CN111051878A - Sterilization performance prediction system and sterilization performance prediction method - Google Patents

Abstract

A sterilization performance prediction system (100) is provided with: a storage unit (130) that stores a CT value database (131) that associates the types of bacteria with a sterilization index value that is the product of the drug concentration and the time required for sterilization; and a control unit (120), wherein the control unit (120) comprises: a concentration estimation unit (121) that calculates the concentration of the drug at a predetermined position within the space (10) in which the drug (20) is dispersed; and a prediction unit (122) which predicts the survival state of bacteria at a predetermined position by referring to the CT value database (131) on the basis of the calculated drug concentration, the type of bacteria (30) to be sterilized, and environmental information indicating the environment in the space (10).

Description

Sterilization performance prediction system and sterilization performance prediction method

Technical Field

The invention relates to a sterilization performance prediction system and a sterilization performance prediction method.

Background

Conventionally, the following processes are performed: the amount of the drug deposited at the predetermined position to be dispersed is calculated by performing a computational fluid dynamics analysis, and the survival state of the bacteria at the predetermined position is predicted based on the calculated amount of the deposited drug and the bacterial species (see, for example, patent document 1).

Documents of the prior art

Patent document

Patent document 1: japanese patent laid-open publication No. 2013-190381

Disclosure of Invention

Problems to be solved by the invention

However, the above-described conventional techniques have a problem that the prediction accuracy of the living state of bacteria is low.

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

Means for solving the problems

In order to achieve the above object, a sterilization performance prediction system according to an aspect of the present invention includes: a storage unit that stores correspondence information that associates a type of bacteria with a sterilization index value that is a product of a drug concentration and time required for sterilization; and a control unit, wherein the control unit includes: a concentration estimation unit that calculates a concentration of the drug at a predetermined position in a space where the drug is dispersed; and a prediction unit that predicts the survival state of the bacteria at the predetermined position with reference to the correspondence information based on the calculated drug concentration, the type of bacteria to be sterilized, and environment information indicating the environment in the space.

In addition, a sterilization performance prediction method according to an embodiment of the present invention includes the steps of: calculating a concentration of the medicament at a prescribed location within the space in which the medicament is dispersed; and predicting the survival state of the bacteria at the predetermined position by referring to correspondence information that associates the type of the bacteria with a bacteria elimination index value that is a product of the concentration of the drug required for the bacteria elimination and time, based on the calculated drug concentration, the type of the bacteria to be sterilized, and the environment information indicating the environment in the space.

In addition, one embodiment of the present invention can be realized as a program for causing a computer to execute the sterilization performance prediction method described above. Alternatively, the program may be implemented in a computer-readable recording medium storing the program.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the present invention, the survival state of bacteria can be predicted with high accuracy.

Drawings

Fig. 1 is a block diagram showing a functional configuration of a sterilization performance prediction system according to embodiment 1.

Fig. 2 is a diagram schematically showing a space to be estimated by the sterilization performance prediction system according to embodiment 1 and states of medicines generated in the space.

Fig. 3 is a diagram schematically showing bacteria present on the surface of a member in a space to be estimated in the sterilization performance prediction system according to embodiment 1 and a chemical agent generated in the space.

Fig. 4 is a diagram showing an example of the CT value database stored in the sterilization performance prediction system according to embodiment 1.

Fig. 5 is a block diagram showing a functional configuration of a density estimating unit of the sterilization performance predicting system according to embodiment 1.

Fig. 6 is a sectional view for explaining an adsorption/release coefficient of a substance with respect to a predetermined plane.

Fig. 7 is a cross-sectional view for explaining a concentration difference ratio used when determining an adsorption/release coefficient of a substance with respect to a predetermined plane.

Fig. 8 is a graph showing the temperature dependence of the adsorption-release coefficient.

FIG. 9 is a graph showing the temperature dependence of the survival state of bacteria.

Fig. 10 is a schematic diagram showing a state where bacteria are covered with water droplets.

FIG. 11 is a graph showing the humidity dependence of the survival state of bacteria.

Fig. 12 is a schematic diagram showing a case where ultraviolet rays are irradiated to the drug and the bacteria.

