Infection risk distribution visualization system

Abstract

To provide an infection risk distribution visualization system capable of easily and accurately creating a distribution of infection risk based on knowledge of specific viruses by measuring carbon dioxide in the target space.SOLUTION: An infection risk distribution visualization system 1 includes: a carbon dioxide concentration meter 11 for measuring concentration of carbon dioxide in a target space; a carbon dioxide concentration distribution calculation unit 12 that calculates concentration distribution in the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11; an infectious disease parameter input unit 21 for inputting a target infectious disease for a user; an infection risk distribution due to droplet nuclei calculation unit 51 that calculates risk distribution of infection due to droplet nuclei in target space based on concentration distribution of the carbon dioxide in the target space calculated by the carbon dioxide concentration distribution calculation unit 12 and infectious disease parameters selected by the infectious disease parameter input unit 21; and an infection risk visualization unit 60 that visualizes the calculated risk distribution.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an infection risk distribution visualization system that visualizes the risk of airborne infection in a target space. [Background technology]

[0002] A technology for identifying the risk of infection with an infectious substance in a space has been disclosed in the past (see Patent Document 1). The technology described in Patent Document 1 generates map information indicating the risk of infection with an infectious substance for each position in the space, based on the positions of people staying in the space and the infection level of the identified people with an infectious substance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-67939 A [Non-Patent Document 1] Rudnick, SN, Milton, DK, Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air 13(3), 237-245., 2003 [Non-Patent Document 2] Brent Stephens: HVAC filtration and the Wells-Riley approach to assessing risks of infectious airborne diseases, Final Report, 2012 [Non-Patent Document 3] Chanjuan Sun, Zhiqiang Zhai(John):The Efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission, Sustainable Cities and Society 62, 2020 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the infection risk identification system described in Patent Document 1, when generating an infection risk map, the user inputs their own physical condition into a mobile terminal. However, in order to identify the infection level, the person staying in the target space must report their own health condition every day. In addition, if the person staying in the target space makes a false report, it is not possible to respond, and there is a risk that an unreliable infection risk map will be generated.

[0005] The present invention aims to provide an infection risk identification system that can measure carbon dioxide and accurately create an infection risk distribution without requiring the effort of people staying in a target space. [Means for solving the problem]

[0007] In the infection risk distribution visualization system according to the present invention, The droplet nuclei infection risk distribution calculation unit calculates the infection risk from the following equations (1) and (2): It is characterized by:

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[0008] The infection risk distribution visualization system according to the present invention comprises: A carbon dioxide concentration meter that measures the concentration of carbon dioxide in a target space; a carbon dioxide concentration distribution calculation unit that calculates a concentration distribution in a target space from the carbon dioxide concentrations at each position measured by the carbon dioxide concentration meter; Infectious diseases targeted by the user Parameters An infectious disease parameter input section for inputting the following: a droplet nucleus infection risk distribution calculation unit that calculates a risk distribution of infection with an infectious disease by droplet nuclei in the target space based on the carbon dioxide concentration distribution in the target space obtained by the carbon dioxide concentration distribution calculation unit and the infectious disease parameters selected by the infectious disease parameter input unit; and A person positioning unit that measures the position of a person within the target space; a droplet infection risk distribution calculation unit that calculates a risk distribution of infection with an infectious disease through droplets in a target space based on the position information of the person obtained by the person positioning unit and the infectious disease parameters selected by the infectious disease parameter input unit; an infection risk visualization unit that visualizes the risk distribution calculated by the droplet nuclei infection risk distribution calculation unit and the droplet infection risk distribution calculation unit; Equipped It is characterized by:

[0009] The infection risk distribution visualization system according to the present invention comprises: The droplet nuclei infection risk distribution calculation unit and the droplet infection risk distribution calculation unit is calculated from the following equations (1) and (5): The risk distribution of Calculations do 4. The infection risk distribution visualization system according to claim 3.

