by S1041. The human respiratory tract is divided into three regions: the upper respiratory tract, including the nasal cavity, oral cavity, pharynx, and larynx; the trachea and bronchi, including the trachea, bronchi, and bronchioles; and the alveoli, including the respiratory bronchi, pulmonary ducts, and alveolar sacs. At the same time, the filtration effect is determined based on the parameters of the protective equipment, and its core formula is shown in the following formula (5):

[0116] (5)

[0117] in, The filtration effect of protective equipment is a dimensionless parameter. The efficiency of the filter material itself is a dimensionless parameter. This is the fit correction factor, a dimensionless parameter. Assuming medical personnel wear N95 masks with a filter material efficiency of 0.95 and a fit correction factor of 0.98, then the filtration efficiency of the protective equipment is... .

[0118] Secondly, the deposition dose in each region was calculated using S1042. Based on the concentration and filtration effect in the breathing zone, combined with the respiratory ventilation and deposition characteristics in each region, the following formula (6) was used:

[0119] (6)

[0120] in, For the first Deposition dose in the region, in copies; This refers to the concentration in the respiratory zone, expressed in copies. Respiratory ventilation, in units of Hour; For the first The sedimentation rate of a region is a dimensionless parameter. The variable is for integration, and the unit is hours. The upper and lower limits of the integral are from 0 to t, representing the exposure duration.

[0121] Continuing with the example above, let's assume the concentration in the respiratory zone of the negative pressure ward is... The respiratory ventilation of medical staff under moderate activity intensity is Hours, exposure duration =3 hours; deposition rates in the upper respiratory tract, trachea, bronchi, and alveoli were respectively The upper respiratory tract deposition dose is then... The calculation process is as follows , copies; tracheobronchial deposition dose is The calculation process is as follows copies; alveolar deposition dose is (1- The calculation process is as follows The above example is merely one example of this application. In practical applications, the relevant values ​​can be adjusted according to different activity intensities, types of protective equipment, and pathogen concentrations. This application does not limit this.

[0122] Finally, the probability of inhalation infection is calculated using S1043. The preset inhalation risk model is in the form of partitioned superposition, and the formula is shown in equation (7) below:

[0123] (7)

[0124] in, This represents the probability of inhalation infection, which is a dimensionless parameter. For the first The reaction-related parameters corresponding to the region are in units of ; For the first Deposition dose in the region, in copies.

[0125] The response-related parameters for SARS-CoV-2 in the upper respiratory tract, trachea and bronchi, and alveoli are as follows: Substituting the deposition dose obtained from the above calculation, The calculation result is The probability of inhalation infection .

[0126] In another specific implementation, the response-related parameters for each region can be adjusted according to the infection characteristics of different pathogens. For example, for the influenza virus, the corresponding value can be obtained by consulting authoritative experimental data. The core calculation process is the same as described above, only the parameter values ​​are different.

[0127] This application, by precisely dividing the respiratory tract area, considering the actual filtration effect of protective equipment, and combining respiratory parameters and deposition characteristics to quantify the deposition dose in each area, makes the calculation of inhalation infection risk more in line with human physiological characteristics and actual protection scenarios, providing accurate and reliable support for comprehensive risk assessment.

[0128] S105. Based on the contact infection probability and the inhalation infection probability, a comprehensive risk assessment is performed through coupled calculation, and the statistics of the contact infection probability, the inhalation infection probability, and the comprehensive infection risk are output.

[0129] Among them, coupling calculation refers to the fusion of risk results from two routes of infection, contact infection and inhalation infection, to obtain a comprehensive risk that reflects the overall probability of infection; statistics are statistical descriptions of the results of multiple calculations, including mean, quantiles or confidence intervals, used to present risk distribution characteristics more comprehensively.

[0130] Optionally, step S105 may specifically include the following steps:

[0131] S1051. Select some parameters from the multidimensional environmental parameters and the relevant calculation parameters of the contact route and inhalation route as random variables. The random variables include at least one or more of the following: surface-to-hand transfer characteristics, hand-to-mucous membrane transfer characteristics, hand-to-mucous membrane contact frequency, pathogen attenuation-related characteristics, surface risk model response-related parameters, and inhalation risk model zonal response-related parameters.

