JPWO2019044254A1 - Eradication performance prediction system and eradication performance prediction method - Google Patents

Abstract

The sterilization performance prediction system (100) stores a storage unit (130) that stores a CT value database (131) that associates a bacterium type with a sterilization index value that is a product of a drug concentration and time required for sterilization. ) And a control unit (120), the control unit (120) calculates a drug concentration at a predetermined position in the space (10) in which the drug (20) is sprayed, and a concentration estimation unit (121). By referring to the CT value database (131) based on the determined drug concentration, the type of bacteria (30) to be sterilized, and environmental information indicating the environment in the space (10), bacteria at a predetermined position And a prediction unit (122) for predicting the survival state of the.

Description

  The present invention relates to a bacteria elimination performance prediction system and a bacteria elimination performance prediction method.

  Conventionally, by performing a computational fluid dynamics analysis, the attached amount of the sprayed drug at a predetermined position is calculated, and based on the calculated attached amount and the bacterial species, the survival state of the bacteria at the predetermined position is predicted. (For example, see Patent Document 1).

JP 2013-190381 A

  However, the above conventional technique has a problem that the prediction accuracy of the survival state of the bacterium is low.

  Therefore, an object of the present invention is to provide a bacteria elimination performance prediction system and a bacteria elimination performance prediction method that can accurately predict the survival state of bacteria.

  In order to achieve the above object, the eradication performance prediction system according to one aspect of the present invention provides correspondence information in which a type of bacteria and an eradication index value that is a product of a drug concentration and time required for eradication are associated with each other. And a control unit, wherein the control unit calculates a drug concentration at a predetermined position in a space where the drug is sprayed, a concentration estimating unit, the calculated drug concentration, and A prediction unit that predicts a survival state of the bacterium at the predetermined position by referring to the correspondence information based on a type of a certain bacterium and environmental information indicating an environment in the space.

  In addition, the method for estimating sterilization performance according to one aspect of the present invention includes a step of calculating a drug concentration at a predetermined position in a space where the drug is sprayed, the calculated drug concentration, and a type of bacteria to be sterilized. And, based on the environment information indicating the environment in the space, by referring to the corresponding information in which the type of the bacterium is associated with a bactericidal index value that is a product of the drug concentration and the time required for the elimination. Predicting the survival state of the bacteria at the predetermined position.

  Further, one embodiment of the present invention can be realized as a program for causing a computer to execute the above-described method for estimating sterilization performance. Alternatively, it can be realized as a computer-readable recording medium storing the program.

  ADVANTAGE OF THE INVENTION According to this invention, the survival state of bacteria can be accurately predicted.

FIG. 1 is a block diagram showing a functional configuration of the sterilization performance prediction system according to the first embodiment. FIG. 2 is a diagram schematically illustrating a space to be estimated by the eradication performance predicting system according to the first embodiment and a state of a drug generated in the space. FIG. 3 is a diagram schematically showing bacteria existing on a member surface in a space to be estimated by the eradication performance predicting system according to the first embodiment, and a drug generated in the space. FIG. 4 is a diagram illustrating an example of a CT value database stored in the sterilization performance prediction system according to the first embodiment. FIG. 5 is a block diagram illustrating a functional configuration of a concentration estimating unit of the sterilization performance prediction system according to the first embodiment. FIG. 6 is a cross-sectional view for explaining the adsorption / desorption coefficient of a substance with respect to a predetermined surface. FIG. 7 is a cross-sectional view for explaining a concentration difference ratio used when determining the adsorption / desorption coefficient of a substance with respect to a predetermined surface. FIG. 8 is a diagram showing the temperature dependence of the adsorption / desorption coefficient. FIG. 9 is a diagram showing the temperature dependence of the survival state of bacteria. FIG. 10 is a schematic diagram showing a state in which bacteria are covered with water droplets. FIG. 11 is a diagram showing the humidity dependence of the survival state of bacteria. FIG. 12 is a schematic diagram illustrating a state in which a medicine and bacteria are irradiated with ultraviolet rays. FIG. 13 is a diagram showing the wavelength dependence of ultraviolet absorption by hypochlorous acid. FIG. 14 is a diagram showing the wavelength dependence of ultraviolet absorption by DNA. FIG. 15 is a diagram showing the wavelength dependence of ultraviolet absorption by a protein. FIG. 16 is a schematic diagram showing a state in which bacteria are covered with organic matter. FIG. 17 is a flowchart showing the operation of the sterilization performance prediction system according to the first embodiment. FIG. 18 is a flowchart illustrating a concentration estimation process in the operation of the sterilization performance prediction system according to the first embodiment. FIG. 19 is a three-dimensional distribution diagram showing the survival state of the bacteria output by the bacteria elimination performance prediction system according to the first embodiment. FIG. 20 is a block diagram illustrating a functional configuration of a sterilization performance prediction system according to the second embodiment. FIG. 21 is a diagram illustrating an example of a scene database stored in the sterilization performance prediction system according to the second embodiment.

  Hereinafter, a sterilization performance prediction system and a sterilization performance prediction method according to an embodiment of the present invention will be described in detail with reference to the drawings. Each of the embodiments described below shows a specific example of the present invention. Therefore, numerical values, shapes, materials, constituent elements, arrangement and connection forms of constituent elements, steps, order of steps, and the like shown in the following embodiments are merely examples, and do not limit the present invention. Therefore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept of the present invention are described as arbitrary components.

  In addition, each drawing is a schematic diagram, and is not necessarily strictly illustrated. Therefore, for example, the scales and the like do not always match in each drawing. Further, in each of the drawings, substantially the same configuration is denoted by the same reference numeral, and redundant description will be omitted or simplified.

(Embodiment 1)
[Overview]
First, an outline of a sterilization performance prediction system according to an embodiment will be described with reference to FIGS.

  FIG. 1 is a block diagram showing a functional configuration of a sterilization performance prediction system 100 according to the present embodiment. FIG. 2 is a diagram schematically illustrating a space 10 to be estimated by the eradication performance predicting system 100 according to the present embodiment, and a state of the medicine 20 generated in the space 10. FIG. 3 is a diagram schematically showing the bacteria 30 existing on the surface 40 of the member in the space 10 to be estimated by the eradication performance predicting system 100 according to the present embodiment, and the drug 20 generated in the space 10. It is. FIG. 3 corresponds to an enlarged cross-sectional view of a part of FIG.

  The space 10 is a space for which the sterilization performance prediction system 100 predicts the sterilization performance. The space 10 is, for example, one room surrounded by walls, windows, doors, and the like. As shown in FIG. 2, a source 10 of a medicine 20 is provided in the space 10. Furniture such as chairs 12 and shelves 13 are arranged in the space 10. In addition, arrangement | positioning of the generation source 11, the chair 12, the shelf 13, etc., the number, etc. are only examples.

  The medicine 20 is a substance having a bactericidal action for removing the bacteria 30. In addition, in this specification, the elimination of bacteria means not only removal of bacteria or bacteria but also removal of viruses. That is, the bacteria 30 include not only bacteria and bacteria but also viruses and the like. The bacterium 30 is, for example, Staphylococcus aureus, Pseudomonas aeruginosa, or Escherichia coli, but is not limited thereto.

For example, the drug 20 is hypochlorous acid (HClO). Alternatively, the chemical 20 may be ozone (O ₃ ) or plasma-treated water (H ₂ O). The medicine 20 is, for example, a liquid such as atomized hypochlorous acid water, but may be a gas or a particulate solid.

  The medicine 20 is released from the source 11 into the space 10. The generation source 11 is a generation device that generates and releases the medicine 20. For example, the generation source 11 is a hypochlorous acid water generation device, and generates hypochlorous acid water by electrolyzing a saline solution. The generation source 11 has a function of forming an airflow into the space 10. The generation source 11 vaporizes the generated hypochlorous acid water and releases hypochlorous acid as a drug 20 together with the airflow. The medicine 20 is diffused into the space 10 along with the airflow.

  When the medicine 20 diffused in the space 10 comes into contact with the bacteria 30 existing in the space 10, the bacteria 30 are decomposed and the bacteria are removed. By dispersing the drug 20 in the space 10 at an appropriate concentration, the bacteria can be efficiently removed.

  The medicine 20 existing in the space 10 is variously affected according to at least one of the property of the medicine 20 and the environment in the space 10. For example, the drug 20a shown in FIG. 2 disappears by self-decomposition. The medicine 20b floats in the space 10 and is diffused. The medicine 20c is adsorbed on the wall of the space 10. The adsorbed drug 20c is released from the wall. The medicine 20d is decomposed by ultraviolet rays and disappears.