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[0010] In the infection risk distribution visualization system according to the present invention, The infection risk visualization unit excludes the user himself / herself from the number of infected people. It is characterized by:

[0011] The infection risk distribution visualization system according to the present invention comprises: Further comprising a state detection unit that detects the state of a person in the target space, The value of the infectious disease parameter Pd is changed when calculated by the droplet infection risk distribution calculation unit depending on the state of the person detected by the state detection unit. It is characterized by:

[0012] The infection risk distribution visualization system according to the present invention comprises: The device further includes an alarm unit that issues an alarm to people who are in an area of ​​the target space where there is a high risk of infection. It is characterized by:

[0013] The infection risk distribution visualization system according to the present invention comprises: Further provided is a guidance unit that guides a person in an area of ​​the target space where the risk of infection is high to an area where the risk of infection is low. It is characterized by: Effect of the Invention

[0014] According to the infection risk identification system of the present invention, it is possible to measure carbon dioxide and accurately create an infection risk distribution without hassle. [Brief description of the drawings]

[0015] [Figure 1] 1 shows an example of an infection risk identification system according to a first embodiment. [Diagram 2] 1 shows an example of an infection risk distribution map caused by droplet nuclei in the infection risk identification system of the first embodiment. [Diagram 3] 3 shows an example of an infection probability with respect to a carbon dioxide concentration used in the infection risk identification system of the first embodiment. [Figure 4] 1 shows an example of an infection risk identification system according to a second embodiment. [Diagram 5] 13 shows an example of an infection risk distribution map caused by droplet nuclei in the infection risk identification system of the second embodiment. [Figure 6] 13 shows an example of a droplet infection risk distribution map in the infection risk identification system of the second embodiment. [Figure 7]13 shows an example of an infection risk distribution map in the infection risk identification system of the second embodiment. [Figure 8] 13 shows an example of an infection probability with respect to a carbon dioxide concentration used in the infection risk identification system of the second embodiment. [Figure 9] 13 shows an example of a droplet infection risk distribution map in the infection risk identification system of the third embodiment. [Figure 10] 13 shows an example of an infection risk identification system according to a fourth embodiment. [Figure 11] 13 shows an example of a droplet infection risk distribution map in the infection risk identification system of the fourth embodiment. [Figure 12] 13 shows an example of an infection risk identification system according to a fifth embodiment. [Figure 13] 13 shows an example of a droplet infection risk distribution map in the infection risk identification system of the fifth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] An infection risk identification system 1 according to an embodiment of the present invention will be described below with reference to the drawings. The infection risk identification system 1 of this embodiment determines the risk of infection with an infectious disease based on carbon dioxide contained in exhaled breath. Here, the infection risk identification system 1 of this embodiment targets airborne infection and droplet infection, and does not target contact infection. Infectious particles are, for example, mold, bacteria, or viruses.

[0017] It is assumed that infectious particles, targeting droplet nuclei, remain suspended in the air for a long period of time. Such infectious particles are spread indoors through exhaled air, including sneezing and coughing. The carbon dioxide concentration in human exhaled air varies depending on the amount of movement of the person, but generally varies from about 10,000 ppm to 90,000 ppm compared to 350 ppm to 400 ppm in outdoor air. In addition, since the source of carbon dioxide in most indoor buildings is human exhaled air, carbon dioxide is effective as a marker of exhaled air exposure.

[0018] In this way, by using carbon dioxide concentration as an indicator of infection risk, assuming infection through exhaled breath exposure, the administrator can grasp the infection risk distribution simply by continuously measuring carbon dioxide concentration. In other words, there is no need for people in the building to carry specific devices or measuring equipment, and there is no need for them to declare their own health status, so the administrator can reduce the amount of work required.

[0019] FIG. 1 shows an example of an infection risk identification system 1 according to the first embodiment.

[0020] The infection risk identification system 1 of the first embodiment comprises a carbon dioxide concentration meter 11 that measures the concentration of carbon dioxide in a target space, a carbon dioxide concentration distribution calculation unit 12 that calculates the concentration distribution in the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11, an infectious disease parameter input unit 21 that inputs the infectious disease that the user is targeting, a droplet nucleus infection risk distribution calculation unit 51 that calculates the risk distribution of infection with an infectious disease through droplet nuclei in the target space from the carbon dioxide concentration distribution in the target space determined by the carbon dioxide concentration distribution calculation unit 12 and the infectious disease parameters selected by the infectious disease parameter input unit 21, and an infection risk visualization unit 60 that visualizes the infection risk distribution calculated by the droplet nucleus infection risk distribution calculation unit 51.

