AI assessment system and method for infection in patients with respiratory infectious diseases based on environmental analysis

Abstract

The present application relates to the field of infectious disease early warning technology, and in particular to an AI assessment system and method for respiratory infectious disease patient infection based on environmental analysis. The method comprises the following steps: analyzing the effects of air pollutants and pathogenic microorganisms in a target area on the physiological state of the human respiratory tract based on air pollution data and historical infection data, and assessing the susceptibility risk of regional personnel; analyzing the characteristics of air pollutants in the target area and the characteristics of the wind environment under the influence of the urban built environment based on multi-source environmental data, and assessing the regional virus spread risk; constructing an infection risk assessment model that combines the regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict the regional personnel infection risk and provide infection risk warnings. By analyzing the interaction mechanism between air pollutants and pathogenic microorganisms, the present application effectively improves the accuracy and reliability of infectious disease infection risk warnings.

Description

Technical Field

[0001] The present application relates to the field of infectious disease early warning technology, and in particular to an AI assessment system and method for respiratory infectious disease patient infection based on environmental analysis. Background Art

[0002] Respiratory infectious diseases are a type of disease in which pathogenic microorganisms invade the human respiratory tract through airborne transmission and cause infection. They are characterized by rapid transmission speed and easy large-scale outbreaks. With the increase in population mobility and urban density, the spread of respiratory infectious diseases has shown more complex environmental dependence and spatiotemporal dynamics.

[0003] Traditional respiratory infectious disease infection risk warnings mainly rely on case reports, epidemiological characteristic analysis, and statistical modeling of meteorological conditions, ignoring the impact of multi-source environmental data on the spread of pathogenic microorganisms. Different types of pollutants may significantly affect the susceptibility of the population by enhancing or inhibiting the infectivity of pathogenic microorganisms in the air. At the same time, pathogenic microorganisms change their diffusion range by attaching to the surface of air pollutants. For example, pollutants with smaller particle size and larger specific surface area can provide more adsorption sites, increase the probability of pathogenic microorganisms attaching, thereby prolonging their stay time and enhancing their long-distance diffusion ability in urban breeze environments. Therefore, existing technologies lack in-depth characterization of urban microenvironment changes, air pollutant characteristics, and the interaction mechanism between pathogenic microorganisms and pollutants, limiting the accuracy and reliability of warnings for respiratory infectious disease risk areas. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, this application provides an AI assessment system and method for respiratory infectious disease patients based on environmental analysis. By analyzing the interaction mechanism between air pollutants and pathogenic microorganisms, it effectively improves the accuracy and reliability of infectious disease infection risk warning.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides an AI assessment method for respiratory infectious disease patient infection based on environmental analysis, comprising the following steps:

[0007] Acquire multi-source environmental data within the target area, as well as regional human behavior data and historical infection data. Multi-source environmental data includes meteorological data, air pollution data, and urban built environment data.

[0008] Analyze the impact of air pollutants and pathogenic microorganisms on human respiratory tract physiological status in the target area based on air pollution data and historical infection data, and assess the susceptibility risk of regional personnel;

[0009] Analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the risk of regional virus spread;

[0010] An infection risk assessment model is constructed to combine regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and provide infection risk warnings.

[0011] Optionally, the assessment of susceptibility risk of regional personnel includes:

[0012] Using air pollution data, we construct a pollutant concentration matrix and an interaction factor matrix between air pollutants and pathogenic microorganisms. The interaction factor matrix is ​​used to describe whether air pollutants enhance or inhibit the stability and invasiveness of pathogenic microorganisms.

[0013] The infection activity enhancement matrix of pathogenic microorganisms is obtained by multiplying the pollutant concentration matrix and the interaction factor matrix;

[0014] Analyze the degree of damage caused by pathogenic microorganisms to the physiological functions of the human respiratory tract through historical infection data and construct a physiological damage effect matrix;

[0015] The regional personnel susceptibility risk index is calculated by combining the infection activity enhancement matrix with the physiological damage effect matrix. The regional personnel susceptibility risk index is used to assess the regional personnel susceptibility risk.