  Similarly, the bacteria 30 existing in the space 10 are also variously affected according to at least one of the characteristics of the bacteria 30 and the environment in the space 10. For example, the bacterium 30 may be decomposed by ultraviolet rays and disappear like the drug 20c. Details will be described later.

  The sterilization performance prediction system 100 according to the present embodiment is realized by one or more information processing devices. Each of the one or more information processing apparatuses is realized by a nonvolatile memory storing a program, a volatile memory serving as a temporary storage area for executing the program, an input / output port, a processor executing the program, and the like.

[Constitution]
Hereinafter, a detailed configuration of the sterilization performance prediction system 100 will be described with reference to FIG. 1 while appropriately referring to FIGS. 2 and 3.

  As shown in FIG. 1, the sterilization performance prediction system 100 includes an acquisition unit 110, a control unit 120, a storage unit 130, and an output unit 140. The bacteria elimination performance prediction system 100 acquires environmental information, drug information, and bacteria information, and performs simulation based on the acquired information, thereby outputting prediction information indicating the survival state of the bacteria 30 at a predetermined position in the space 10. I do. The predetermined position is a target position for prediction.

  The target position is one subspace selected as a position for which the density is to be estimated from a plurality of subspaces obtained by dividing the space 10 into a three-dimensional matrix. The target position is represented by, for example, a three-dimensional coordinate system. The target position is not limited to one subspace, but may be a plurality of subspaces, or all subspaces, that is, the entire space 10. In this case, the sterilization performance prediction system 100 may output the three-dimensional distribution of the living state of the bacteria 30 in the space 10 as prediction information.

  The subspace corresponds to a unit (mesh) of calculation in the density estimation simulation. The sizes of the plurality of subspaces may be different from each other. The plurality of partial spaces are, for example, cubic spaces having the same size as each other. The length of one side of the partial space is, for example, 80 mm, but is not limited thereto. The partial space may be a rectangular parallelepiped or a triangular pyramid as long as it is a solid (three-dimensional) element. The size of the subspace may be determined based on the size of the space 10.

  The environment information is information indicating an environment in the space 10. Specifically, the environmental information includes first environmental information that affects the state of the bacterium 30 to be removed, and second environmental information that affects the medicine 20 that is sprayed on the space 10. The first environment information is information indicating at least one of the temperature, humidity, and ultraviolet ray amount at the target position, and the surface temperature when the target position includes the surface 40 of the member existing in the space 10. The second environmental information includes, among the temperature, the humidity, and the amount of ultraviolet light at the target position, and the surface temperature and the amount of organic substances attached to the surface 40 when the target position includes the surface 40 of the member existing in the space 10. This is information indicating at least one. Note that the member is a building material such as a wall material, a ceiling material, a floor material, or a window material that forms the space 10, or an obstacle such as the chair 12, the shelf 13, or the generation source 11 arranged in the space 10. . Details of each of the first environment information and the second environment information will be described later.

  The medicine information is information on the medicine 20 generated in the space 10. Specifically, the medicine information is information indicating an amount of the medicine 20 generated, a wind direction and a wind speed of an airflow for moving the medicine 20, and the like. The medicine information may include information indicating the type of the medicine 20.

  The bacteria information is bacteria that can exist in the space 10 and is information on the bacteria 30 to be removed. Specifically, the bacteria information is information indicating the type of bacteria, bacteria, or virus.

  The acquisition unit 110 acquires environment information, medicine information, and bacteria information. The acquisition unit 110 is realized by, for example, an input interface to which an output signal output from a sensor such as a temperature and humidity sensor is input, a user interface to receive an operation from a user, a communication interface to communicate with the generation source 11, and the like. You. The user interface is, for example, a touch panel or a physical operation button. Alternatively, the acquisition unit 110 may be realized by a communication interface that communicates with a terminal device such as a smartphone operated by a user.

  In the present embodiment, acquisition unit 110 acquires temperature information indicating the temperature in space 10 from a temperature and humidity sensor or a temperature sensor. Specifically, the temperature information indicates the air temperature in the space 10 or the surface temperature of the surface 40 of the member located in the space 10. The acquisition unit 110 acquires humidity information indicating the humidity in the space 10 from the temperature / humidity sensor or the humidity sensor. The acquisition unit 110 acquires ultraviolet information indicating the amount of ultraviolet light in the space 10 from the ultraviolet light meter.

  Note that at least one of the temperature and humidity sensor and the ultraviolet light meter may be provided in a plurality in the space 10. The acquisition unit 110 may acquire temperature information, humidity information, and ultraviolet information in each of the plurality of partial spaces configuring the space 10 from the plurality of temperature / humidity sensors and the ultraviolet light meter.

  The acquisition unit 110 acquires material information indicating the material of a member located in the space 10 from a terminal device or the like. The material information specifically indicates the material of the member for each partial space. The material information may be stored in the storage unit 130 in advance when the space 10 is formed, for example.

  The acquisition unit 110 acquires, from the generation source 11, generation amount information indicating the generation amount of the medicine 20. The generation amount information indicates, for example, the concentration of the medicine 20 generated from the generation source 11 per unit time as the generation amount. The acquisition unit 110 acquires airflow information indicating a wind direction and a wind speed of an airflow supplied from the generation source 11 into the space 10. The airflow information indicates the traveling direction and the traveling speed of the airflow as the wind direction and the wind speed, respectively, with the outlet of the generation source 11 as a reference position.

  The acquisition unit 110 acquires bacteria information from a terminal device or the like. For example, the acquisition unit 110 acquires, from the terminal device, information indicating the type of the germ 30 to be disinfected selected by the user operating the terminal device as germ information.

  The acquisition unit 110 further obtains, from a terminal device or the like operated by the user, the size and shape of the space 10 and geometry information indicating the size, shape, and arrangement position of an obstacle such as the chair 12 existing in the space 10. May be obtained. The acquisition unit 110 may further acquire type information indicating the type of the medicine 20.

  In addition, the sterilization performance prediction system 100 according to the present embodiment may include a sensor that acquires environmental information. The sensor performs a function of acquiring environment information among the functions of the acquisition unit 110. The sensor is, for example, at least one of the above-described temperature and humidity sensor, a temperature sensor, a humidity sensor, an ultraviolet light meter, and an organic substance detection sensor.

  The control unit 120 executes a main function of the sterilization performance prediction system 100. The control unit 120 is realized by a nonvolatile memory storing a program, a volatile memory serving as a temporary storage area for executing the program, an input / output port, a processor executing the program, and the like. In the present embodiment, the control unit 120 includes a concentration estimating unit 121 and a predicting unit 122, as shown in FIG.

  The concentration estimating unit 121 calculates a drug concentration at a predetermined position in the space 10 where the medicine 20 is sprayed. Specifically, the concentration estimating unit 121 outputs concentration information indicating the concentration of the medicine 20 at a target position in the space 10 by performing a simulation of estimating the concentration of the medicine 20. In the present embodiment, the concentration estimating unit 121 calculates the amount of the medicine 20 generated, the wind direction and the wind speed of the air flow that moves the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, The adsorption / desorption coefficient of the medicine 20 on the predetermined surface and at least one of the temperature, humidity, and ultraviolet ray amount in the space 10 are acquired as input data, and the medicine concentration is calculated using the acquired input data. The detailed configuration of the density estimating unit 121 will be described later.

  The prediction unit 122 refers to the CT value database 131 stored in the storage unit 130 based on the calculated drug concentration, the type of the bacterium 30 to be eradicated, and the environmental information, to thereby determine the bacterium at the target position. Predict 30 survival states. In the present embodiment, the prediction unit 122 predicts the survival state of the bacterium 30 by further referring to the CT value database 131 based on the drug information indicating the type of the drug 20 to be scattered in the space 10.

  The survival state of the bacterium 30 is represented by, for example, a decrease number of the bacterium 30 at a target position after a predetermined period has elapsed since the drug 20 was released. The number of bacteria 30 reduced is represented by the number of digits. The fact that the number of digits of the eradication is 1 (also referred to as “single digit eradication”) means that the number of bacteria is reduced by one digit, that is, the number of bacteria after the eradication is 0.1 when the initial state is 1. It is to become 1. That is, single-digit eradication means removing 90% of the bacteria 30, that is, 90% disinfection. Similarly, double digit eradication means 99% eradication. 3-digit eradication means 99.9% eradication.