[0021] The carbon dioxide concentration meter 11 measures the concentration of carbon dioxide in the target space. The carbon dioxide concentration meter 11 may be fixedly installed in advance on a building or fittings, or may be provided on a portable device such as a mobile terminal. At least one carbon dioxide concentration meter 11, and preferably multiple, may be provided in the target space.

[0022] The carbon dioxide concentration distribution calculation unit 12 calculates the concentration distribution in the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11. The concentration distribution is formed continuously by interpolating between the data based on the discretized data of the points measured by the carbon dioxide concentration meter 11.

[0023] Fig. 2 shows an example of an infection risk distribution map due to droplet nuclei in the infection risk identification system 1 of the first embodiment. In the figure, a triangle indicates the position of the carbon dioxide concentration meter 11. In Fig. 2, the carbon dioxide concentration decreases from level 5, which is a dark color, to level 1, which is a light color. Note that the entirety may be expressed by a gradation without using a boundary line.

[0024] The interpolation method between carbon dioxide concentration distribution data may be linear interpolation, spline interpolation, Kriging, polynomial interpolation, Natural Neighbor, IDW method, etc. For example, when spatial autocorrelation is taken into consideration, the interpolation method may be Kriging. Also, when carbon dioxide in the air is not forced convected but diffuses isotropically, the interpolation method may be simple linear interpolation.

[0025] The droplet nuclei infection risk distribution calculation unit 51 calculates the risk distribution of infection with an infectious disease through droplet nuclei in the target space from the carbon dioxide concentration distribution in the target space obtained by the carbon dioxide concentration distribution calculation unit 12 and the infectious disease parameters input by the infectious disease parameter input unit 21.

[0026] The risk distribution of infectious diseases is evaluated using the modified Wells-Riley model proposed by Rudnick and Milton in Non-Patent Document 1. The following formulas (1) and (2) use carbon dioxide concentration as a parameter for the infection probability, and the infection risk can be calculated by inputting the infectious disease parameters described below.

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[0027] Since the infection risk identification system 1 of the first embodiment only considers infection by droplet nuclei, the droplet nuclei infection risk distribution calculation unit 51 calculates the droplet nuclei infection risk distribution of formula (2). Note that it is assumed that droplet nuclei diffuse along the air flow in the same manner as carbon dioxide does, and remain suspended in the air for a long period of time.

[0028] The infection risk identification system 1 of the first embodiment can calculate the infection probability for a specific infectious disease using only the measured value of carbon dioxide concentration by inputting the infectious disease parameters I, q, n, and t as constant values. The infectious disease parameters have different characteristics depending on the infection status of building users and each specific virus, but by setting them based on knowledge, a scientifically based infection risk distribution can be created.

[0029] Although I indicates the number of infected people, the operator cannot determine whether a person is infected or not. Therefore, when the system is in operation, the risk distribution may be visualized by giving I / n with a certain probability. Note that the number of people n in the target space may be a variable value based on information obtained by the human positioning unit 31, and I may be a fixed value.

[0030] The carbon dioxide concentration in the exhaled air during light work is about 30,000 ppm, while the carbon dioxide concentration in the outside air is about 350-400 ppm. The volume fraction of CO2 added to the exhaled air during breathing, C a and the volume fraction of CO2 in the outdoor air, C oBy setting is a constant value, it is possible to calculate the infection risk distribution assuming a person's work intensity and outdoor air conditions under constant conditions.

[0031] FIG. 3 shows an example of the infection probability with respect to the carbon dioxide concentration used in the infection risk identification system 1 of the first embodiment.

[0032] In order to obtain the infection probability for a carbon dioxide concentration, the operator or user of the infection risk identification system 1 sets infectious disease parameters based on knowledge of a specific virus. Table 1 shows an example of infectious disease parameters used in the infection risk identification system 1 of the first embodiment. (Table 1) C o : Volume fraction of carbon dioxide in outdoor air 400 ppm C a : Volume fraction of carbon dioxide added to exhaled air during breathing 30000ppm I: Number of infected people: 1 person q: infectious particle generation rate 200 / h n: Number of people in the target space 5 people t: Stay time (total exposure time) 1 hour

[0033] The infectious disease parameter, the infectious particle generation amount q, may be determined with reference to the research results of Brent Stephens et al. shown in Non-Patent Document 2.