[0016] Optionally, the assessing regional virus spread risk includes:

[0017] Analyze the differences in air pollutant types within the target area based on air pollution data and evaluate the characteristics of air pollutants based on differences in their adsorption capacity;

[0018] Evaluate the wind environment characteristics under the influence of urban built environment in the target area based on meteorological data and urban built environment data;

[0019] The wind environment characteristics are combined with the air pollutant characteristics to calculate the virus diffusion environmental adaptability index, which is used to assess the regional virus diffusion risk.

[0020] Optionally, the evaluating of air pollutant characteristics comprises:

[0021] Obtain air pollution data, which includes pollutant type, pollutant volume concentration, pollutant specific surface area and pollutant particle size;

[0022] The ratio of the total surface area of ​​pollutants per unit volume of air to the baseline value of the total surface area of ​​pollutants per unit volume of air is taken as the virus attachment coefficient. The total surface area of ​​pollutants per unit volume of air is the product of the volume concentration of pollutants and the specific surface area of ​​pollutants.

[0023] The particle size matching coefficient between pollutants and viruses is determined by analyzing the distribution characteristics of the particle size of pollutants and virus particles:

[0024] ;

[0025] In the formula Indicates air pollutants The median particle size, Indicates the characteristic particle size of the virus. represents the standard deviation, Indicates the particle size matching coefficient between air pollutants and viruses;

[0026] The product of the virus attachment coefficient of the pollutant and the particle size matching coefficient is used as the binding potential coefficient between the pollutant and the virus, and the average binding potential coefficient of all types of pollutants in the target area is used as the air pollutant characteristic index in the target area to quantify the potential ability of pollutant particles to carry viruses.

[0027] Optionally, the wind environment characteristics under the influence of the urban built environment in the assessment target area include:

[0028] Analyze the building distribution characteristics in the target area through urban built environment data and calculate the sky openness of the target area;

[0029] The wind speed standard deviation and wind speed mean in the target area are extracted through meteorological data, and the ratio of wind speed standard deviation to wind speed mean is used as the turbulence intensity in the target area;

[0030] The product of sky openness and turbulence intensity is used as the wind environment characteristic index in the target area. The wind environment characteristic index evaluates the ventilation status under the influence of the urban built environment in the target area.

[0031] Optionally, the infection risk warning includes:

[0032] The regional personnel infection risk index is obtained through the infection risk assessment model. When the regional personnel infection risk index is greater than or equal to the preset regional personnel infection risk threshold, an infection risk warning is issued; when the regional personnel infection risk index is less than the preset regional personnel infection risk threshold, an infection risk warning is issued.

[0033] In a second aspect, the present application provides an AI assessment system for respiratory infectious disease patients based on environmental analysis, including:

[0034] The data acquisition module is used to obtain multi-source environmental data in the target area, as well as regional personnel behavior data and historical infection data. The multi-source environmental data includes meteorological data, air pollution data, and urban built environment data;

[0035] The susceptibility risk assessment module is used to analyze the impact of air pollutants and pathogenic microorganisms in the target area on the physiological state of the human respiratory tract based on air pollution data and historical infection data, and to assess the susceptibility risk of regional personnel;

[0036] The diffusion risk assessment module is used to analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the regional virus diffusion risk;

[0037] The infection risk warning module is used to build an infection risk assessment model to combine regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and provide infection risk warning;

[0038] The control module is used to control the operation of the data acquisition module, the susceptibility risk assessment module, the spread risk assessment module and the infection risk warning module.

[0039] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an AI assessment method for infection of patients with respiratory infectious diseases based on environmental analysis by calling the computer program stored in the memory.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute an AI assessment method for infection in patients with respiratory infectious diseases based on environmental analysis.

[0041] Compared with the prior art, this application has the following advantages and beneficial effects:

[0042] This application systematically analyzes the interaction mechanism between air pollutants and pathogenic microorganisms by integrating multi-source environmental data, and then quantifies the enhancing effect of pollutants on the activity of pathogenic microorganisms, the degree of damage to the human respiratory defense function, and the ability of viruses to spread in complex urban environments, effectively improving the accuracy and reliability of respiratory infectious disease infection risk warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0044] Figure 1 This is a schematic diagram of the overall process of the AI ​​assessment method for respiratory infectious disease patients based on environmental analysis provided in an embodiment of the present application;

[0045] Figure 2Schematic diagram of the structure of an AI assessment system for respiratory infectious disease patients based on environmental analysis provided in an embodiment of the present application;

[0046] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0048] See Figure 1 , Figure 1 This is a schematic diagram of the overall process of the AI ​​assessment method for respiratory infectious disease patients based on environmental analysis provided in an embodiment of the present application, which specifically includes the following steps:

[0049] S110: Acquire multi-source environmental data in the target area, as well as regional personnel behavior data and historical infection data. The multi-source environmental data includes meteorological data, air pollution data, and urban built environment data.