[0132] Among them, random variables refer to parameters whose values ​​are uncertain in actual scenarios, and their values ​​may fluctuate due to individual differences, environmental changes and other factors; pathogen decay-related characteristics refer to parameters related to the decrease of pathogen activity over time, such as half-life and decay rate.

[0133] S1052. Set a probability distribution for the random variable and use the Monte Carlo simulation method to sample according to the preset probability distribution.

[0134] Among them, the probability distribution is a mathematical model used to describe all possible values ​​of a random variable and their corresponding probabilities of occurrence. Different types of random variables correspond to appropriate probability distributions. The Monte Carlo simulation method is a numerical calculation method that simulates the behavior of complex systems by sampling a large number of random samples. By sampling multiple times to cover different combinations of values ​​of random variables, it can approximate the real results.

[0135] S1053. For each combination of parameters obtained from sampling, the ingested dose via the contact route, the probability of contact infection, the deposition dose in each area, and the probability of inhalation infection are calculated sequentially, and then the comprehensive infection risk corresponding to a single sampling is obtained through coupled calculation.

[0136] S1054. Summarize the contact infection probability, inhalation infection probability, and overall infection risk corresponding to all samples, calculate and output the statistics of the contact infection probability, the inhalation infection probability, and the overall infection risk, wherein the statistics include the mean, quantiles, or confidence intervals.

[0137] Among them, the mean is the average value of multiple sampling results, reflecting the overall level of risk; the quantile is the key numerical point divided after the sampling results are sorted by size, used to reflect the distribution range of risk; the confidence interval refers to the range of the true risk value at a certain confidence level, used to characterize the reliability of the assessment results.

[0138] In one specific implementation, this step involves first selecting key parameters with uncertainties, such as the transfer efficiency between surfaces and hands, and the transfer efficiency between hands and mucous membranes, from multidimensional environmental parameters and relevant calculation parameters of contact and inhalation routes as random variables. Then, a suitable probability distribution is matched to each random variable to match its actual value patterns. Monte Carlo simulation is used to perform multiple samplings according to the set probability distribution to obtain multiple sets of parameter combinations. For each set of sampling parameters, the contact route ingestion dose, contact infection probability, respiratory tract deposition dose, and inhalation infection probability are calculated sequentially. The comprehensive infection risk corresponding to this set of parameters is obtained through coupling formulas. Finally, all sampling results are summarized, and the mean, quantiles, or confidence intervals of the contact infection probability, inhalation infection probability, and comprehensive infection risk are calculated and output. The specific application scenario using a negative pressure isolation ward is explained below:

[0139] First, random variables were selected via S1051. From multidimensional environmental parameters and related calculation parameters, the following were selected as random variables: surface-to-hand transfer efficiency, hand-to-mucous membrane transfer efficiency, hand-to-mucous membrane contact frequency, pathogen half-life, response-related parameters of the surface risk model, and zonal response-related parameters of the inhalation risk model. These parameters are all key factors affecting the infection risk calculation results and have significant uncertainties in real-world scenarios.

[0140] Secondly, probability distributions are set and sampling is performed via S1052. Suitable probability distributions are specified for each random variable: the transfer efficiency between surface and hand follows a log-normal distribution; the transfer efficiency between hand and mucous membrane follows a normal distribution; the contact frequency between hand and mucous membrane follows a normal distribution; the pathogen half-life follows a triangular distribution; the response-related parameters of the surface risk model follow a triangular distribution; and the zonal response-related parameters of the inhalation risk model follow a triangular distribution. A Monte Carlo simulation method is used, and sampling is performed according to the set probability distributions. The preset number of samplings is 10,000, and each sampling yields a complete set of parameters. The above example is merely one example of this application. In practical applications, the probability distribution type and sampling number can be adjusted according to parameter characteristics and scenario requirements; this application does not limit this.