[0034] The infection risk visualization unit 60 visualizes the risk distribution of infection with an infectious disease in the target space based on the carbon dioxide concentration distribution in the target space calculated by the carbon dioxide concentration distribution calculation unit 12 and the infectious disease parameters selected by the infectious disease parameter input unit 21.

[0035] In this way, according to the infection risk identification system 1 of the first embodiment, it is possible to create an infection risk distribution based on knowledge of a specific virus by measuring the carbon dioxide concentration in a room.

[0036] FIG. 4 shows an example of an infection risk identification system 1 according to the second embodiment.

[0037] The infection risk identification system 1 of the second embodiment comprises a carbon dioxide concentration meter 11 that measures the concentration of carbon dioxide in a target space, a carbon dioxide concentration distribution calculation unit 12 that calculates the concentration distribution in the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11, an infectious disease parameter input unit 21 that inputs the infectious disease that a user is targeting, a human positioning unit 31 that measures the position of a person in the target space, a droplet nucleus infection risk distribution calculation unit 51 that calculates the risk distribution of infection with an infectious disease by droplets in the target space from the carbon dioxide concentration distribution in the target space determined by the carbon dioxide concentration distribution calculation unit 12 and the infectious disease parameters selected by the infectious disease parameter input unit 21, a droplet infection risk distribution calculation unit 52 that calculates the risk distribution of infection with an infectious disease by droplets in the target space from the human positioning unit 31 and the infectious disease parameters selected by the infectious disease parameter input unit 21, and an infection risk visualization unit 60 that visualizes the infection risk distribution calculated by the droplet nucleus infection risk distribution calculation unit 51 and the droplet infection risk distribution calculation unit 52.

[0038] The carbon dioxide concentration meter 11, the carbon dioxide concentration distribution calculation unit 12, and the infection parameter input unit 21 of the second embodiment are the same as those of the first embodiment, so only the different parts will be described.

[0039] The infection risk identification system 1 of the second embodiment visualizes risk distribution based on people's location information in addition to carbon dioxide concentration. That is, the infection risk identification system 1 of the second embodiment considers infection by droplets within a predetermined range centered on an infected person in addition to infection by droplet nuclei assumed in the first embodiment.

[0040] Areas with high carbon dioxide concentrations are affected by people's breath and thus suggest a high risk of infection. However, when working alone in a space such as a personal booth, even if the carbon dioxide concentration is high, the risk of infection is considered to be low compared to being in close proximity to other people. Therefore, the infection risk identification system 1 of the second embodiment creates an infection risk distribution that takes into account people's position information.

[0041] The human positioning unit 31 measures the position of a person in a target space. For example, the human positioning unit 31 may use a beacon, Wi-Fi, RFID (Radio Frequency Identifier), UWB (Ultra Wide Band), or the like.

[0042] The droplet infection risk distribution calculation unit 52 calculates the risk distribution of infection with an infectious disease through droplets in the target space from the person's position information obtained by the person positioning unit 31 and the infectious disease parameters selected by the infectious disease parameter input unit 21. That is, the droplet infection risk distribution calculation unit 52 calculates the droplet infection risk distribution shown in the following formula (3) which indicates the infection risk through droplets.

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[0043] The infection risk identification system 1 creates an infection risk map on the assumption that the person closest to you is an infected person. near is basically set to 1. However, assuming that there is a certain probability that infected people exist in the space, q near If is derived, I near may be given as the probability of the presence of an infected person.

[0044] The formula for the droplet infection risk distribution was determined with reference to Non-Patent Document 3. The droplet transmission probability P dThe distribution of is created based on a function of distance from the origin, which is the position coordinate of the person acquired by the person positioning unit 31. d The formula for is an initial velocity of 5 m / s, and the conditions considering the evaporation of droplets are roughly calculated to derive the following approximate formula (4). The present embodiment also uses formula (4), but if there are differences in the initial velocity at the time of droplet diffusion, differences in the evaporation rate, or other medical findings, etc., P d The function of may be modified. d If is negative, P d =0. P d =(-18.19ln(d)+43.276) / 100 (4)

[0045] The infection risk identification system 1 of the second embodiment takes into account both droplet nuclei and droplet infection, so the droplet nuclei infection risk distribution calculation unit 51 calculates the droplet nuclei infection risk distribution using equation (2), and the droplet infection risk distribution calculation unit 52 calculates the droplet infection risk distribution using equation (3).