[0050] S120: Analyze the impact of air pollutants and pathogenic microorganisms on human respiratory physiological status in the target area based on air pollution data and historical infection data, and assess the susceptibility risk of regional personnel;

[0051] By constructing a pollutant concentration matrix and a matrix of interaction factors between pollutants and pathogens, we can effectively quantify the synergistic and antagonistic effects of environmental pollution on pathogen infection activity. Furthermore, we can construct a physiological damage matrix based on the degree of damage to respiratory physiological function by pathogens. This matrix ultimately forms a regional susceptibility risk index. By building a chain from pollution exposure to pathogen enhancement to physiological vulnerability, we can systematically assess the infection vulnerability faced by the regional population under current environmental conditions and evaluate the susceptibility risk of regional personnel, including:

[0052] A pollutant concentration matrix and an interaction factor matrix between air pollutants and pathogenic microorganisms are constructed using air pollution data. The interaction factor matrix is ​​used to describe whether air pollutants enhance or inhibit the stability and invasiveness of pathogenic microorganisms. In one embodiment of the present application, the interaction factor matrix can be:

[0053] ;

[0054] In the formula Indicates air pollutants and pathogenic microorganisms The interaction factors can be obtained by fitting the population activity of pathogenic microorganisms under different air pollutant exposures. , indicating that air pollutants synergistically enhance the activity of pathogenic microorganisms. , indicating that air pollutants inhibit the activity of pathogenic microorganisms and weaken their infection ability. Represents the interaction factor matrix between air pollutants and pathogenic microorganisms. The interaction factor matrix is ​​used to describe the regulatory effect of pollutants on the invasiveness or infection potential of pathogenic microorganisms;

[0055] The infection activity enhancement matrix of pathogenic microorganisms is obtained by multiplying the pollutant concentration matrix with the interaction factor matrix. The infection activity enhancement matrix is ​​used to quantify the combined effect of pollutant concentration and its enhancement or inhibition of microbial infectivity, reflecting the indirect effect of polluted environment on infection risk.

[0056] The degree of damage caused by pathogenic microorganisms to the physiological functions of the human respiratory tract is analyzed through historical infection data, and a physiological damage effect matrix is ​​constructed. The physiological damage effect matrix reflects the intensity of the impact of specific pathogenic microorganisms on key defense functions such as mucosal barriers, airway patency, and immune stability.

[0057] The regional personnel susceptibility risk index is calculated by combining the infection activity enhancement matrix with the physiological damage effect matrix. That is, by performing matrix multiplication of the infection activity enhancement matrix and the physiological damage effect matrix, the regulatory effect of pollutants on microbial activity is transmitted to the damage intensity of respiratory physiological function, and the regional personnel susceptibility risk index is output after weighted aggregation. The regional personnel susceptibility risk index is used to assess the regional personnel susceptibility risk, that is, the physiological vulnerability of the regional population to infection by specific pathogens under the current environmental pollution level.

[0058] S130: Analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the risk of regional virus spread;

[0059] By integrating air pollution data, meteorological data, and urban built environment data, we systematically assess the ability of viruses to spread in specific regional environments. We construct a virus diffusion environmental adaptability index to quantify the potential for pathogen spread under the combined influence of current pollutant characteristics and wind conditions. This index not only reveals the supporting role of pollutants as virus carriers in the diffusion pathway, but also reflects the regulatory effect of urban spatial morphology on local wind fields and particle migration. This allows us to assess regional virus spread risks, including:

[0060] Analyze the differences in air pollutant types within the target area based on air pollution data and evaluate the characteristics of air pollutants based on differences in their adsorption capacity;

[0061] Evaluate the wind environment characteristics under the influence of urban built environment in the target area based on meteorological data and urban built environment data;

[0062] The virus diffusion environment adaptability index is calculated by combining wind environment characteristics with air pollutant characteristics. The virus diffusion environment adaptability index is used to assess the regional virus diffusion risk;