Fig. 13 is a graph showing the wavelength dependence of hypochlorous acid in absorbing ultraviolet rays.

FIG. 14 is a graph showing the wavelength dependence of DNA absorbing ultraviolet light.

Fig. 15 is a graph showing the wavelength dependence of ultraviolet light absorption by proteins.

FIG. 16 is a schematic view showing a case where bacteria are covered with an organic substance.

Fig. 17 is a flowchart showing the operation of the sterilization performance prediction system according to embodiment 1.

Fig. 18 is a flowchart showing the concentration estimation process in the operation of the sterilization performance prediction system according to embodiment 1.

FIG. 19 is a three-dimensional distribution diagram showing the survival state of bacteria, which is output from the sterilization performance prediction system according to embodiment 1.

Fig. 20 is a block diagram showing a functional configuration of the sterilization performance prediction system according to embodiment 2.

Fig. 21 is a diagram showing an example of a scenario database stored in the sterilization performance prediction system according to embodiment 2.

Detailed Description

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. The embodiments described below are all specific examples of the present invention. Accordingly, the numerical values, shapes, materials, constituent elements, arrangement and connection modes of the constituent elements, steps, order of the steps, and the like shown in the following embodiments are examples, and the present invention is not limited thereto. Therefore, among the components in the following embodiments, components that are not recited in the independent claims representing the most generic concept of the present invention are described as arbitrary components.

The drawings are schematic and not necessarily strictly illustrated. Therefore, for example, the scales and the like are not always the same in each drawing. In the drawings, substantially the same components are denoted by the same reference numerals, and redundant description is omitted or simplified.

(embodiment mode 1)

[ summary ]

First, an outline of a sterilization performance prediction system according to an embodiment will be described with reference to fig. 1 to 3.

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 showing the space 10 as an estimation target of the sterilization performance prediction system 100 according to the present embodiment and the state of the medicine 20 generated in the space 10. Fig. 3 is a diagram schematically showing bacteria 30 present on a surface 40 of a member in the space 10 to be estimated by the sterilization performance prediction system 100 according to the present embodiment and the medicine 20 generated in the space 10. Fig. 3 corresponds to an enlarged cross-sectional view of a part of fig. 2.

The space 10 is a space to be predicted by the sterilization performance prediction system 100 to predict the sterilization performance. The space 10 is, for example, 1 room enclosed by walls, windows, doors, etc. As shown in fig. 2, a generation source 11 of a medicine 20 is provided in the space 10. In addition, furniture such as a chair 12 and a rack 13 are disposed in the space 10. The arrangement, number, and the like of the generation source 11, the chair 12, the rack 13, and the like are merely examples.

The drug 20 has a sterilization effect of removing the bacteria 30. In the present specification, sterilization means not only sterilization or bacteria removal but also virus removal. That is, the bacteria 30 include not only bacteria and bacteria but also viruses and the like. Examples of the bacteria 30 include, but are not limited to, staphylococcus aureus, pseudomonas aeruginosa, and escherichia coli.

For example, agent 20 is hypochlorous acid (HClO). Alternatively, the agent 20 may be ozone (O)₃) Or water (H) subjected to plasma treatment₂O), and the like. The chemical 20 is a liquid such as atomized hypochlorous acid water, but may be a gas or a solid in the form of fine particles.

The medicament 20 is released from the production source 11 to the space 10. The generation source 11 is a generation device that generates and releases the medical agent 20. For example, the generator 11 is a hypochlorous acid water generator, and generates hypochlorous acid water by electrolyzing a saline solution. The generation source 11 has a function of forming a gas flow toward the space 10. The generation source 11 vaporizes the generated hypochlorous acid water, and releases hypochlorous acid as the chemical 20 together with the gas flow. The medicament 20 diffuses along the airflow within the space 10.

When the chemical 20 diffused in the space 10 comes into contact with the bacteria 30 existing in the space 10, the bacteria 30 are decomposed and sterilized. The agent 20 is diffused in the space 10 at an appropriate concentration, thereby efficiently sterilizing the space.