  In the present embodiment, the prediction unit 122 calculates the number of decreased bacteria Y based on the following equation (1).

  In Expression (1), the estimated CT value is a CT value calculated based on the drug concentration estimated by the concentration estimating unit 121. The estimated CT value is calculated for each target position. The estimated CT value indicates the sterilization performance of the medicine 20 at the target position.

  The reference CT value is a CT value required when performing X-digit sterilization. The reference CT value is obtained by referring to the CT value database 131 stored in the storage unit 130.

  The environmental factor f is a correction coefficient based on environmental information. Specifically, the environmental factor f is determined by the temperature, the humidity and the amount of ultraviolet rays, the surface temperature of the member, the amount of organic matter, and the like. Details will be described later.

  Note that the prediction unit 122 may calculate the eradication rate of the bacterium 30 based on the following equation (2).

  The storage unit 130 stores a CT value database 131. The storage unit 130 is, for example, a nonvolatile memory such as an HDD (Hard Disk Drive) or a semiconductor memory.

  The CT value database 131 is an example of correspondence information in which a type of bacteria is associated with a disinfection index value which is a product of a drug concentration (C) required for disinfection and a time (T). The eradication index value is a so-called CT value. In the correspondence information, the type of bacteria and the eradication index value are associated with each other for each type of the medicine 20.

  FIG. 4 is a diagram showing the CT value database 131 stored in the sterilization performance prediction system 100 according to the present embodiment. As shown in FIG. 4, a CT value is associated with a combination of the type of the bacteria 30 and the type of the medicine 20.

  The CT value is, for example, the product of the drug concentration and the time required to eliminate 99% of the corresponding bacteria 30 using the corresponding drug 20. That is, the CT value is the product of the drug concentration and the time required to eliminate the bacteria 30 by two digits. The CT value may be the product of the drug concentration and the time required for removing bacteria 30 by one digit (that is, 90% of bacteria) or by three digits (that is, 99.9% of bacteria). It may be the product of the drug concentration and the time required to perform the measurement.

As the CT value stored in the CT value database 131 is larger, a higher concentration of the drug 20 is required to remove the corresponding bacteria 30, and / or the time during which the bacteria 30 are exposed to the drug 20. Means that many are needed. The CT value is obtained, for example, by performing a sterilization test under standard conditions. The reference conditions are, for example, a temperature of 20 ° C., a humidity of 50% RH, and an ultraviolet ray amount of 0 mW / cm ² , but are not limited thereto.

  Note that the correspondence format of information in the CT value database 131 is not limited to the example shown in FIG. For example, the CT value database 131 may indicate the number of digits that can be disinfected when the disinfection processing of a predetermined CT value is performed according to the combination of the type of the medicine 20 and the type of the bacterium 30.

  For example, when the drug 20 is ozone, Staphylococcus aureus can be sterilized 1.7 digits by performing a sterilization process with a CT value of 10. Pseudomonas aeruginosa can be eradicated by 0.57 digits by performing a sterilization treatment with a CT value of 10. Escherichia coli can be sterilized by 1.44 digits by performing a sterilization treatment with a CT value of 25.

  In addition, for example, when the drug 20 is hydrogen peroxide, Staphylococcus aureus can be sterilized by two digits by performing a sterilization process with a CT value of 3350. In addition, when the drug 20 is hydrogen peroxide, Staphylococcus aureus can be sterilized by six digits by performing a sterilization process with a CT value of 258,000.

  The output unit 140 outputs prediction information indicating the survival state of the bacteria calculated by the control unit 120. For example, the output unit 140 is a display, and displays image data indicating a three-dimensional distribution of density. Alternatively, the output unit 140 may be an output interface connected to an external display device. The output unit 140 may output image data to an external display device.

[Density estimation unit]
Subsequently, a detailed configuration of the density estimating unit 121 according to the present embodiment will be described with reference to FIG. FIG. 5 is a block diagram showing a functional configuration of the concentration estimating unit 121 of the sterilization performance prediction system 100 according to the present embodiment.

  As shown in FIG. 5, the concentration estimating unit 121 includes a determining unit 210, a calculating unit 220, and a storage unit 230. The concentration estimating unit 121 acquires environment information and medicine information, and performs a simulation based on the acquired information, thereby outputting concentration information indicating the concentration of the medicine 20 at a predetermined position in the space 10. The predetermined position is a target position for density estimation.

  The determination unit 210 determines the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, and the adsorption / release coefficient of the medicine 20, based on the information acquired by the acquisition unit 110. The determination unit 210 outputs the determined self-decomposition coefficient, diffusion coefficient and adsorption / desorption coefficient, and the information acquired by the acquisition unit 110 to the calculation unit 220 as input data. The details of the self-decomposition coefficient, the diffusion coefficient, and the adsorption-release coefficient will be described later.

  As shown in FIG. 5, the storage unit 230 stores a first database 231 for self-decomposition coefficients, a second database 232 for diffusion coefficients, and a third database 233 for adsorption and emission coefficients. The storage unit 230 is, for example, a nonvolatile memory such as an HDD or a semiconductor memory. Note that the storage unit 230 may be implemented using the same hardware resources as the storage unit 130 illustrated in FIG.

  The calculation unit 220 calculates the concentration of the medicine 20 in the space 10 by performing a simulation of the medicine 20 diffusing into the space 10. The arithmetic unit 220 is realized by a non-volatile memory storing a program, a volatile memory serving as a temporary storage area for executing the program, an input / output port, a processor executing the program, and the like.

  Specifically, the calculation unit 220 calculates the amount of the medicine 20 generated, the wind direction and the wind speed of the air flow for moving the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, and a predetermined surface in the space 10. And the at least one of temperature, humidity, and the amount of ultraviolet rays in the space 10 are acquired as input data. The calculation unit 220 obtains the input data, and calculates the concentration of the medicine 20 in the space 10 using the obtained input data.

  The arithmetic unit 220 performs, for example, an analysis based on CFD (Computational Fluid Dynamics) (hereinafter referred to as CFD analysis). The CFD analysis is performed based on a model such as RANS (Reynolds-Averaged Navier-Stokes equations), DNS (Direct Numerical Simulation), LES (Large Eddy Simulation), or DES (Detached Eddy Simulation). Specifically, the calculation unit 220 calculates the type of the medicine 20, the amount of the medicine 20, the wind direction and the wind speed of the airflow, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, and the adsorption and release of the medicine 20. By performing CFD analysis using the coefficient, the temperature, humidity, and the amount of ultraviolet light in the space 10 as input data, the drug concentration of each partial space constituting the space 10 is calculated. Specifically, the calculation unit 220 generates a three-dimensional distribution of the drug concentration for each subspace. More specifically, the calculation unit 220 calculates the concentration of the medicine 20 at an arbitrary time at an arbitrary position in the space 10. The calculation unit 220 generates a three-dimensional distribution of the drug concentration in the space 10 that changes over time.

  For example, the larger the amount of the medicine 20 generated, the larger the estimated concentration value of each subspace. The smaller the amount of the medicine 20 generated, the smaller the estimated concentration value of each subspace. Further, based on the wind direction of the airflow, for example, the density estimated value of the subspace located downstream of the generation source 11 (specifically, a position distant from the generation source 11) is calculated upwind of the generation source 11 (specifically, Is smaller than the estimated density value of the subspace located at a position close to the source 11). Further, when the wind speed is high, the estimated value of the density in the subspace at a position distant from the generation source 11 increases. When the wind speed is low, the estimated value of the density in the subspace at a position distant from the generation source 11 becomes small. The relationship between the self-decomposition coefficient, the diffusion coefficient, the adsorption / desorption coefficient, and the concentration estimation value will be described later.

  The calculation unit 220 outputs concentration information indicating the calculated drug concentration to the prediction unit 122.

[Concentration estimation simulation]
Subsequently, the details of the simulation for estimating the drug concentration for calculating the estimated CT value in the above equation (1) will be described. First, three parameters relating to the properties of the medicine 20, which are acquired as input data by the calculation unit 220 of the concentration estimation unit 121 according to the present embodiment, will be described. The three parameters are the self-decomposition coefficient, diffusion coefficient, and adsorption-release coefficient of the drug 20.

[Self-decomposition coefficient]
First, the self-decomposition coefficient of the medicine 20 will be described.