[0063] Airborne viruses often spread by attaching to the surface of particulate matter. Therefore, the type, particle size, and surface area of ​​the pollutant determine its potential to carry viruses. For example, viruses are highly susceptible to attaching to PM2.5 or ultrafine pollutant particles in the air. Assessing the characteristics of air pollutants includes:

[0064] Obtain air pollution data, which includes pollutant type, pollutant volume concentration, pollutant specific surface area and pollutant particle size;

[0065] The ratio of the total surface area of ​​pollutants per unit volume of air to the baseline value of the total surface area of ​​pollutants per unit volume of air is taken as the virus attachment coefficient. The total surface area of ​​pollutants per unit volume of air is the product of the volume concentration of pollutants and the specific surface area of ​​pollutants.

[0066] The particle size matching coefficient between pollutants and viruses is determined by analyzing the distribution characteristics of the particle size of pollutants and virus particles:

[0067] ;

[0068] In the formula Indicates air pollutants The median particle size, Indicates the characteristic particle size of the virus, Represents the standard deviation, which is used to control the sensitivity of the virus adsorption efficiency of pollutant particles to the particle size deviation. ,in, is the number of pollutant particle size samples when the adsorption efficiency drops to 50% of the peak value. Since the exponential decay is more consistent with the adsorption drop characteristics caused by particle size deviation, the exponential function is used to calculate the adsorption efficiency. Describe the particle size matching effect between pollutants and viruses, that is, viruses are more likely to attach to pollutant particles with similar particle sizes. At the same time, because pollutant particles that are too large or too small will lead to increased particle size deviation, logarithmic transformation is used Make the effects of the two cases symmetrical, Indicates the particle size matching coefficient between air pollutants and viruses;

[0069] The product of the virus attachment coefficient of the pollutant and the particle size matching coefficient is used as the binding potential coefficient of the pollutant and the virus, and the average binding potential coefficient of all types of pollutants in the target area is used as the air pollutant characteristic index in the target area to quantify the potential ability of pollutant particles to carry viruses;

[0070] The ventilation capacity and air flow characteristics of the target area are quantitatively assessed by constructing a wind environment characteristic index. The sky openness index reflects the degree to which urban spatial form restricts air flow, while turbulence intensity characterizes the ability of wind speed changes to promote pollutant diffusion. The two are coupled to calculate the wind environment characteristic index, thereby characterizing the promoting or inhibiting effect of urban space on the diffusion of air pollutants. This assessment also assesses the wind environment characteristics within the target area under the influence of the urban built environment, including:

[0071] The building distribution characteristics in the target area are analyzed by using urban built environment data, and the sky openness of the target area is calculated. In one embodiment of the present application, the sky openness can also be obtained by using a raster calculation model acquisition method, a fisheye lens hemispherical photo acquisition method, and a vector calculation model acquisition method.

[0072] The wind speed standard deviation and wind speed mean in the target area are extracted through meteorological data, and the ratio of wind speed standard deviation to wind speed mean is used as the turbulence intensity in the target area;

[0073] The product of sky openness and turbulence intensity is used as the wind environment characteristic index in the target area. The wind environment characteristic index evaluates the ventilation status under the influence of the urban built environment in the target area. In one embodiment of the present application, the wind environment characteristic index can be:

[0074] ;

[0075] In the formula represents the standard deviation of wind speed in the target area, represents the average wind speed in the target area, Indicates the turbulence intensity in the target area. High turbulence intensity can promote the diffusion of air pollutants. Indicates the The terrain affects the altitude angle at azimuth angles. Indicates the number of azimuth angles, Indicates the sky openness of the target area. Sky openness is an indicator that quantitatively describes the openness of urban form. The value of sky openness is between 0 and 1. When the value of sky openness is close to 1, the sky openness at the observation point is greater. When the value of sky openness is close to 0, the sky openness at the observation point is smaller. As the sky openness decreases, the regional ventilation capacity also gradually decreases, which will lead to a weakening of the diffusion capacity of air pollutants. Represents the wind environment characteristic index.