The drug 20 present in the space 10 is affected by various factors depending on at least one of the characteristics of the drug 20 and the environment in the space 10. For example, the medicament 20a shown in fig. 2 is ineffective due to self-decomposition. The medicine 20b floats and spreads in the space 10. The medicine 20c is adsorbed on the wall of the space 10. In addition, the adsorbed medicine 20c is released from the wall. The medicine 20d is decomposed by ultraviolet rays and becomes ineffective.

Similarly, the bacteria 30 present in the space 10 are affected by at least one of the characteristics of the bacteria 30 and the environment in the space 10. For example, the bacteria 30 may be decomposed by ultraviolet rays and become ineffective, as in the case of the drug 20 c. Details will be described later.

The sterilization performance prediction system 100 according to the present embodiment is implemented by 1 or more information processing apparatuses. The 1 or more information processing apparatuses are each realized by a nonvolatile memory in which a program is stored, a volatile memory as a temporary storage area for executing the program, an input/output port, a processor for executing the program, and the like.

[ Structure ]

Next, referring to fig. 2 and 3 as appropriate, the detailed configuration of the sterilization performance prediction system 100 will be described with reference to fig. 1.

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 sterilization performance prediction system 100 acquires environmental information, drug information, and bacteria information, and performs simulation based on the acquired information to output prediction information indicating the survival state of the bacteria 30 at a predetermined position in the space 10. The predetermined position is a predicted target position.

The target position is 1 local space selected as a position to be an estimation target of density from among a plurality of local spaces obtained by dividing the space 10 into a three-dimensional matrix. The object position is represented, for example, by a three-dimensional coordinate system. The object position is not limited to 1 local space, and may be a plurality of local spaces, or may be all the local spaces, that is, the entire space 10. In this case, the sterilization performance prediction system 100 may output the three-dimensional distribution of the living states of the bacteria 30 in the space 10 as the prediction information.

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

The environment information is information indicating the environment in the space 10. Specifically, the environmental information includes first environmental information that affects the state of bacteria 30 to be sterilized and second environmental information that affects the chemical 20 dispersed in 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 environment information is information indicating at least one of the temperature, the humidity, and the amount of ultraviolet rays at the target position, and the surface temperature and the amount of organic matter adhering to the surface 40 when the target position includes the surface 40 of the member existing in the space 10. The member is a building material such as a wall material, ceiling material, floor material, or window material forming the space 10, or an obstacle such as a chair 12, a rack 13, or a generation source 11 disposed in the space 10. The details of each of the first environment information and the second environment information will be described later.

The medicine information is information related to the medicine 20 generated in the space 10. Specifically, the medicine information is information indicating the amount of medicine 20 generated, the wind direction and the wind speed of the 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 information on bacteria 30 that may exist in the space 10 and are the objects of sterilization. Specifically, the bacteria information is information indicating the type of bacteria, or viruses.

The acquisition unit 110 acquires environmental information, drug information, and bacteria information. The acquisition unit 110 is implemented by, for example, an input interface for inputting an output signal output from a sensor such as a temperature/humidity sensor, a user interface for accepting an operation from a user, a communication interface for communicating with the generation source 11, and the like. The user interface is, for example, a touch panel or a physical operation button. Alternatively, the acquisition unit 110 may be implemented by a communication interface for communicating with a terminal device such as a smartphone operated by a user.

In the present embodiment, the acquisition unit 110 acquires temperature information indicating the temperature in the space 10 from a temperature/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 a temperature/humidity sensor or a humidity sensor. The acquisition unit 110 acquires ultraviolet information indicating the amount of ultraviolet light in the space 10 from the ultraviolet light meter.

Further, a plurality of temperature/humidity sensors and/or ultraviolet light meters may be provided in the space 10. The acquisition unit 110 may acquire temperature information, humidity information, and ultraviolet information in each of a plurality of partial spaces constituting the space 10 from a plurality of temperature/humidity sensors and ultraviolet light meters.

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

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

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

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

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

The control unit 120 executes the main functions of the sterilization performance prediction system 100. The control unit 120 is implemented by a nonvolatile memory in which a program is stored, a volatile memory as a temporary storage area for executing the program, an input/output port, a processor for executing the program, and the like. In the present embodiment, as shown in fig. 1, the control unit 120 includes a density estimation unit 121 and a prediction unit 122.