  The self-decomposition coefficient is a parameter indicating the degree to which the drug 20 is self-decomposed. Specifically, the self-decomposition coefficient is a reaction coefficient defined by the Arrhenius equation shown in the following equation (3).

In the equation (3), k is the autolysis coefficient (unit [/ s]) of the medicine 20. A is a frequency factor. E _a is the activation energy. R is a gas constant. T is the absolute temperature in the space 10. Here, A and E _a is a value determined according to the type of drug 20.

  As shown in Expression (3), the self-decomposition coefficient k depends on the temperature in the space 10. Specifically, as the temperature in the space 10 is lower, the self-decomposition coefficient k is smaller, and the self-decomposition of the drug 20 is less likely to proceed. As the temperature in the space 10 becomes higher, the self-decomposition coefficient k becomes larger, and the self-decomposition of the drug 20 becomes easier.

Incidentally, frequency factor for calculating the self-decomposition coefficients k A, etc. the activation energy E _a, is obtained by measuring beforehand according to the following procedure. First, for example, an acrylic box having a volume of 1 m ³ kept at a temperature of 20 ° C. and a humidity of 50% is prepared, and hypochlorous acid gas is stored as a chemical 20 therein. The concentration of hypochlorous acid gas in the acrylic box is measured over time from the initial state. The same measurement is performed under different conditions by changing the combination of temperature and humidity.

The self-decomposition coefficient k is calculated from the reaction rate equation of hypochlorous acid [HClO] = [HClO] _initial × e− ^kt . [HClO] _initial indicates the concentration of hypochlorous acid gas in the initial state. [HClO] indicates the concentration of hypochlorous acid gas after a lapse of time t from the initial state.

A self-decomposition coefficient k corresponding to two or more different temperatures is calculated, and an Arrhenius plot is performed using the calculated self-decomposition coefficient k. The Arrhenius plot is to draw the calculated value in a two-dimensional coordinate system where the horizontal axis is 1 / T and the vertical axis is 1nk. The slope -E _a / R and the intercept lnA are calculated based on the approximate expression of the Arrhenius plot.

  In the present embodiment, determination unit 210 determines self-decomposition coefficient k based on the temperature in space 10. Specifically, the determination unit 210 determines the self-decomposition coefficient k by referring to the first database 231 stored in the storage unit 230.

  The first database 231 is a database indicating the correspondence between the self-decomposition coefficient k and the temperature. Specifically, the first database 231 indicates the self-decomposition coefficient k calculated in advance based on the above equation (3) for each combination of the temperature and the type of the medicine 20. When there is only one kind of the medicine 20, the first database 231 may indicate a self-decomposition coefficient k calculated in advance for each temperature.

  Note that the storage unit 230 may store the above equation (3) instead of the first database 231. The determination unit 210 may calculate the self-decomposition coefficient k using Expression (3) based on the type information and the temperature information acquired by the acquisition unit 110.

  In the present embodiment, by using the self-decomposition coefficient as the input data for the simulation, the decrease in the concentration of the drug 20 due to the self-decomposition can be reflected in the simulation result. Specifically, the larger the self-decomposition coefficient, the smaller the density estimation value is than the density estimation value when the self-decomposition coefficient is not used as input data. As the self-decomposition coefficient is smaller, the density estimation value becomes larger than the density estimation value when the self-decomposition coefficient is not used as input data. In any case, the density close to the actually measured value can be estimated by using the self-decomposition coefficient as input data.

[Diffusion coefficient]
Next, the diffusion coefficient of the medicine 20 will be described.

  The diffusion coefficient is a parameter indicating the degree to which the medicine 20 is diffused. Specifically, the diffusion coefficient is defined by the following Einstein-Stokes equation (4).

  In the equation (4), D is a diffusion coefficient (unit [m / s]) of the medicine 20. k is Boltzmann's constant. T is the absolute temperature in the space 10. B is the mobility of the medicine 20. μ is the viscosity of the drug 20. a is the molecular radius of the drug 20. Here, μ and a are values determined according to the type of the medicine 20.

  As shown in Expression (4), the diffusion coefficient D depends on the temperature in the space 10. Specifically, the lower the temperature in the space 10 is, the smaller the diffusion coefficient is, and the more difficult it is for the medicine 20 to be diffused. As the temperature in the space 10 increases, the diffusion coefficient increases, and the medicine 20 is easily diffused. More specifically, the diffusion coefficient D has a proportional relationship with the absolute temperature T in the space 10.

  In the present embodiment, determination unit 210 determines diffusion coefficient D based on the temperature in space 10. Specifically, the determination unit 210 determines the diffusion coefficient D by referring to the second database 232 stored in the storage unit 230.

  The second database 232 is a database indicating the correspondence between the diffusion coefficient D and the temperature. Specifically, the second database 232 indicates the diffusion coefficient D calculated in advance based on the above equation (4) for each combination of the temperature and the type of the medicine 20. When there is only one kind of the medicine 20, the second database 232 may indicate the diffusion coefficient D calculated in advance for each temperature.

  Note that the storage unit 230 may store the above equation (4) instead of the second database 232. The determination unit 210 may calculate the diffusion coefficient D using Expression (4) based on the type information and the temperature information acquired by the acquisition unit 110.

  In the present embodiment, by using the diffusion coefficient as input data for the simulation, a change in concentration due to diffusion of the medicine 20 can be reflected in the simulation result. Specifically, the larger the diffusion coefficient, the more easily the medicine 20 spreads. For this reason, for example, the estimated concentration value of the medicine 20 at a position distant from the generation source 11 is larger than the estimated concentration value when the diffusion coefficient is not used as input data. Further, the smaller the diffusion coefficient is, the more difficult it is for the medicine 20 to spread. For this reason, for example, the concentration estimate of the medicine 20 at a position distant from the source 11 is smaller than the concentration estimate when the diffusion coefficient is not used as input data. In any case, the density close to the actually measured value can be estimated by using the diffusion coefficient as input data.

[Adsorption release coefficient]
Next, the adsorption / desorption coefficient of the medicine 20 will be described.

  FIG. 6 is a cross-sectional view for explaining the adsorption and release coefficient of the medicine 20 with respect to the predetermined surface 40. The predetermined surface 40 is a building material such as a wall material forming the space 10 or a wall surface of an obstacle such as the generation source 11 located in the space 10.

  As shown in FIG. 6, the medicine 20 located near the surface 40 is adsorbed on the surface 40 like the medicine 20x after the elapse of a predetermined time, and the medicine 20 on the surface 40 without being adsorbed like the medicine 20y. In some cases, it remains in the vicinity.

  The adsorption and release coefficient is a parameter indicating the degree of adsorption and release of the drug 20 on the predetermined surface 40. The adsorption / desorption coefficient is defined as a distance that the medicine 20 approaches the surface 40 per unit time. Specifically, the adsorption / desorption coefficient is defined based on the equation representing the amount of adsorption shown in the following equation (5).

In the equation (5), _hp is the adsorption / desorption coefficient (unit [m / s]) of the drug 20. If the adsorption / desorption coefficient is a positive value, it means that the drug 20 is adsorbed on the surface 40. If the adsorption-release coefficient is a negative value, it means that the drug 20 is released from the surface 40. The larger the absolute value of the adsorption and release coefficient, the larger the amount of adsorption or release. The smaller the absolute value of the adsorption and release coefficient, the smaller the amount of adsorption or release.

F is the amount of adsorption of the medicine 20 (unit [m ³ / s]). ΔC is the concentration difference ratio of the medicine 20. A is the area (unit [m ² ]) of the surface 40 shown in FIG.

  The adsorption of the drug 20 includes physical adsorption and chemical adsorption. Physical adsorption means, for example, that the drug 20 is captured by minute pores or irregularities provided on the surface 40. The physical adsorption is determined based on, for example, the BET adsorption isotherm shown in the following equation (6).

In the equation (6), p ₀ is the saturated vapor pressure of the medicine 20. p is the vapor pressure of the medicine 20. v ₁ is the saturated adsorption amount. v is the amount of adsorption. c is represented by the following equation (7).

In equation (7), c ₀ is a constant. R is a gas constant. T is the absolute temperature. E ₁ is the heat of adsorption of the drug 20 on the surface 40. E is the heat of condensation of the medicine 20. Here, E ₁ and E is a value determined according to the type of drug 20.

  The chemical adsorption means that the drug 20 is captured by a chemical bond such as an ionic bond with minute water or the like attached to the surface 40, for example. The amount of adsorption in the chemical adsorption depends on the surface temperature of the surface 40 or the air temperature near the surface 40.