[0141] Next, risk calculation for a single sampling is performed using S1053. For each parameter combination obtained from sampling, the following calculations are performed sequentially: First, based on the surface pathogen concentration field and the transfer efficiency between the surface and hands, and the transfer efficiency between hands and mucous membranes in this set of parameters, the dose ingested via the contact route is calculated, and then substituted into the surface risk model to obtain the probability of contact infection; Second, based on the concentration in the respiratory zone and the zonal reaction-related parameters in this set of parameters, the deposition dose in each respiratory tract region is calculated, and then substituted into the inhalation risk model to obtain the probability of inhalation infection; Third, the comprehensive infection risk corresponding to a single sampling is obtained through coupling calculation, and the core formula (8) is shown below:

[0142] (8)

[0143] in, Considering the overall infection risk, it is a dimensionless parameter. The probability of infection through contact is a dimensionless parameter. This represents the probability of inhalation infection, which is a dimensionless parameter.

[0144] For example, a certain sampling yields the probability of contact infection. Probability of inhalation infection The overall risk of infection .

[0145] Finally, the statistics are summarized and output using S1054. The probability of contact infection, the probability of inhalation infection, and the overall infection risk corresponding to 10,000 samples are summarized, and statistics for each of the three risk categories are calculated: the mean is the arithmetic mean of all sampling results; the quantiles are selected at the 2.5% and 97.5% quantiles to represent the upper and lower limits of the risk distribution; and the confidence interval is determined based on the 2.5% and 97.5% quantiles. For example, the mean of the contact infection probability obtained after summarizing is... The 2.5% quantile is The 97.5th percentile is The corresponding 95% confidence interval is [ , The mean probability of inhalation infection was 0.032, and the 2.5th percentile was... The 97.5th percentile is 0.18, and the corresponding 95% confidence interval is [ ]. [0.18]; The mean of the overall infection risk was 0.0325, and the 2.5th percentile was The 97.5th percentile is 0.182, and the corresponding 95% confidence interval is […]. [0.182], and output these statistics.

[0146] In another specific implementation, the number of samplings can be adjusted according to the required accuracy of the evaluation. For example, if the accuracy requirement is high, the number of samplings can be increased to 50,000, or the probability distribution type can be changed according to the actual distribution characteristics of the parameters. The core calculation process is the same as above, only the sampling size or distribution setting is different.

[0147] This application introduces Monte Carlo simulation to characterize the randomness of parameters and combines coupled calculations to obtain comprehensive risks and statistics, making the risk assessment results more in line with the uncertainty of actual scenarios and providing a comprehensive and robust reference for prevention and control decisions.

[0148] S106. Based on the statistical value of the comprehensive infection risk and the preset risk threshold, generate control parameters for the ventilation system, and control the indoor ventilation system according to the control parameters to adjust the operating parameters.

[0149] Among them, the preset risk threshold is the upper limit of infection risk set according to the epidemic prevention requirements and safety standards of the indoor environment, which is used to determine whether the current risk is within an acceptable range; the control parameters are the specific set values ​​that guide the operation of the ventilation system, covering key indicators that affect the ventilation effect; the control commands are operation signals that can be directly recognized and executed by the ventilation system, which are used to realize the automatic adjustment of operating parameters.

[0150] Optionally, step S106 may specifically include the following steps:

[0151] S1061. Under the constraint that the statistical quantity of the comprehensive infection risk does not exceed the preset risk threshold, the control parameters of the ventilation system are generated with at least one of the following as the optimization target: energy consumption index, pressure difference fluctuation index, or noise index. The control parameters include at least one of the following: air change rate setpoint, supply and exhaust air volume setpoint, pressure difference setpoint, supply and return air ratio setpoint, air outlet condition setpoint, or operating period strategy.

[0152] Among them, the energy consumption index refers to the total amount of energy consumed during the operation of the ventilation system, the pressure difference fluctuation index refers to the fluctuation range of the indoor and outdoor pressure difference from the set value, and the noise index refers to the sound intensity generated during the operation of the ventilation system. The optimization goal is to achieve the optimal state of energy consumption, pressure difference fluctuation, or noise while meeting the risk control requirements.