The concentration estimating unit 121 calculates the concentration of the medicine at a predetermined position in the space 10 in which the medicine 20 is dispersed. Specifically, the concentration estimating unit 121 outputs concentration information indicating the concentration of the drug 20 at the target position in the space 10 by performing a simulation of the concentration estimation of the drug 20. In the present embodiment, the concentration estimating unit 121 acquires at least one of the amount of generated medicine 20, the wind direction and the wind speed of the airflow for moving the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, the adsorption/release coefficient of the medicine 20 with respect to a predetermined surface in the space 10, the temperature, humidity, and ultraviolet ray amount in the space 10 as input data, and calculates the medicine concentration using the acquired input data. The detailed configuration of the concentration estimating section 121 will be described later.

The prediction unit 122 predicts the survival state of the bacteria 30 at the target position by referring to the CT value database 131 stored in the storage unit 130 based on the calculated drug concentration, the type of the bacteria 30 to be sterilized, and the environmental information. In the present embodiment, the prediction unit 122 also predicts the survival state of the bacteria 30 by referring to the CT value database 131 based on the drug information indicating the type of the drug 20 dispersed in the space 10.

The survival state of the bacteria 30 is represented by, for example, the amount of decrease in the bacteria 30 at the target position after a predetermined period of time has elapsed from the release of the drug 20. The amount of bacteria 30 decreased is expressed by the number of steps (Japanese: number of trusses). The number of bacteria removed is 1 (also referred to as "1-order bacteria removal"), which means that the number of bacteria is reduced by 1 order, that is, the number of bacteria after the bacteria removal is 0.1 when the initial state is 1. That is, a level 1 sterilization refers to 90% of degerming 30, i.e., 90% sterilization. Similarly, a 2-order sterilization refers to 99% sterilization. Level 3 degerming means 99.9% degerming.

In the present embodiment, the predicting part 122 calculates the reduction amount Y of the bacteria 30 based on the following formula (1).

[ numerical formula 1]

In equation (1), the estimated CT value is a CT value calculated based on the drug concentration estimated by the concentration estimating unit 121. An estimated CT value is calculated for each object position. The estimated CT value represents the sterilization performance of the pharmaceutical agent 20 at the subject location.

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

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

The prediction unit 122 may calculate the bacteria removal rate of the bacteria 30 based on the following formula (2).

[ numerical formula 2]

The bacteria removal rate [% ]]＝100×(1-10^-Y)···(2)

The storage unit 130 stores a CT value database 131. The storage unit 130 is 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 that relates the type of bacteria to a sterilization index value that is the product of the drug concentration (C) and the time (T) required for sterilization. The sterilization index value is a so-called CT value. In the correspondence information, the bacteria type and the bacteria elimination index value are associated with 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, the CT value is associated with the combination of the type of bacteria 30 and the type of medicine 20.

The CT value is, for example, the product of the drug concentration and time required to remove 99% of the corresponding bacteria 30 using the corresponding drug 20. That is, the CT value is the product of the concentration of the drug and the time required for 2-order sterilization of the bacteria 30. The CT value may be a product of a drug concentration and time required for 1-order sterilization (i.e., 90% sterilization) or a product of a drug concentration and time required for 3-order sterilization (i.e., 99.9% sterilization) of the bacteria 30.

The larger the CT value stored in the CT value database 131, the more concentrated the drug 20 is required to remove the corresponding bacteria 30, and/or the more time the bacteria 30 are exposed to the drug 20 is required. CT values are obtained, for example, by performing a sterilization test under baseline conditions. The reference conditions are, for example, a temperature of 20 ℃, a humidity of 50% RH, and an ultraviolet dose of 0mW/cm²But is not limited thereto.

The correspondence form of the information in the CT value database 131 is not limited to the example shown in fig. 4. For example, the CT value database 131 may indicate the number of levels of bacteria that can be removed when the sterilization process is performed with a predetermined CT value, depending on the combination of the type of the drug 20 and the type of the bacteria 30.