In this embodiment, the suction discharge coefficient h _p is determined as the macroscopic factors comprehensively adsorption factors such as physical adsorption and chemical adsorption. Specifically, by converting the moving distance of the drug 20 on the basis of the density difference between the ends of the surface 40 of the area A shown in FIG. 6, it is defined adsorption emission coefficient h _p. Specifically, using equation (5) based on the measured value of the concentration is determined adsorption emission coefficient h _p.

Figure 7 is a sectional view for explaining a concentration difference ratio ΔC used in determining the adsorption emission coefficient h _p of the drug 20 for a given surface 40. As shown in FIG. 7, the space near the surface 40 is virtually defined as a first area and a second area.

The first area is an area facing the surface 40. The second area is an area farther from the surface 40 than the first area, and shares a face with the first area. The first area and the second area are, for example, rectangular parallelepiped spaces having the same size. The width w of the first zone is smaller than the adsorption or desorption coefficient h _p × analysis time, for example, is 1mm or less.

  At this time, the density difference ratio ΔC is represented by the following equation (8).

In Expression (8), C _1t and C _2t are the concentrations of the medicines 20 in the first zone and the second zone at the time t, respectively. From the expressions (5) and (8), it can be seen that the larger the concentration C _2t of the second area is, the larger the amount of adsorption to the surface 40 is.

Figure 8 is a graph showing the temperature dependence of the adsorption or desorption coefficients h _p. 8, the horizontal axis is the temperature, the vertical axis represents the adsorption or desorption coefficient h _p. As shown in FIG. 8, as the temperature increases, the adsorption emission coefficient h _p becomes small, hardly occurs adsorption to the surface 40 of the drug 20. Higher the temperature becomes lower, the suction discharge coefficient h _p becomes large, prone to adsorb to the surface 40 of the drug 20. The adsorption / desorption coefficient and the temperature have a substantially linear relationship.

In this embodiment, determining section 210, the material of the surface 40, the shape determines the adsorption emission coefficient h _p based on at least one of temperature and humidity. Specifically, determining unit 210 refers to the third database 233 stored in the storage unit 230, determines the adsorption emission coefficient h _p.

The third database 233 is a database indicating correspondence between the adsorption or desorption coefficient h _p and temperature. Specifically, the third database 233 includes a predetermined adsorption / desorption coefficient h based on the relationship shown in FIG. 8 and the like for each combination of the material, shape, temperature, and humidity of the surface 40, and the type of the medicine 20. _p is shown. Incidentally, when the type of the agent 20 is only one, the third database 233, the material of the surface 40, the shape, even shows a temperature and humidity adsorption emission coefficient h _p which is previously determined for each combination of Good.

In order to save memory resources, the third database 233, instead of showing the adsorption emission coefficient h _p corresponding to all combinations, and shows the adsorption emission coefficient h _p corresponding to a part of a combination Is also good.

  For example, in the third database 233, when the temperature is 20 ° C. and the humidity is 50% RH, the adsorption and emission coefficients of the material A, the material B, and the material C are 0.0006 m / s and 0.0011 m / s, respectively. s and 0.02 m / s. The determining unit 210 corrects the values read from the third database 233 based on the function representing the correspondence between each of the shape, the temperature, and the humidity of the surface 40 and the adsorption / desorption coefficient, so that the determination unit 210 responds to the input data. The calculated adsorption / desorption coefficient may be calculated. Alternatively, the determination unit 210 may calculate the adsorption / desorption coefficient according to the input data by an interpolation process using the value stored in the third database 233.

For example, the adsorption / desorption coefficient and the temperature have a substantially linear relationship as shown in FIG. Specifically, as the temperature increases, the adsorption emission coefficient h _p becomes small, hardly occurs adsorption to the surface 40 of the drug 20. Higher the temperature becomes lower, the suction discharge coefficient h _p becomes large, prone to adsorb to the surface 40 of the drug 20.

Moreover, as the humidity increases, an increase in the surface moisture content of the surface 40, because the drug 20 is likely to be trapped, macroscopically adsorption emission coefficient h _p becomes larger. That is, as the humidity increases, the drug 20 is more likely to be adsorbed on the surface 40 or less likely to be released. Higher the humidity is low, reduces the surface moisture content of the surface 40, because the drug 20 is less likely to be caught, macroscopically adsorption emission coefficient h _p becomes smaller. In other words, the lower the humidity, the harder the adsorption of the drug 20 to the surface 40 or the easier the release.

Also, the more the surface of the surface 40 a shape in which rough, specifically, the larger the surface roughness of the surface 40, the suction discharge coefficient h _p becomes large, prone to adsorb to the surface 40 of the drug 20. The smaller the surface roughness of the surface 40, the suction discharge coefficient h _p becomes small, hardly occurs adsorption to the surface 40 of the drug 20.

  In the present embodiment, by using the adsorption / desorption coefficient as input data for the simulation, a decrease in the concentration of the drug 20 due to the adsorption on the surface 40 can be reflected in the simulation result. Specifically, as the adsorption / desorption coefficient is larger, the concentration estimation value becomes smaller than the concentration estimation value when the adsorption / desorption coefficient is not used as input data. The smaller the adsorption / desorption coefficient, the larger the concentration estimation value is than the concentration estimation value when the adsorption / desorption coefficient is not used as input data. In any case, the concentration close to the actually measured value can be estimated by using the adsorption / desorption coefficient as the input data.

[Effect of environment on drugs and bacteria]
Next, the effect of the environment on the medicine 20 and the bacteria 30 will be described. In the present embodiment, the environment that affects the medicine 20 and the bacterium 30 includes air temperature, humidity and the amount of ultraviolet rays, surface temperature, and the amount of organic substances.

[Temperature and surface temperature]
First, the effect of the temperature and the surface temperature on the drug 20 and the bacteria 30 will be described with reference to FIG.

  FIG. 9 is a diagram showing the temperature dependence of the survival state of the bacterium 30. In FIG. 9, the horizontal axis is temperature. The temperature is the temperature of the air with which the bacteria 30 are in contact, and the surface temperature of the surface 40 of the member with which the bacteria 30 are in contact. In FIG. 9, the vertical axis indicates the easiness of survival of the bacterium 30, that is, the difficulty of the bacterium 30 to die. The larger the value on the vertical axis, the easier the bacteria 30 survive.

  As shown in FIG. 9, the lower the temperature, the easier the bacteria 30 survive, and the higher the temperature, the harder the bacteria 30 survive. In other words, the lower the temperature is, the harder the bacteria 30 are to be eliminated, and the higher the temperature, the easier the bacteria 30 are to be eliminated. This is presumably because the higher the temperature, the more heat energy is given to both the drug 20 and the bacterium 30, the reactivity between the drug 20 and the bacterium 30 increases, and the sterilization performance increases.

  From the above, the environmental factor f in the above equation (1) is set to a smaller value as the temperature is higher, and is set to a larger value as the temperature is lower. As a result, the reduced number Y of the bacteria 30 calculated using the equation (1) becomes a value in which the influence of the temperature is taken into consideration. Therefore, according to the bacteria elimination performance prediction system 100 according to the present embodiment, the prediction accuracy of the survival state of the bacteria 30 can be improved.

[Humidity]
Next, the effect of humidity on the medicine 20 and the bacteria 30 will be described with reference to FIG.

  FIG. 10 is a schematic diagram showing a state in which the bacteria 30 are covered with water droplets 50. When the humidity of the space 10 is high, the amount of surface moisture increases. For this reason, as shown in FIG. 10, the water droplet 50 adheres to the surface 40, and the bacteria 30 are easily covered by the water droplet 50.

  As an effect of humidity on the germs 30, there is an effect that the germs 30 swell due to the water droplets 50. At this time, the permeability of the cell membrane of the bacterium 30 increases, and the drug 20 due to the osmotic pressure easily permeates. For this reason, it is considered that the reactivity between the drug 20 and the bacterium 30 increases, and the bactericidal performance increases.

  Further, as an effect of humidity on the medicine 20, there is an effect that the medicine 20 is easily captured by the water droplets 50 covering the bacteria 30. The amount of the drug 20 captured by the water droplet 50 increases, and the drug concentration near the bacterium 30 increases. Therefore, it is considered that the contact time between the drug 20 and the bacterium 30 becomes longer, and the bactericidal performance is enhanced.