[0076] S140: Construct an infection risk assessment model that combines regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and provide infection risk warnings;

[0077] By organically integrating regional personnel susceptibility risk, virus spread risk, and personnel behavior data, and then combining it with the infection risk assessment model to predict regional personnel infection risk and implement dynamic infection risk warning based on set thresholds, including:

[0078] The regional personnel infection risk index is obtained through the infection risk assessment model. When the regional personnel infection risk index is greater than or equal to the preset regional personnel infection risk threshold, an infection risk warning is issued; when the regional personnel infection risk index is less than the preset regional personnel infection risk threshold, an infection risk warning is issued. The infection risk assessment model is constructed based on the long short-term memory neural network.

[0079] In one embodiment of the present application, the method for determining setting parameters such as weighted weights and preset regional personnel infection risk thresholds can be: by constructing a data set by acquiring multi-source environmental data, regional personnel behavior data, and historical infection data, calculating the regional personnel susceptibility risk index and obtaining the regional personnel infection risk index through an infection risk assessment model, and at the same time obtaining the expert's judgment results on the regional personnel susceptibility risk and the regional personnel infection risk, importing the obtained regional personnel susceptibility risk index, regional personnel infection risk index, and judgment results into the fitting software, and outputting the weighted weights and preset regional personnel infection risk thresholds that meet the maximum judgment accuracy.

[0080] See Figure 2 , Figure 2 This is a schematic diagram of the structure of an AI assessment system for respiratory infectious disease patients based on environmental analysis provided in an embodiment of the present application. This embodiment provides an AI assessment system for respiratory infectious disease patients based on environmental analysis, including:

[0081] Data acquisition module 210, for acquiring multi-source environmental data in the target area, as well as regional personnel behavior data and historical infection data. The multi-source environmental data includes meteorological data, air pollution data, and urban built environment data;

[0082] Susceptibility risk assessment module 220, for analyzing the impact of air pollutants and pathogenic microorganisms on the physiological state of the human respiratory tract in the target area based on air pollution data and historical infection data, and assessing the susceptibility risk of people in the area;

[0083] Diffusion risk assessment module 230 is used to analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the regional virus diffusion risk;

[0084] Infection risk warning module 240, used to build an infection risk assessment model to combine regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and issue infection risk warnings;

[0085] The control module 250 is used to control the operation of the data acquisition module, the susceptibility risk assessment module, the spread risk assessment module and the infection risk warning module.

[0086] In one embodiment of the present application, the susceptibility risk assessment module 220 is used to analyze the impact of air pollutants and pathogenic microorganisms on the physiological state of the human respiratory tract in the target area based on air pollution data and historical infection data, and assess the susceptibility risk of people in the area, including:

[0087] Using air pollution data, we construct a pollutant concentration matrix and an interaction factor matrix between air pollutants and pathogenic microorganisms. The interaction factor matrix is ​​used to describe whether air pollutants enhance or inhibit the stability and invasiveness of pathogenic microorganisms.

[0088] The infection activity enhancement matrix of pathogenic microorganisms is obtained by multiplying the pollutant concentration matrix and the interaction factor matrix;

[0089] Analyze the degree of damage caused by pathogenic microorganisms to the physiological functions of the human respiratory tract through historical infection data and construct a physiological damage effect matrix;

[0090] The regional personnel susceptibility risk index is calculated by combining the infection activity enhancement matrix with the physiological damage effect matrix. The regional personnel susceptibility risk index is used to assess the regional personnel susceptibility risk.

[0091] In one embodiment of the present application, the spread risk assessment module 230 is used to analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the regional virus spread risk, including:

[0092] Analyze the differences in air pollutant types within the target area based on air pollution data and evaluate the characteristics of air pollutants based on differences in their adsorption capacity;

[0093] Evaluate the wind environment characteristics under the influence of urban built environment in the target area based on meteorological data and urban built environment data;

[0094] The virus diffusion environment adaptability index is calculated by combining wind environment characteristics with air pollutant characteristics. The virus diffusion environment adaptability index is used to assess the regional virus diffusion risk;

[0095] Among them, the assessment of air pollutant characteristics includes: obtaining air pollution data, which includes pollutant type, pollutant volume concentration, pollutant specific surface area and pollutant particle size;

[0096] The ratio of the total surface area of ​​pollutants per unit volume of air to the baseline value of the total surface area of ​​pollutants per unit volume of air is taken as the virus attachment coefficient. The total surface area of ​​pollutants per unit volume of air is the product of the volume concentration of pollutants and the specific surface area of ​​pollutants.