[0046] Fig. 5 shows an example of a droplet nuclei-related infection risk distribution map in the infection risk identification system 1 of the second embodiment. Fig. 6 shows an example of a droplet-related infection risk distribution map in the infection risk identification system 1 of the second embodiment.

[0047] Since the infectious disease parameter f is proportional to the carbon dioxide concentration, the distribution shown in Figure 5 has the same tendency as the carbon dioxide concentration distribution shown in Figure 2. In Figure 6, circles indicate locations where people are present. When people are present, taking into account the possibility that each person in the target space is a source of infection, a circular area of ​​a specified radius around the person's position is set as a risk area and placed in the infectious disease parameter Pd. If the circular areas overlap, the one with the larger Pd is used.

[0048] FIG. 7 shows an example of an infection risk distribution map in the infection risk identification system of the second embodiment.

[0049] The infection risk visualization unit 60 calculates and visualizes the risk distribution using the risk distribution calculated by the droplet nuclei infection risk distribution calculation unit 51 and the droplet infection risk distribution calculation unit 52. As an example, the infection risk visualization unit 60 of the second embodiment superimposes and visualizes the risk distributions calculated by the droplet nuclei infection risk distribution calculation unit 51 and the droplet infection risk distribution calculation unit 52. In other words, the infection risk visualization unit 60 calculates and visualizes the infection risk distribution of the following formula (5).

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[0050] FIG. 8 shows an example of the infection probability with respect to the carbon dioxide concentration used in the infection risk identification system 1 of the second embodiment.

[0051] Table 2 shows an example of infectious disease parameters used in the infection risk identification system 1 of the second embodiment. (Table 2) C o : Volume fraction of carbon dioxide in outdoor air 400 ppm C a : Volume fraction of carbon dioxide added to exhaled air during breathing 30000ppm I: Number of infected people: 1 person q: infectious particle generation rate 200 / h n: Number of people in the target space 5 people t: Stay time (total exposure time) 1 hour q near : Droplet infectious particle generation rate 0.5 quanta / h

[0052] As shown in Figure 8, the closer the person is to similar people, the higher the probability of infection.

[0053] In this way, the infection risk identification system 1 of the second embodiment can relatively identify areas with high and low infection risk of a specific virus by measuring the carbon dioxide concentration in a room and the number of people present in the room. By looking at the infection risk distribution, users of the target space can identify areas with low infection risk and choose to stay in those areas, thereby reducing the infection risk.

[0054] FIG. 9 shows an example of a droplet infection risk distribution map in the infection risk identification system 1 of the third embodiment.

[0055] The infection risk identification system 1 of the third embodiment differs from the second embodiment in that it can visualize the infection risk distribution for each individual. The infection risk identification system 1 of the second embodiment is a system that can visualize the same infection risk distribution for all users. However, the user will be treated as an infection source. In other words, the user's surroundings will be displayed as an area of ​​high infection risk due to the user's own presence. Therefore, the infection risk identification system 1 of the third embodiment displays the surroundings of the user's location in a way that does not show the influence of the infection risk due to the user's own droplets, as shown in FIG. 9.

[0056] Thus, according to the infection risk identification system 1 of the third embodiment, in addition to the effect of the second embodiment, it is possible to provide appropriate warnings to each individual.

[0057] In addition, the infection risk visualization unit 60 in the infection risk identification system 1 of the third embodiment can overlay the infection risk distribution map due to droplets shown in Figure 9 and the infection risk distribution map due to droplet nuclei shown in Figure 5, and visualize them as an infection risk distribution map as shown in Figure 7.

[0058] Fig. 10 shows an example of the infection risk identification system 1 of the fourth embodiment. Fig. 11 shows an example of a droplet infection risk distribution map in the infection risk identification system 1 of the fourth embodiment.

[0059] The infection risk identification system 1 of the fourth embodiment includes a state detection unit 41 in addition to the infection risk identification system 1 of the second or third embodiment. The state detection unit 41 detects the state of a person in the target space.