[0153] S1062. Convert the control parameters into control commands for the indoor ventilation system, and send the control commands to the indoor ventilation system through the ventilation system interface, so that the indoor ventilation system can automatically adjust its operating parameters to reduce the overall risk of infection.

[0154] The ventilation system interface is a communication channel connecting the computing device and the ventilation system, used to transmit control commands. Automatic adjustment of operating parameters means that the ventilation system automatically changes its operating status, such as air volume and differential pressure, according to the received control commands, without manual intervention.

[0155] In one specific implementation, this step first defines a preset risk threshold to ensure that the statistical value of the overall infection risk does not exceed the threshold. Then, under this constraint, with at least one of the following indicators—energy consumption, pressure fluctuation, or noise—as the optimization target, and considering the operating characteristics of the ventilation system in the negative pressure ward and the indoor environmental requirements, ventilation system control parameters are generated, covering air exchange rate, supply and exhaust air volume, pressure difference, supply and return air ratio, vent conditions, or operating time strategies. Subsequently, the generated control parameters are converted into directly identifiable and executable control commands according to the ventilation system's communication protocol and sent to the ventilation system controller in the negative pressure ward through the ventilation system interface. This ultimately achieves the complete process of automatic adjustment of ventilation system operating parameters. The specific application scenario using a negative pressure ward is explained below:

[0156] First, control parameters are generated via S1061. A preset risk threshold is defined to ensure that the statistical value of the overall infection risk does not exceed this threshold. Simultaneously, a multi-objective optimization model is constructed with the goal of minimizing energy consumption. The core logic is to minimize the energy consumption of the ventilation system while ensuring the overall infection risk does not exceed the preset risk threshold. Ventilation system energy consumption is affected by factors such as supply air volume, exhaust air volume, and indoor-outdoor pressure difference. For example, the preset risk threshold for negative pressure wards is 0.05, while the current average overall infection risk is 0.06, exceeding the threshold. Through the optimization model calculation, control parameters are generated: the air exchange rate setpoint is adjusted from 6 times / hour to 8 times / hour, and the supply air volume setpoint is adjusted from 500... Adjusted to 650 The exhaust volume setting is from 550 Adjusted to 720 The differential pressure setpoint remains unchanged at -15 Pa. The above example is merely one example of this application. In practical applications, the type and value of control parameters can be adjusted according to different optimization objectives and scenario requirements. This application does not limit this.

[0157] Secondly, control commands are sent via S1062 to adjust the ventilation system. The generated air volume is 650... Exhaust volume 720 Control parameters are converted into corresponding control commands according to the ventilation system's communication protocol. For example, the air supply volume parameter is converted into a frequency converter command, and the differential pressure parameter is converted into a valve opening command. These control commands are sent to the ventilation system controller in the negative pressure ward through the ventilation system interface. After receiving the commands, the controller drives the fan to adjust its speed to change the supply and exhaust air volume, and at the same time adjusts the valve opening to maintain a stable differential pressure, so that the ventilation system operates according to the new control parameters, thereby reducing the concentration of pathogens in the room and ultimately reducing the overall risk of infection.

[0158] In another specific implementation, the noise index can be minimized as the optimization objective. In this case, the optimization model will prioritize adjusting the air outlet operating condition setpoint or runtime strategy to reduce the noise generated by the ventilation system, provided that the risk threshold is met. The core process is the same as above, only the optimization objective and the corresponding control parameter adjustment direction are different.

[0159] This application achieves dynamic prevention and control of infection risk through a closed-loop linkage between risk and ventilation control. It ensures that the risk is controllable while taking into account the system's operational efficiency, providing automated and precise technical support for indoor epidemic prevention.

[0160] Optionally, before obtaining the multidimensional environmental parameters of the target room, the following steps are also included:

[0161] The system receives real-time data from monitoring points of the indoor ventilation system via a ventilation system interface. The real-time data includes at least one of temperature, humidity, pressure difference, air volume, and wind speed. Based on the real-time data, the boundary condition parameters in the multidimensional environmental parameters are updated.