For example, when the chemical 20 is ozone, staphylococcus aureus can be sterilized at 1.7 steps by being subjected to sterilization treatment with a CT value of 10. Pseudomonas aeruginosa can be sterilized at a level of 0.57 by performing sterilization treatment with a CT value of 10. Coli can be sterilized at 1.44 level by being subjected to a sterilization treatment with a CT value of 25.

For example, when the chemical 20 is hydrogen peroxide, staphylococcus aureus can be sterilized at 2 steps by being subjected to sterilization treatment with a CT value of 3350. When the chemical 20 is hydrogen peroxide, staphylococcus aureus can be sterilized at 6 steps by being subjected to sterilization treatment with a CT value of 258000.

The output unit 140 outputs the 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 representing 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.

[ concentration estimating section ]

Next, the detailed configuration of the concentration estimating unit 121 according to the present embodiment will be described with reference to fig. 5. Fig. 5 is a block diagram showing a functional configuration of the density estimating unit 121 of the sterilization performance predicting system 100 according to the present embodiment.

As shown in fig. 5, the density estimating unit 121 includes a determining unit 210, a calculating unit 220, and a storage unit 230. The concentration estimating unit 121 acquires the environmental information and the medicine information, and outputs concentration information indicating the concentration of the medicine 20 at a predetermined position in the space 10 by performing simulation based on the acquired information. The prescribed position is a target position for concentration estimation.

The determination unit 210 determines the self-decomposition coefficient of the drug 20, the diffusion coefficient of the drug 20, and the adsorption/release coefficient of the drug 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, diffusion coefficient, and adsorption/desorption 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/desorption coefficients. The storage unit 230 is a nonvolatile memory such as an HDD or a semiconductor memory. The storage unit 230 may be implemented using the same hardware resources as the storage unit 130 shown in fig. 1.

The calculation unit 220 calculates the concentration of the drug 20 in the space 10 by simulating the drug 20 diffusing in the space 10. The arithmetic unit 220 is implemented by a nonvolatile memory in which a program is stored, a volatile memory as a temporary storage area for executing the program, an input/output port, a processor for executing the program, and the like.

Specifically, the calculation unit 220 acquires, as input data, at least one of the amount of generated medicine 20, the wind direction and the wind speed of the airflow for moving the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, the adsorption/release coefficient of the medicine 20 with respect to a predetermined surface in the space 10, and the temperature, humidity, and ultraviolet ray amount in the space 10. The arithmetic unit 220 acquires input data, and calculates the concentration of the medicine 20 in the space 10 using the acquired input data.

The computing unit 220 performs analysis (hereinafter, referred to as CFD analysis) based on, for example, Computational Fluid Dynamics (CFD). The CFD analysis is performed based on a model such as RANS (Reynolds-Averaged Navier-stokes equation), DNS (Direct Numerical Simulation), LES (Large Eddy Simulation), or DES (separated Eddy Simulation). Specifically, the calculation unit 220 calculates the drug concentration in each local space constituting the space 10 by performing CFD analysis using the type of the drug 20, the amount of the drug 20 generated, the wind direction and the wind speed of the airflow, the self-decomposition coefficient of the drug 20, the diffusion coefficient of the drug 20, the adsorption/release coefficient of the drug 20, the temperature, humidity, and ultraviolet ray amount in the space 10 as input data. Specifically, the calculation unit 220 generates a three-dimensional distribution of the drug concentration for each local space. More specifically, the calculation unit 220 calculates the concentration of the drug 20 at an arbitrary position in the space 10 at an arbitrary time. The calculation unit 220 generates a three-dimensional distribution of the temporal change in the drug concentration in the space 10.