[0060] For example, the status detection unit 41 detects whether a person in the target space is wearing a mask. To detect whether a mask is being worn, the status detection unit 41 may use a camera. If the status detection unit 41 detects that a mask is being worn, the influence range of the infectious disease parameter Pd when calculated by the droplet infection risk distribution calculation unit 52 is reduced, and if the status detection unit 41 detects that a mask is not being worn, the influence range of the infectious disease parameter Pd when calculated by the droplet infection risk distribution calculation unit 52 is increased.

[0061] The condition detection unit 41 may also detect the fever state of people in the target space. In the case of detecting a fever state, the condition detection unit 41 may use a thermal imaging camera. If the condition detection unit 41 detects that the fever is lower than a predetermined temperature, the influence range of the infectious disease parameter Pd when calculated by the droplet infection risk distribution calculation unit 52 is reduced, and if the condition detection unit 41 detects that the fever is higher than the predetermined temperature, the influence range of the infectious disease parameter Pd when calculated by the droplet infection risk distribution calculation unit 52 is increased.

[0062] Additionally, the state detection unit 41 may detect the movement of people in the target space, their heart rates, breathing rates, the amount of conversation, and the frequency of occurrence of actions such as coughing or sneezing.

[0063] The infection risk identification system 1 of the fourth embodiment displays the size of the radius centered on a person in response to the infectious disease parameter Pd, as shown in Fig. 11. For example, the radius of a circle centered on a person with a fever and not wearing a mask is larger than the radius of a circle centered on a person with a fever and wearing a mask.

[0064] The infection risk identification system 1 basically operates with the parameter s = 1. However, for visualization for each individual, the value of the parameter s may be reduced when the individual wears a mask, so that the risk due to droplets is reduced.

[0065] Thus, according to the fourth embodiment of the infection risk identification system 1, in addition to the effects of the other embodiments, personal attributes such as a person's condition can be incorporated into the infection risk, making it possible to more accurately grasp areas of high and low risk of infection with a specific virus.

[0066] In addition, the infection risk visualization unit 60 in the infection risk identification system 1 of the fourth embodiment can overlay the infection risk distribution map due to droplets shown in Figure 11 and the infection risk distribution map due to droplet nuclei shown in Figure 5, and visualize them as an infection risk distribution map as shown in Figure 7.

[0067] Fig. 12 shows an example of the infection risk identification system 1 of the fifth embodiment. Fig. 13 shows an example of a droplet infection risk distribution map in the infection risk identification system 1 of the fifth embodiment.

[0068] The infection risk identification system 1 of the fifth embodiment has at least an alarm unit 71 or a guidance unit 72 in addition to the components of the infection risk identification system 1 of the fourth embodiment. The alarm unit 71 issues an alarm to people in an area of ​​the target space where the infection risk is high. The alarm issued by the alarm unit 71 may be a sound, a vibration, a display, or the like.

[0069] The guiding unit 72 guides a person in an area of ​​the target space with a high infection risk to an area with a low infection risk. Areas with a low infection risk may be prioritized. For example, if distance is prioritized, the person may be guided to an area that is closest to the current location within a preset infection risk range. Also, if infection risk is prioritized, the person may be guided to an area with the lowest infection risk within a preset distance range. However, it is preferable not to guide the person to the same area as other people.

[0070] Furthermore, the infection risk identification system 1 may have a seat storage unit 80. The seat storage unit 80 stores seats in the target space that are registered in advance by the operator. As shown in Fig. 13, the infection risk identification system 1 can be set so that when the guiding unit 72 guides a person, it guides the person to a seat with the lowest infection risk.

[0071] Seats with a low infection risk may be given priority. For example, if distance is prioritized, the person may be guided to the seat closest to the current location within a preset infection risk range. Also, if infection risk is prioritized, the person may be guided to the seat with the lowest infection risk within a preset distance range. However, it is preferable not to guide the person to the same seat as other people.