[0162] Among them, boundary condition parameters are key data describing the boundary state of the indoor environment, used to support the subsequent construction of the concentration field and risk calculation. These include parameters related to the operation of the ventilation system and the indoor environment, such as temperature, humidity, pressure difference, air volume, and wind speed. Real-time data from monitoring points are indoor environment and system operation data continuously collected by the ventilation system through sensors and other devices, which can reflect the true state of the current environment.

[0163] In one specific implementation, a communication connection is established with the indoor ventilation system via a ventilation system interface to receive real-time data collected from monitoring points of the ventilation system. This data includes key information such as temperature, humidity, pressure difference, air volume, and wind speed. This real-time data is directly used to update the boundary condition parameters in the multi-dimensional environmental parameters, ensuring that the basic data upon which subsequent concentration field construction and risk calculation rely are consistent with the actual indoor environmental conditions, thereby improving the accuracy of the assessment results. The above example is merely one example of this application. In practical applications, the type of real-time data received can be adjusted according to the monitoring capabilities of the ventilation system, and this application does not limit this.

[0164] Figure 3 This is a schematic diagram illustrating a specific implementation of a quantitative assessment and closed-loop control system for indoor pathogen aerosol transmission risk, provided in this application embodiment. (Refer to...) Figure 3 The system may include:

[0165] The ventilation system data interface module 31 is used to obtain operating parameters and real-time data from monitoring points from the indoor ventilation system, and to send control commands to the indoor ventilation system.

[0166] Data acquisition module 32 is used to acquire multi-dimensional environmental parameters of the target room and store the multi-dimensional environmental parameters in the memory of the computing device;

[0167] The concentration field construction module 33 is used to establish a computing domain in the computing device based on the multidimensional environmental parameters, calculate the airborne pathogen concentration field and the surface pathogen concentration field respectively, and extract the respiratory zone concentration from the airborne pathogen concentration field.

[0168] The contact risk calculation module 34 is used to calculate the contact route intake dose based on the pathogen concentration field on the object surface and the personnel activity parameters in the multidimensional environmental parameters, and input the contact route intake dose into a preset surface risk model to obtain the contact infection probability.

[0169] The inhalation risk calculation module 35 is used to calculate the deposition dose of each region of the human respiratory tract based on the concentration in the breathing zone and the protective equipment parameters in the multidimensional environmental parameters, and input the deposition dose into the preset inhalation risk model to obtain the probability of inhalation infection.

[0170] The comprehensive assessment module 36 is used to perform a comprehensive risk assessment based on the contact infection probability and the inhalation infection probability through coupled calculation, and output the statistics of the contact infection probability, the inhalation infection probability and the comprehensive infection risk.

[0171] The control output module 37 is used to generate control parameters for the ventilation system based on the statistics of the comprehensive infection risk and the preset risk threshold, and to control the indoor ventilation system to adjust the operating parameters according to the control parameters.

[0172] The indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control system of this application embodiment is used to implement the aforementioned indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control method. Therefore, the specific implementation of the indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control system can be seen in the embodiment section of the indoor pathogen aerosol transmission risk quantitative assessment and closed-loop control method above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0173] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk.

[0174] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk.

[0175] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0176] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in the embodiments of the above-described method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk.

[0177] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0178] The above provides a detailed description of the method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk, characterized in that, include: Acquire multidimensional environmental parameters of the target room and store the multidimensional environmental parameters in the memory of the computing device; Based on the multidimensional environmental parameters, a computational domain is established in the computing device to calculate the airborne pathogen concentration field and the surface pathogen concentration field, and to extract the respiratory zone concentration from the airborne pathogen concentration field. Based on the pathogen concentration field on the object surface, combined with the human activity parameters in the multidimensional environmental parameters, the dose ingested via the contact route is calculated, and the dose ingested via the contact route is input into a preset surface risk model to obtain the probability of contact infection. Based on the concentration in the respiratory zone and the protective equipment parameters in the multidimensional environmental parameters, the deposition dose in each region of the human respiratory tract is calculated, and the deposition dose is input into a preset inhalation risk model to obtain the probability of inhalation infection. Based on the contact infection probability and the inhalation infection probability, a comprehensive risk assessment is performed through coupled calculation, and the statistics of the contact infection probability, the inhalation infection probability, and the comprehensive infection risk are output. Based on the statistical data of the comprehensive infection risk and the preset risk threshold, control parameters for the ventilation system are generated, and the indoor ventilation system is controlled according to the control parameters to adjust the operating parameters.