  FIG. 11 is a diagram showing the humidity dependence of the survival state of the bacteria 30. In FIG. 11, the horizontal axis is humidity. The vertical axis indicates the ease with which the bacteria 30 can survive, that is, the difficulty with which the bacteria 30 die. The larger the value on the vertical axis, the easier the bacteria 30 survive.

  As shown in FIG. 11, the bacteria 30 are more likely to survive as the humidity is lower, and the bacteria 30 are more difficult to survive as the humidity is higher. In other words, the lower the humidity is, the harder the bacteria 30 are to be removed, and the higher the humidity, the easier the bacteria 30 are to be removed.

  From the above, the environmental factor f in the above equation (1) is set to a smaller value as the humidity is higher, and is set to a larger value as the humidity is lower. As a result, the reduced number Y of the bacteria 30 calculated using the equation (1) becomes a value in which the influence of humidity is considered. Therefore, according to the bacteria elimination performance prediction system 100 according to the present embodiment, the prediction accuracy of the survival state of the bacteria 30 can be improved.

[Ultraviolet ray amount]
Next, the effect of the amount of ultraviolet rays on the medicine 20 and the bacteria 30 will be described with reference to FIG.

  First, the effect of the ultraviolet rays 60 on the medicine 20 is that the ultraviolet rays 60 are absorbed by the medicine 20 and decompose the medicine 20. FIG. 12 is a schematic diagram showing a state in which the medicine 20 and the bacteria 30 are irradiated with ultraviolet rays 60. FIG. 12 shows a state in which the medicine 20 irradiated with the ultraviolet rays 60 is decomposed and disappears.

  FIG. 13 is a diagram showing the wavelength dependence of ultraviolet absorption by hypochlorous acid. In FIG. 13, the horizontal axis represents the wavelength of the ultraviolet ray 60, and the vertical axis represents the absorbance of the ultraviolet ray by hypochlorous acid.

  As shown in FIG. 13, hypochlorous acid has an absorption peak at a wavelength of about 290 nm. Hypochlorous acid, when irradiated with ultraviolet light, absorbs the ultraviolet light and is decomposed. Although FIG. 13 shows a graph of the absorbance for each concentration of hypochlorous acid, there is a tendency that hypochlorous acid absorbs ultraviolet light at any concentration.

  As the amount of ultraviolet rays increases, the drug 20 is more easily decomposed, and the drug concentration decreases. As the amount of ultraviolet light is smaller, the drug 20 is less likely to be decomposed and the drug concentration is less likely to decrease. Therefore, in consideration of the influence on the medicine 20, the environmental factor f in the equation (1) is set to a larger value as the amount of ultraviolet rays is larger, and is set to a smaller value as the amount of ultraviolet rays is smaller. In addition, as shown in FIG. 13, since the absorbance has wavelength dependence, the environmental factor f may be set to a value corresponding to the wavelength of the ultraviolet light 60, for example.

  In addition, as an effect of the ultraviolet rays 60 on the bacteria 30, the ultraviolet rays 60 are absorbed by DNA and proteins constituting the bacteria 30 and have an effect of weakening them. FIGS. 14 and 15 are diagrams showing the wavelength dependence of ultraviolet absorption by DNA and protein, respectively. In each of FIG. 14 and FIG. 15, the horizontal axis is the wavelength of ultraviolet light and visible light, and the vertical axis is the absorbance of ultraviolet light by DNA or protein.

  As shown in FIG. 14, DNA has an absorption peak at a wavelength of about 260 nm. DNA, when exposed to UV light, absorbs the UV light and damages or weakens it. As shown in FIG. 15, the protein has an absorption peak at a wavelength of about 280 nm. Proteins, when exposed to UV light, absorb the UV light and become damaged or weakened.

  The greater the amount of ultraviolet light, the more easily the bacteria 30 are damaged or weakened, and the more likely they are to die. As the amount of ultraviolet light is smaller, the bacteria 30 are less likely to be damaged or weakened, and are less likely to die. Therefore, when the influence on the bacteria 30 is taken into consideration, the environmental factor f in the equation (1) is set to a smaller value as the amount of ultraviolet light is larger, and is set to a larger value as the amount of ultraviolet light is smaller. Note that, as shown in FIGS. 14 and 15, since the absorbance has wavelength dependence, the environmental factor f may be set to a value corresponding to the wavelength of the ultraviolet light 60, for example.

  Note that the relationship between the amount of ultraviolet rays and the environmental factor f differs between the effect on the medicine 20 and the effect on the bacteria 30. For this reason, the environmental factor f is set according to the magnitude relationship of the effect of the ultraviolet rays 60 on each of the medicine 20 and the bacteria 30. For example, when the decomposition of the drug 20 is more intense than the weakening of the bacterium 30 by the ultraviolet rays 60, the bacteria elimination performance of the bacterium 30 is reduced, so the environmental factor f is set to a large value. Further, if the bacteria 30 are more weakly weakened than the decomposition of the medicine 20 by the ultraviolet rays 60, the bacteria elimination performance of the bacteria 30 will be higher, so the environmental factor f is set to a small value. As described above, according to the bacteria elimination performance prediction system 100 according to the present embodiment, it is possible to improve the prediction accuracy of the survival state of the bacteria 30.

[Amount of organic matter]
Next, the effect of the amount of the organic substance on the medicine 20 will be described with reference to FIG.

  FIG. 16 is a schematic diagram showing a state in which the bacteria 30 are covered with the organic substance 70. As shown in FIG. 16, when the bacteria 30 are covered with the organic matter 70, the medicine 20 becomes difficult to reach the bacteria 30. Specifically, when the drug 20 permeates the organic substance 70, the amount of the drug 20 that reacts with the organic substance 70 and reaches the bacteria 30 is reduced. Therefore, it is considered that the larger the amount of the organic substance 70, the less the contact between the drug 20 and the bacterium 30 and the lower the bactericidal performance.

  From the above, the environmental factor f in the above equation (1) is set to a larger value as the amount of organic matter is larger, and is set to a smaller value as the amount of organic matter is smaller. As a result, the reduced number Y of the bacteria 30 calculated using the equation (1) becomes a value in consideration of the influence of the organic matter. Therefore, according to the bacteria elimination performance prediction system 100 according to the present embodiment, the prediction accuracy of the survival state of the bacteria 30 can be improved.

[motion]
Next, the operation of the eradication performance predicting system 100 according to the present embodiment will be described using FIG. 17 and FIG.

  FIG. 17 is a flowchart showing the operation of the eradication performance predicting system 100 according to the present embodiment. FIG. 18 is a flowchart showing a concentration estimation process in the operation of the sterilization performance prediction system 100 according to the present embodiment.

  As shown in FIG. 17, first, the acquisition unit 110 acquires environment information, medicine information, and bacteria information (S10). Specifically, the obtaining unit 110 obtains temperature information, humidity information, and ultraviolet information as environmental information from a temperature and humidity sensor and an ultraviolet light meter provided in the space 10. The acquisition unit 110 further acquires type information and airflow information of the medicine 20 from the generation source 11 as medicine information. The obtaining unit 110 further obtains information indicating the type of the bacterium 30 from a terminal device or the like as germ information. Further, the acquisition unit 110 acquires the geometry information and the material information of the space 10.

  Next, the concentration estimating unit 121 calculates a drug concentration by performing a simulation based on the information acquired by the acquiring unit 110 (S20). Specifically, the density estimating unit 121 performs a density estimating process illustrated in FIG.

As shown in FIG. 18, first, the density estimating unit 121 acquires input data of a density estimation simulation (S22). Specifically, determining unit 210, based on such environmental information and drug information to determine autolysis coefficient k, the diffusion coefficient D and adsorption emission coefficient h _p.

Next, the calculation unit 220 calculates the concentration of the medicine 20 at the target position in the space 10 (S24). Specifically, the arithmetic unit 220 acquires temperature information, humidity information, ultraviolet information, geometry information and material information, and, autolysis coefficient k determined, the diffusion coefficient D and the input data adsorption emission coefficient h _p To get as The calculation unit 220 calculates a drug concentration for each subspace by performing CFD analysis based on the acquired input data.

  Referring back to FIG. 17, the prediction unit 122 predicts the survival state of the bacterium 30 by referring to the CT value database 131 based on the calculated drug concentration, the type of the bacterium 30, and the environmental information (S30). . Specifically, the prediction unit 122 calculates the number of reduced bacteria 30 based on the above equation (1). Alternatively, the prediction unit 122 may calculate the eradication rate of the bacteria 30 based on the above equation (2).