[0072] Thus, according to the infection risk identification system 1 of the fifth embodiment, in addition to the effects of the other embodiments, by having the alarm unit 71, it is possible to notify people in areas with a high risk of infection with a specific virus of the danger, thereby reducing the risk of infection. Also, by having the guidance unit 72, it is possible to guide people to areas with a low risk of infection, thereby reducing the risk of infection. Furthermore, by having the seat memory unit 80, it is possible to guide people to seats with a low risk of infection, thereby reducing the risk of infection.

[0073] In addition, the infection risk visualization unit 60 in the infection risk identification system 1 of the fifth embodiment can overlay the infection risk distribution map due to droplets shown in Figure 13 and the infection risk distribution map due to droplet nuclei shown in Figure 5, and visualize them as an infection risk distribution map as shown in Figure 7.

[0074] As described above, the infection risk distribution visualization system 1 of this embodiment includes a carbon dioxide concentration meter 11 that measures the concentration of carbon dioxide in a target space, a carbon dioxide concentration distribution calculation unit 12 that calculates the concentration distribution of the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11, an infectious disease parameter input unit 21 that inputs the infectious disease targeted by the user, an infection risk distribution calculation unit 51 by droplet nuclei that calculates the risk distribution of infection with an infectious disease by droplet nuclei in the target space from the carbon dioxide concentration distribution of the target space calculated by the carbon dioxide concentration distribution calculation unit 12 and the parameters of the infectious disease selected by the infectious disease parameter input unit 21, and an infection risk visualization unit 60 that visualizes the risk distribution calculated by the infection risk distribution calculation unit 51 by droplet nuclei. Therefore, according to the infection risk identification system 1 of this embodiment, an infection risk distribution based on knowledge of a specific virus can be created by measuring the carbon dioxide concentration in a room.

[0075] In the infection risk distribution visualization system 1 according to the present invention, the droplet nuclei infection risk distribution calculation unit 51 calculates the infection risk from the following formulas (1) and (2).

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[0076] Therefore, an infection risk distribution can be generated based on knowledge of a particular virus.

[0077] The infection risk distribution visualization system 1 of the present invention comprises a carbon dioxide concentration meter 11 that measures the concentration of carbon dioxide in a target space, a carbon dioxide concentration distribution calculation unit 12 that calculates the concentration distribution in the target space from the carbon dioxide concentration at each position measured by the carbon dioxide concentration meter 11, an infectious disease parameter input unit 21 into which a user inputs an infectious disease that is a target, a droplet nucleus infection risk distribution calculation unit 51 that calculates the risk distribution of infection with an infectious disease by droplet nuclei in the target space from the carbon dioxide concentration distribution in the target space calculated by the carbon dioxide concentration distribution calculation unit 12 and the infectious disease parameters selected by the infectious disease parameter input unit 21, a human positioning unit 31 that measures the position of a person in the target space, a droplet infection risk distribution calculation unit 52 that calculates the risk distribution of infection with an infectious disease by droplets in the target space from the person position information calculated by the human positioning unit 31 and the infectious disease parameters selected by the infectious disease parameter input unit 21, and an infection risk visualization unit 60 that visualizes the risk distribution calculated by the droplet nucleus infection risk distribution calculation unit 51 and the droplet infection risk distribution calculation unit 52. Therefore, according to the infection risk identification system 1 of the present embodiment, it is possible to measure carbon dioxide in a target space and easily and accurately create a distribution of infection risks based on knowledge of a specific virus.

[0078] In the infection risk distribution visualization system 1 of this embodiment, the droplet nuclei infection risk distribution calculation unit 51 calculates the infection risk from the following equations (1) and (5).

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[0079] Therefore, an infection risk distribution can be generated based on knowledge of a particular virus.

[0080] In the infection risk distribution visualization system 1 of this embodiment, the infection risk visualization unit 60 excludes the user himself / herself from the number of infected people. Therefore, it is possible to provide appropriate warnings to each individual.

[0081] The infection risk distribution visualization system 1 of this embodiment further includes a status detection unit 41 that detects the status of people in the target space, and changes the numerical value of the infectious disease parameter Pd when calculated by the droplet infection risk distribution calculation unit 52 depending on the status of people detected by the status detection unit 41. Therefore, personal attributes such as a person's status can be incorporated into the infection risk, making it possible to more accurately grasp areas with high and low infection risk for a specific virus.