2. The method according to claim 1, characterized in that, Based on the aforementioned multidimensional environmental parameters, a computational domain is established in the computing device to calculate the airborne pathogen concentration field and the surface pathogen concentration field, including: Based on the spatial layout parameters in the multidimensional environmental parameters, a computing domain is established in the computing device; Based on the ventilation system operating parameters and boundary condition parameters in the multidimensional environmental parameters, the computational domain is discretized into a grid, and corresponding boundary conditions are set according to the air supply outlets, exhaust outlets, pressure differentials, or leakage channels in the ventilation system. Using the boundary conditions as constraints, and by coupling pathogen characteristic parameters in the multidimensional environmental parameters, the aerosol convection, diffusion, transport, and deposition processes are simulated to obtain the preliminary concentration field of pathogens in the air and on object surfaces. The characteristics of pathogen activity decaying over time are further incorporated into the initial concentration field to obtain an airborne pathogen concentration field and an object surface pathogen concentration field that include the decay effect.

3. The method according to claim 1, characterized in that, Based on the pathogen concentration field on the object surface, and combined with the human activity parameters in the multidimensional environmental parameters, the exposure dose is calculated, and the exposure dose is input into a preset surface risk model to obtain the contact infection probability, including: Based on the pathogen concentration field on the object surface, a transfer model between the surface and the hand based on the concentration gradient is used to update the pathogen surface density on the hand after contact. The update needs to refer to the pathogen surface density on the hand before contact and the pathogen concentration on the object surface in the contact area. Combining the human activity parameters in the multidimensional environmental parameters, based on the updated hand pathogen areal density, and in conjunction with the transfer characteristics between the surface and the hand, the transfer characteristics between the hand and the mucous membrane, the contact area, the contact frequency between the hand and the mucous membrane, the exposure duration, and the pathogen decay characteristics, the dose ingested via the contact route is calculated. The portion of the ingested dose that is transferred to the facial mucosa via the contact route is substituted into a preset surface risk model to obtain the probability of contact infection; wherein, the preset surface risk model is in exponential form and requires the import of response-related parameters corresponding to the target pathogen during construction.

4. The method according to claim 1, characterized in that, Based on the concentration in the respiratory zone and the protective equipment parameters in the multidimensional environmental parameters, the deposition dose in each region of the human respiratory tract is calculated, and the deposition dose is input into a preset inhalation risk model to obtain the probability of inhalation infection, including: The human respiratory tract is divided into at least three regions: the upper respiratory tract, trachea and bronchi, and alveoli. The filtration effect is determined based on the protective equipment parameters in the multidimensional environmental parameters. The filtration effect is the product of the efficiency of the filter material itself and the tightness correction coefficient. Based on the concentration in the respiratory zone and the filtration effect, combined with the respiratory ventilation and the deposition characteristics of each region of the human respiratory tract, the deposition dose corresponding to each region is calculated respectively. The deposition dose of each region is substituted into a preset inhalation risk model to obtain the probability of inhalation infection. The preset inhalation risk model is in the form of a partitioned superposition. When constructing it, the corresponding reaction-related parameters of each respiratory region need to be imported. The superposition calculation is performed by superimposing the correlation results of the deposition dose of each region with the corresponding reaction-related parameters.