[0097] The particle size matching coefficient between pollutants and viruses is determined by analyzing the distribution characteristics of pollutant particle size and virus particle size;

[0098] The product of the virus attachment coefficient of the pollutant and the particle size matching coefficient is used as the binding potential coefficient between the pollutant and the virus, and the average binding potential coefficient of all types of pollutants in the target area is used as the air pollutant characteristic index in the target area to quantify the potential ability of pollutant particles to carry viruses.

[0099] Assess the wind environment characteristics under the influence of the urban built environment in the target area, including:

[0100] Analyze the building distribution characteristics in the target area through urban built environment data and calculate the sky openness of the target area;

[0101] The wind speed standard deviation and wind speed mean in the target area are extracted through meteorological data, and the ratio of wind speed standard deviation to wind speed mean is used as the turbulence intensity in the target area;

[0102] The product of sky openness and turbulence intensity is used as the wind environment characteristic index in the target area. The wind environment characteristic index evaluates the ventilation status under the influence of the urban built environment in the target area.

[0103] The above-mentioned parameters and steps for each unit module to implement corresponding functions in the AI ​​assessment system for respiratory infectious disease infection based on environmental analysis of this application can be referred to the parameters and steps in the embodiment of the AI ​​assessment method for respiratory infectious disease infection based on environmental analysis above, and will not be repeated here.

[0104] Reference Figure 3 As shown, an embodiment of the present invention further provides an electronic device 300, including a memory 320 for storing a computer program 322; and a processor 310 for executing the computer program 322 to implement an AI assessment method for infection of patients with respiratory infectious diseases based on environmental analysis as in any of the above embodiments.

[0105] It should be noted that the Figure 3 is a structural diagram of an electronic device 300 according to an exemplary embodiment. Figure 3 The contents herein shall not be considered as any limitation on the scope of application of the present invention.

[0106] Specifically, the electronic device 300 may include: at least one processor 310, at least one memory 320, a power supply 330, a communication interface 340, an input / output interface 350, and a communication bus 360. The memory 320 is used to store a computer program 322, which is loaded and executed by the processor 310 to implement the relevant steps of the AI ​​assessment method for respiratory infectious disease patient infection based on environmental analysis disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 300 in the embodiments of the present invention may specifically be an electronic computer.

[0107] In an embodiment of the present invention, the power supply 330 is used to provide operating voltage for each hardware device on the electronic device 300; the communication interface 340 can create a data transmission channel between the electronic device 300 and external devices, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present invention, and is not specifically limited here; the input and output interface 350 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0108] In addition, the memory 320, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 321, a computer program 322, etc., and the storage method can be temporary storage or permanent storage.

[0109] The operating system 321 is used to manage and control the hardware devices and computer programs on the electronic device 300, and can be Windows Server, NetWare, Unix, Linux, etc. In addition to including the computer program 322 that can be used to implement the AI ​​assessment method for respiratory infectious disease infection based on environmental analysis performed by the electronic device 300 as disclosed in any of the aforementioned embodiments, the computer program 322 can further include computer programs 322 that can be used to perform other specific tasks.

[0110] An embodiment of the present invention also provides a computer-readable storage medium for storing a computer program 322, which, when executed by the processor 310, implements an AI assessment method for respiratory infectious disease patient infection based on environmental analysis as in any of the above embodiments.