[0082] The infection risk distribution visualization system 1 of this embodiment further includes an alarm unit 71 that issues an alarm to people who are in an area of ​​the target space where the infection risk is high. Therefore, it is possible to accurately create an infection risk distribution based on knowledge of a specific virus by measuring carbon dioxide in the target space without hassle.

[0083] The infection risk distribution visualization system 1 of the present embodiment further includes a guidance unit 72 that guides people in areas of high infection risk in the target space to areas of low infection risk. Therefore, people can be guided to areas of low infection risk, and the infection risk can be reduced.

[0084] It should be noted that the present invention is not limited to the embodiments, and that although many specific details are included for illustrative purposes in the description of the embodiments, those skilled in the art may make various variations and modifications to these details. [Explanation of symbols]

[0085] 1. Infection risk distribution visualization system 11...Carbon dioxide concentration meter 12...Carbon dioxide concentration distribution calculation unit 21...Infectious disease parameter input section 31...Human positioning unit 41…Status detection unit 51... Droplet nuclei infection risk distribution calculation unit 52... Droplet infection risk distribution calculation unit 60…Infection Risk Visualization Department 71...Alarm section 72…Guiding part 80…Seat memory section

Claims

1. A carbon dioxide concentration meter that measures the concentration of carbon dioxide in a target space; a carbon dioxide concentration distribution calculation unit that calculates a concentration distribution in a target space from the carbon dioxide concentrations at each position measured by the carbon dioxide concentration meter; an infectious disease parameter input unit for allowing a user to input target infectious disease parameters; a droplet nucleus infection risk distribution calculation unit that calculates a risk distribution of infection with an infectious disease by droplet nuclei in the target space based on the carbon dioxide concentration distribution in the target space obtained by the carbon dioxide concentration distribution calculation unit and the infectious disease parameters selected by the infectious disease parameter input unit; and A person positioning unit that measures the position of a person within the target space; a droplet infection risk distribution calculation unit that calculates a risk distribution of infection with an infectious disease through droplets in a target space based on the position information of the person obtained by the person positioning unit and the infectious disease parameters selected by the infectious disease parameter input unit; an infection risk visualization unit that visualizes the risk distribution calculated by the droplet nuclei infection risk distribution calculation unit and the droplet infection risk distribution calculation unit; Equipped An infection risk distribution visualization system characterized by:

2. The droplet nuclei infection risk distribution calculation unit and the droplet infection risk distribution calculation unit calculate the risk distribution from the following equations (1) and (5).

2. The infection risk distribution visualization system according to claim 1 . [0010] Where: V: Volume of indoor air to be diffused [m 3 ], V e : Equivalent volume of exhaled air contained in room air [m 3 ], C: CO in indoor air 2 volume fraction [ppm], C o : CO in outdoor air 2 volume fraction [ppm], C a : CO added to exhaled air during breathing 2 Volume fraction of [ppm], I: Number of infected people [people], q: Amount of infectious particles generated by droplet nuclei [quanta / h], n: Number of people in the target space [people], t: Stay time [h], P d : Probability of droplet transmission, I near : Number of infected people who generate infectious droplets [people], q near :Amount of infectious particles generated by droplets [quanta / h], s: The probability that a person who is not immune to an infectious disease will develop an infectious disease when droplets come into direct or indirect contact with the mucous membrane, P: Probability of infection, It is.

3. The infection risk visualization unit does not include the user himself in the number of infected people.

3. The infection risk distribution visualization system according to claim 1 or 2.

4. Further comprising a state detection unit that detects the state of a person in the target space, The value of the infectious disease parameter Pd is changed when calculated by the droplet infection risk distribution calculation unit depending on the state of the person detected by the state detection unit.

4. The infection risk distribution visualization system according to claim 1, wherein the infection risk distribution visualization system is a system for visualizing a distribution of infection risk.

5. The device further includes an alarm unit that issues an alarm to people who are in an area of ​​the target space where there is a high risk of infection.

5. The infection risk distribution visualization system according to claim 1, wherein the infection risk distribution visualization system is a system for visualizing a distribution of infection risk.

6. Further provided is a guidance unit that guides a person in an area of ​​the target space where the risk of infection is high to an area where the risk of infection is low.

6. An infection risk distribution visualization system according to claim 1, wherein the infection risk distribution visualization system comprises:

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