5. The method according to claim 1, characterized in that, Based on the contact infection probability and the inhalation infection probability, a comprehensive risk assessment is performed through coupled calculations, and the statistics of the contact infection probability, the inhalation infection probability, and the comprehensive infection risk are output, including: From the multidimensional environmental parameters and the relevant calculation parameters of the contact route and inhalation route, some parameters are selected as random variables. The random variables include at least one or more of the following: surface-to-hand transfer characteristics, hand-to-mucous membrane transfer characteristics, hand-to-mucous membrane contact frequency, pathogen attenuation-related characteristics, surface risk model response-related parameters, and inhalation risk model zonal response-related parameters. A probability distribution is set for the random variable, and sampling is performed according to the preset probability distribution using the Monte Carlo simulation method; For each combination of parameters obtained from sampling, the ingested dose via the contact route, the probability of contact infection, the deposition dose in each area, and the probability of inhalation infection are calculated sequentially. Then, the comprehensive infection risk corresponding to a single sampling is obtained through coupled calculation. Summarize the contact infection probability, inhalation infection probability, and overall infection risk corresponding to all samples, calculate and output the statistics of the contact infection probability, the inhalation infection probability, and the overall infection risk, whereby the statistics include the mean, quantiles, or confidence intervals.

6. The method according to claim 1, characterized in that, Based on the statistical measures of the comprehensive infection risk and the preset risk threshold, control parameters for the ventilation system are generated, and the indoor ventilation system is controlled according to the control parameters to adjust the operating parameters, including: Under the constraint that the statistical quantity of the comprehensive infection risk does not exceed the preset risk threshold, the control parameters of the ventilation system are generated with at least one of the following as the optimization target: energy consumption index, pressure difference fluctuation index, or noise index. The control parameters include at least one of the following: air change rate set value, supply and exhaust air volume set value, pressure difference set value, supply and return air ratio set value, air outlet condition set value, or operating period strategy. The control parameters are converted into control commands for the indoor ventilation system, and the control commands are sent to the indoor ventilation system through the ventilation system interface, so that the indoor ventilation system can automatically adjust its operating parameters to reduce the overall risk of infection.

7. The method according to claim 1, characterized in that, Before obtaining the multidimensional environmental parameters of the target room, the following steps are also included: The system receives real-time data from monitoring points of the indoor ventilation system via a ventilation system interface. The real-time data includes at least one of temperature, humidity, pressure difference, air volume, and wind speed. Based on the real-time data, the boundary condition parameters in the multidimensional environmental parameters are updated.

8. A quantitative assessment and closed-loop control system for indoor pathogen aerosol transmission risk, characterized in that, include: The ventilation system data interface module is used to obtain operating parameters and real-time data from monitoring points of the indoor ventilation system, and to send control commands to the indoor ventilation system. The data acquisition module is used to acquire multi-dimensional environmental parameters of the target room and store the multi-dimensional environmental parameters in the memory of the computing device; The concentration field construction module is used to establish a computing domain in the computing device based on the multidimensional environmental parameters, calculate the airborne pathogen concentration field and the surface pathogen concentration field respectively, and extract the respiratory zone concentration from the airborne pathogen concentration field. The contact risk calculation module is used to calculate the exposure dose based on the pathogen concentration field on the object surface and the human activity parameters in the multidimensional environmental parameters, and input the exposure dose into a preset surface risk model to obtain the contact infection probability. The inhalation risk calculation module is used to calculate the deposition dose of each region of the human respiratory tract based on the concentration in the breathing zone and the protective equipment parameters in the multidimensional environmental parameters, and input the deposition dose into a preset inhalation risk model to obtain the probability of inhalation infection. The comprehensive assessment module is used to perform a comprehensive risk assessment based on the contact infection probability and the inhalation infection probability through coupled calculation, and output the statistics of the contact infection probability, the inhalation infection probability and the comprehensive infection risk; The control output module is used to generate control parameters for the ventilation system based on the statistics of the comprehensive infection risk and the preset risk threshold, and to control the indoor ventilation system according to the control parameters to adjust the operating parameters.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for quantitative assessment and closed-loop control of indoor pathogen aerosol transmission risk as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the quantitative assessment and closed-loop control method for indoor pathogen aerosol transmission risk as described in any one of claims 1 to 7.

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