  Finally, the output unit 140 outputs the calculated number of bacteria 30 reduced or the eradication rate as prediction information (S40). The output unit 140 may display, for example, a three-dimensional distribution map of the eradication rate shown in FIG.

  FIG. 19 is a three-dimensional distribution diagram of the disinfection rate output by the disinfection performance prediction system 100 according to the present embodiment. In FIG. 19, the density difference is represented by the dot density difference.

  The three-dimensional distribution map is, for example, a three-dimensional heat map that indicates the level of the bacteria elimination rate based on the color or brightness. In addition, the output unit 140 may output a two-dimensional distribution map of the disinfection rate cut out on an arbitrary plane. Alternatively, the output unit 140 may display, for example, a graph showing the time change of the sterilization rate at an arbitrary position. Further, the output unit 140 may display a three-dimensional distribution map of the number of reduced bacteria 30 instead of the bacteria removal rate. The output unit 140 may display a three-dimensional distribution map of the drug concentration.

[Effects, etc.]
As described above, the eradication performance predicting system 100 according to the present embodiment provides the CT value database 131 in which the type of the evacuation is associated with the eradication index value which is the product of the concentration of the drug required for eradication and the time. And a control unit 120. The control unit 120 calculates a drug concentration at a predetermined position in the space 10 where the medicine 20 is sprayed, a concentration estimating unit 121, the calculated drug concentration, the type of the bacterium 30 to be sterilized, A prediction unit 122 that predicts the survival state of the bacterium 30 at a predetermined position by referring to the CT value database 131 based on environment information indicating the environment of the germ 30.

  Thereby, based on the environmental information, it is possible to predict the survival state of the bacteria 30 in consideration of the effect of the environment on the drug 20 or the bacteria 30. Therefore, according to the bacteria elimination performance prediction system 100, the survival state of the bacteria 30 can be accurately predicted.

  Also, for example, in the CT value database 131, the type of bacteria and the eradication index value are associated with each other for each type of drug. The prediction unit 122 predicts the survival state of the bacterium 30 by further referring to the CT value database 131 based on the drug information indicating the type of the drug 20 to be scattered in the space 10.

  Thereby, for example, when the type of the medicine 20 to be sprayed on the space 10 can be changed, the survival state of the bacteria 30 can be accurately predicted according to the type of the changed medicine 20.

  In addition, for example, the environmental information includes first environmental information that affects the state of the bacterium 30 to be disinfected and second environmental information that affects the medicine 20 that is sprayed on the space 10.

  This makes it possible to predict the survival state of the bacterium 30 in consideration of both the effect of the environment on the drug 20 and the effect of the environment on the bacterium 30. Therefore, according to the bacteria elimination performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

  Further, for example, the first environment information is information indicating at least one of the temperature, humidity, and ultraviolet ray amount at a predetermined position, and the surface temperature when the predetermined position includes the surface 40 of the member existing in the space 10. It is.

  Thus, the survival state of the bacterium 30 can be predicted in consideration of at least one of the temperature, the humidity, the amount of ultraviolet light, and the surface temperature, which greatly affect the bacterium 30. Therefore, according to the bacteria elimination performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

  Further, for example, the second environment information includes the temperature, humidity, and ultraviolet ray amount at a predetermined position, and the surface temperature and the organic matter attached to the surface 40 when the predetermined position includes the surface 40 of the member existing in the space 10. Information indicating at least one of the quantities.

  Thus, the survival state of the bacterium 30 can be predicted in consideration of at least one of the temperature, the humidity, the amount of ultraviolet light, the surface temperature, and the amount of organic matter, which greatly affect the medicine 20. Therefore, according to the bacteria elimination performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

  Further, for example, the sterilization performance prediction system 100 may further include a sensor for acquiring environmental information.

  Thereby, environmental information can be acquired with high accuracy using the sensor. Since the accuracy and reliability of the environmental information are high, the accuracy of the prediction result based on the environmental information can be increased.

  In addition, for example, the concentration estimating unit 121 may determine the amount of the medicine 20 generated, the wind direction and the wind speed of the airflow that moves the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, the predetermined surface in the space 10, and the like. And the at least one of the temperature, humidity, and the amount of ultraviolet light in the space 10 are obtained as input data, and a drug concentration is calculated using the obtained input data.

  Thereby, the concentration of the medicine 20 to be sprayed in the space 10 can be accurately estimated. According to the concentration estimating method according to the present embodiment, it is possible to accurately estimate the concentration of the drug 20 diffused in the space 10 without needing to actually measure the concentration of the drug 20. Therefore, the survival state of the bacterium 30 can be accurately predicted based on the highly accurate drug concentration.

  Further, for example, in the method for estimating the sterilization performance according to the present embodiment, the step of calculating the drug concentration at a predetermined position in the space 10 where the drug 20 is sprayed, the calculated drug concentration, and the sterilization target. A CT value database in which the type of the bacterium 30 and the eradication index value which is the product of the concentration of the drug required for the eradication and the time are associated based on the type of the bacterium 30 and the environmental information indicating the environment in the space 10. A prediction step of predicting the survival state of the bacterium 30 at a predetermined position by referring to the reference 131.

  Thereby, similarly to the sterilization performance prediction system 100, the survival state of the bacteria 30 can be accurately predicted.

(Embodiment 2)
Next, a second embodiment will be described.

  In the sterilization performance prediction system 100 according to the first embodiment, temperature information and the like obtained from sensors and the like are obtained as environment information. On the other hand, in the sterilization performance prediction system according to the present embodiment, space information indicating the type of the space 10 in which the medicine 20 is sprayed is acquired as environment information. Thereby, the sterilization performance prediction system according to the present embodiment can reduce the processing amount with a small amount of information and can accurately predict the survival state of the bacteria 30.

  FIG. 20 is a block diagram showing a functional configuration of the sterilization performance prediction system 300 according to the present embodiment. As shown in FIG. 20, the sterilization performance prediction system 300 includes an acquisition unit 310, a control unit 320, a storage unit 330, and an output unit 140. The following description focuses on differences from the first embodiment, and description of common points is omitted or simplified.

  The acquisition unit 310 acquires environment information, medicine information, and bacteria information. The acquisition of the medicine information and the bacteria information is the same as that of the acquisition unit 110 of the first embodiment. In the present embodiment, acquisition section 310 acquires spatial information as environment information. The space information is an example of environment information, and is information indicating the type of the space 10 in which the medicine 20 is sprayed.

  The acquisition unit 310 is realized by, for example, a communication interface that communicates with a terminal device such as a smartphone operated by a user, or a user interface such as a touch panel or physical operation buttons. For example, the acquisition unit 310 causes a user to input information for specifying the space 10 such as the name of the space 10 via a terminal device or the like, and obtains the input information as space information. Alternatively, the acquisition unit 310 may display a selection screen including a plurality of scenes prepared in advance on a display or the like, and allow the user to select a scene suitable as a space in which the medicine 20 is sprayed. The acquisition unit 310 may select an instruction to select one scene from a plurality of prepared scenes as spatial information.

  The control unit 320 is different from the control unit 120 according to the first embodiment in that the control unit 320 includes a prediction unit 322 instead of the prediction unit 122. The prediction unit 322 predicts the survival state of the fungus 30 by referring to the scene database 331 stored in the storage unit 330 based on the space information acquired by the acquisition unit 310 as the environment information. Specifically, the prediction unit 322 refers to the scene database 331 based on the spatial information, and determines the environmental factor f in Expression (1) shown in the first embodiment. The prediction unit 322 predicts the survival state of the bacterium 30 by using Expression (1) based on the determined environmental factor f.

  The storage unit 330 further stores a scene database 331. The storage unit 330 is realized by a nonvolatile memory such as an HDD and a semiconductor memory.

  The scene database 331 stores, for each type of the space 10, that is, for each scene, a predetermined temperature, humidity, and ultraviolet light amount, a surface temperature of a member existing in the space, and an organic substance attached to the surface of the member. It is an example of scene information associated with at least one of the amounts. FIG. 21 is a diagram showing a scene database 331 stored in the sterilization performance prediction system 300 according to the present embodiment.

  In the scene database 331 shown in FIG. 21, three scenes of a kitchen, a living room, and a toilet are prepared in advance. For each of the three scenes, a temperature, a humidity, an ultraviolet ray amount, an organic matter amount, and a weighting coefficient are stored in association with each other. The weighting factor is an environmental factor f.