[0111] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0112] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0113] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. An AI-based assessment method for respiratory infectious disease patients based on environmental analysis, characterized in that: The steps include: Acquire multi-source environmental data within the target area, as well as regional human behavior data and historical infection data. Multi-source environmental data includes meteorological data, air pollution data, and urban built environment data. Analyze the impact of air pollutants and pathogenic microorganisms on human respiratory tract physiological status in the target area based on air pollution data and historical infection data, and assess the susceptibility risk of regional personnel; Analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the risk of regional virus spread; Construct an infection risk assessment model that combines regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and provide infection risk warnings; The susceptibility risks of people in the assessment area include: Using air pollution data, we construct a pollutant concentration matrix and an interaction factor matrix between air pollutants and pathogenic microorganisms. The interaction factor matrix is ​​used to describe whether air pollutants enhance or inhibit the stability and invasiveness of pathogenic microorganisms. The infection activity enhancement matrix of pathogenic microorganisms is obtained by multiplying the pollutant concentration matrix and the interaction factor matrix; Analyze the degree of damage caused by pathogenic microorganisms to the physiological functions of the human respiratory tract through historical infection data and construct a physiological damage effect matrix; The regional personnel susceptibility risk index is calculated by combining the infection activity enhancement matrix with the physiological damage effect matrix. The regional personnel susceptibility risk index is used to assess the regional personnel susceptibility risk. The assessment of regional virus spread risk includes: Analyze the differences in air pollutant types within the target area based on air pollution data and evaluate the characteristics of air pollutants based on differences in their adsorption capacity; Evaluate the wind environment characteristics under the influence of urban built environment in the target area based on meteorological data and urban built environment data; The virus diffusion environment adaptability index is calculated by combining wind environment characteristics with air pollutant characteristics. The virus diffusion environment adaptability index is used to assess the regional virus diffusion risk; The assessment of air pollutant characteristics includes: Obtain air pollution data, which includes pollutant type, pollutant volume concentration, pollutant specific surface area and pollutant particle size; The ratio of the total surface area of ​​pollutants per unit volume of air to the baseline value of the total surface area of ​​pollutants per unit volume of air is taken as the virus attachment coefficient. The total surface area of ​​pollutants per unit volume of air is the product of the volume concentration of pollutants and the specific surface area of ​​pollutants. The particle size matching coefficient between pollutants and viruses is determined by analyzing the distribution characteristics of the particle size of pollutants and virus particles: ； In the formula Indicates air pollutants The median particle size, Indicates the characteristic particle size of the virus, represents the standard deviation, Indicates the particle size matching coefficient between air pollutants and viruses; The product of the virus attachment coefficient of the pollutant and the particle size matching coefficient is used as the binding potential coefficient between the pollutant and the virus, and the average binding potential coefficient of all types of pollutants in the target area is used as the air pollutant characteristic index in the target area to quantify the potential ability of pollutant particles to carry viruses.

2. The AI ​​assessment method for respiratory infectious disease patients based on environmental analysis according to claim 1 is characterized in that: The wind environment characteristics under the influence of the urban built environment in the target assessment area include: Analyze the building distribution characteristics in the target area through urban built environment data and calculate the sky openness of the target area; The wind speed standard deviation and wind speed mean in the target area are extracted through meteorological data, and the ratio of wind speed standard deviation to wind speed mean is used as the turbulence intensity in the target area; The product of sky openness and turbulence intensity is used as the wind environment characteristic index in the target area. The wind environment characteristic index evaluates the ventilation status under the influence of the urban built environment in the target area.

3. The AI ​​assessment method for respiratory infectious disease patients based on environmental analysis according to claim 1, characterized in that: The infection risk warning includes: The regional personnel infection risk index is obtained through the infection risk assessment model. When the regional personnel infection risk index is greater than or equal to the preset regional personnel infection risk threshold, an infection risk warning is issued; when the regional personnel infection risk index is less than the preset regional personnel infection risk threshold, an infection risk warning is issued.

4. An AI assessment system for respiratory infectious disease patients based on environmental analysis, applied to the AI ​​assessment method for respiratory infectious disease patients based on environmental analysis according to any one of claims 1 to 3, characterized in that: The system comprises: The data acquisition module is used to obtain multi-source environmental data in the target area, as well as regional personnel behavior data and historical infection data. The multi-source environmental data includes meteorological data, air pollution data, and urban built environment data; The susceptibility risk assessment module is used to analyze the impact of air pollutants and pathogenic microorganisms in the target area on the physiological state of the human respiratory tract based on air pollution data and historical infection data, and to assess the susceptibility risk of regional personnel; The diffusion risk assessment module is used to analyze the characteristics of air pollutants in the target area and the wind environment characteristics under the influence of the urban built environment based on multi-source environmental data to assess the regional virus diffusion risk; The infection risk warning module is used to build an infection risk assessment model to combine regional personnel susceptibility risk, regional virus spread risk, and regional personnel behavior data to predict regional personnel infection risk and provide infection risk warning; The control module is used to control the operation of the data acquisition module, the susceptibility risk assessment module, the spread risk assessment module and the infection risk warning module.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the AI ​​assessment method for infection of patients with respiratory infectious diseases based on environmental analysis as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the AI ​​assessment method for infection of patients with respiratory infectious diseases based on environmental analysis as described in any one of claims 1 to 3.

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