  The weighting coefficient stored in the scene database 331 is a value determined in advance based on the corresponding temperature, humidity, ultraviolet light amount, and organic matter amount. The specific determination method is the same as the method for determining the environmental factor f described in the first embodiment.

  The temperature, humidity, ultraviolet light amount, and organic matter amount stored in the scene database 331 are each representative values in the corresponding scene. The representative value can be obtained, for example, by actually measuring each of the temperature, the humidity, the amount of ultraviolet rays, and the amount of organic matter in a space simulating the corresponding scene. For example, the representative value corresponding to a kitchen may be a value obtained by averaging values actually measured in a plurality of types of kitchens.

  The representative values of the temperature, humidity, ultraviolet light amount, and organic matter amount stored in the scene database 331 may be updated according to the space 10. The environmental factor f is redetermined and updated when the representative value is updated.

  As described above, in the sterilization performance prediction system 300 according to the present embodiment, for example, the storage unit 330 further includes a predetermined temperature, humidity, and ultraviolet light amount, and the space 10 A scene database 331 is stored in which at least one of the surface temperature of the member existing in the inside and the amount of organic substances attached to the surface 40 of the member is associated. The environment information is information indicating the type of the space 10 in which the medicine 20 is sprayed. The prediction unit 122 predicts the survival state of the bacteria 30 by further referring to the scene database 331 based on the environment information.

  Thus, since the environmental factor f is determined in advance for each scene, the environmental factor f is determined, for example, only by the user selecting a scene and not requiring measured values such as temperature. Therefore, the amount of calculation can be reduced, and the survival state of the bacterium 30 can be accurately calculated.

(Other)
As described above, the bacteria elimination performance prediction system and the bacteria elimination performance prediction method according to the present invention have been described based on the above embodiments, but the present invention is not limited to the above embodiments.

  For example, in the above-described embodiment, an example is described in which the storage unit 130 stores the CT value database 131 indicating the CT values corresponding to the types of bacteria and the types of drugs, but the present invention is not limited to this. For example, when the medicine 20 to be used is fixed at a specific type, the storage unit 130 may store association information in which the type of bacteria is associated with a CT value.

  Further, for example, in the above embodiment, the environmental information affecting both the medicine 20 and the bacterium 30 is acquired, but only the environmental information affecting the medicine 20 or only the environmental information affecting the germ 30 is obtained. May be obtained.

  Further, for example, in the above-described embodiment, an example is described in which the estimation of the drug concentration is performed by the CFD analysis, but the present invention is not limited to this. For example, it may be estimated that the medicine is evenly diffused in the space 10 based on the amount of the medicine 20 generated.

  Further, the communication method between the devices described in the above embodiment is not particularly limited. When wireless communication is performed between devices, the wireless communication method (communication standard) is, for example, short-range wireless communication such as Zigbee (registered trademark), Bluetooth (registered trademark), or wireless LAN (Local Area Network). is there. Alternatively, the wireless communication method (communication standard) may be communication via a wide area communication network such as the Internet. Wired communication may be performed between the devices instead of wireless communication. Specifically, the wired communication is power line communication (PLC) or communication using a wired LAN.

  Further, in the above-described embodiment, a process executed by a specific processing unit may be executed by another processing unit. Further, the order of the plurality of processes may be changed, or the plurality of processes may be executed in parallel. The distribution of components included in the sterilization performance prediction system to a plurality of devices is an example. For example, components included in one device may be included in another device. Further, the sterilization performance prediction system may be realized as a single device.

  For example, the processing described in the above embodiment may be realized by centralized processing using a single device (system), or may be realized by distributed processing using a plurality of devices. Good. In addition, the number of processors that execute the program may be one or more. That is, centralized processing or distributed processing may be performed.

  Further, in the above embodiment, all or a part of the components such as the control unit may be configured by dedicated hardware, or may be realized by executing a software program suitable for each component. Is also good. Each component may be realized by a program execution unit such as a CPU (Central Processing Unit) or a processor reading and executing a software program recorded on a recording medium such as an HDD or a semiconductor memory.

  In addition, components such as the control unit may be configured by one or a plurality of electronic circuits. Each of the one or more electronic circuits may be a general-purpose circuit or a dedicated circuit.

  The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large scale integration (LSI). The IC or LSI may be integrated on one chip, or may be integrated on a plurality of chips. Here, the term is referred to as an IC or an LSI, but the term varies depending on the degree of integration, and may be referred to as a system LSI, a VLSI (Very Large Scale Integration), or a ULSI (Ultra Large Scale Integration). Further, an FPGA (Field Programmable Gate Array) programmed after manufacturing the LSI can be used for the same purpose.

  Further, general or specific aspects of the present invention may be realized by a system, an apparatus, a method, an integrated circuit, or a computer program. Alternatively, the present invention may be realized by a non-transitory computer-readable recording medium such as an optical disk, an HDD, or a semiconductor memory in which the computer program is stored. Further, the present invention may be realized by an arbitrary combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

  In addition, a form obtained by performing various modifications that can be conceived by those skilled in the art to each embodiment, and a combination of components and functions in each embodiment without departing from the spirit of the present invention are realized. Embodiments are also included in the present invention.

10 Space 20, 20a, 20b, 20c, 20d, 20x, 20y Drug 30 Bacteria 40 Surface 100, 300 Eradication performance prediction system 120, 320 Control unit 121 Concentration estimation unit 122, 322 Prediction unit 130, 330 Storage unit 131 CT value Database (correspondence information)
331 Scene database (scene information)

Claims (10)

A storage unit that stores correspondence information in which a type of bacteria and a bacteria removal index value that is a product of a drug concentration and time required for bacteria removal are associated with each other.
And a control unit,
The control unit includes:
A concentration estimating unit that calculates a drug concentration at a predetermined position in the space where the medicine is sprayed,
By referring to the correspondence information based on the calculated drug concentration, the type of bacteria to be removed, and environmental information indicating the environment in the space, the survival state of the bacteria at the predetermined position is predicted. A disinfection performance prediction system comprising a prediction unit. In the correspondence information, for each type of drug, the type of bacteria and the eradication index value are associated,
The disinfection performance prediction system according to claim 1, wherein the prediction unit predicts the survival state of the bacterium by further referring to the correspondence information based on drug information indicating a type of the drug to be sprayed in the space. The environmental information includes:
First environmental information that affects the state of the bacteria to be disinfected;
The disinfection performance prediction system according to claim 1 or 2, further comprising second environmental information that affects the medicine sprayed in the space. The first environment information is information indicating at least one of a temperature, a humidity, an amount of ultraviolet rays at the predetermined position, and a surface temperature when the predetermined position includes a surface of a member existing in the space. The disinfection performance prediction system according to claim 3. The second environment information includes the temperature, humidity, and amount of ultraviolet light at the predetermined position, and the surface temperature and the amount of organic substances attached to the surface when the predetermined position includes the surface of a member existing in the space. The disinfection performance prediction system according to claim 3 or 4, wherein the information indicates at least one of the information. The storage unit may further include, for each type of space, at least one of a predetermined temperature, humidity, and amount of ultraviolet light, and a surface temperature of a member existing in the space and an amount of an organic substance attached to the surface of the member. Stores the scene information associated with the
The environment information is information indicating a type of a space in which the medicine is sprayed,
The disinfection performance prediction system according to claim 1, wherein the prediction unit predicts the survival state of the bacteria by further referring to the scene information based on the environment information. The disinfection performance prediction system according to any one of claims 1 to 6, further comprising a sensor for acquiring the environmental information. The concentration estimating unit, the generation amount of the medicine, the wind direction and velocity of the air flow that moves the medicine, the self-decomposition coefficient of the medicine, the diffusion coefficient of the medicine, and the medicine of the medicine to a predetermined surface in the space The adsorption / desorption coefficient and at least one of the temperature, humidity and ultraviolet ray amount in the space are obtained as input data, and the drug concentration is calculated using the obtained input data. The described eradication performance prediction system. Calculating the drug concentration at a predetermined position in the space where the drug is sprayed,
Based on the calculated drug concentration, the type of bacteria to be removed, and environmental information indicating the environment in the space, the product of the type of bacteria, the concentration of the drug required for bacteria removal and time, A prediction step of predicting the survival state of the bacterium at the predetermined position by referring to correspondence information in which the bacterium index value is associated with the bacterium index value.   A program for causing a computer to execute the sterilization performance prediction method according to claim 9.

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