For example, the greater the amount of agent 20 produced, the greater the estimated concentration value for each local space. The smaller the amount of the agent 20 produced, the smaller the estimated value of the concentration in each local space. In addition, based on the wind direction of the airflow, for example, the estimated concentration value of the local space located downwind of the generation source 11 (specifically, a position far from the generation source 11) is smaller than the estimated concentration value of the local space located upwind of the generation source 11 (specifically, a position near the generation source 11). In addition, in the case where the wind speed is high, the estimated value of the concentration in the local space away from the generation source 11 becomes large. In the case where the wind speed is small, the estimated value of the concentration in the local space distant from the generation source 11 becomes small. The relationship between the self-decomposition coefficient, diffusion coefficient, and adsorption/release coefficient and the estimated concentration 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 ]

Next, details of the estimation simulation for the drug concentration for calculating the estimated CT value in the above equation (1) will be described. First, 3 parameters related to the properties of the drug 20, which are acquired as input data by the arithmetic unit 220 of the concentration estimation unit 121 according to the present embodiment, will be described. The 3 parameters are the self-decomposition coefficient, diffusion coefficient and adsorption-release coefficient of the pharmaceutical agent 20.

[ self-decomposition coefficient ]

First, the self-decomposition coefficient of the drug 20 will be described.

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

[ numerical formula 3]

In the formula (3), k is the self-decomposition coefficient (unit [/s ] of the drug 20]). A is the frequency factor. E_aIs the activation energy. R is a gas constant. T is the absolute temperature within the space 10. Here, A and E_aIs a value determined according to the type of the drug 20.

As shown in equation (3), the self-decomposition coefficient k depends on the temperature in the space 10. Specifically, the lower the temperature in the space 10, the smaller the self-decomposition coefficient k, and the more difficult the self-decomposition of the drug 20 progresses. The higher the temperature in the space 10, the larger the self-decomposition coefficient k, and the more easily the self-decomposition of the drug 20 progresses.

In addition, the frequency factor A and the activation energy E for calculating the self-decomposition coefficient k_aEtc. can be obtained by performing actual measurement in advance according to the following procedure. First, for example, a volume of 1m is prepared in which the temperature is maintained at 20 ℃ and the humidity is maintained at 50%³The hypochlorous acid gas is accumulated in the acrylic case as the chemical 20. The hypochlorous acid gas concentration in the acryl box was measured over time from the initial state. The same measurement was performed under different conditions by changing the combination of the air temperature and the humidity.

Reaction rate formula [ HClO ] based on hypochlorous acid]＝[HClO]_initial×e^-ktThe self-decomposition coefficient k is calculated. Further, [ HClO ]]_initialThe concentration of hypochlorous acid gas in the initial state is shown. [ HClO]The hypochlorous acid gas concentration after the lapse of time t from the initial state is shown.

Self-decomposition coefficients k corresponding to 2 or more different temperatures are calculated, and Arrhenius drawing is performed using the calculated self-decomposition coefficients k. The Arrhenius plot refers to a plot of the calculated values in a two-dimensional coordinate system with 1/T on the horizontal axis and lnk on the vertical axis. Calculating slope-E based on an approximation of an Arrhenius plot_a/R and intercept lnA.

In the present embodiment, the determination unit 210 determines the self-decomposition coefficient k based on the temperature in the 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 showing the correspondence relationship between the self-decomposition coefficient k and the temperature. Specifically, the first database 231 shows 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. In addition, when the types of the medicines 20 are only 1, the first database 231 may show the self-decomposition coefficient k calculated in advance for each temperature.

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

In the present embodiment, the reduction in concentration due to the self-decomposition of the drug 20 can be reflected in the simulation result by using the self-decomposition coefficient as the input data of the simulation. Specifically, the larger the self-decomposition coefficient is, the smaller the concentration estimated value is with respect to the concentration estimated value in the case where the self-decomposition coefficient is not used as input data. The smaller the self-decomposition coefficient is, the larger the concentration estimated value is with respect to the concentration estimated value in the case where the self-decomposition coefficient is not used as input data. In either case, the concentration 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 drug 20 will be described.

The diffusion coefficient is a parameter indicating the degree of diffusion of the drug 20. Specifically, the diffusion coefficient is defined by the equation of einstein-stokes shown in the following equation (4).

[ numerical formula 4]

In the formula (4), D is the diffusion coefficient (unit [ m/s ]) of the drug 20. k is the boltzmann constant. T is the absolute temperature within the space 10. B is the mobility of the drug 20. μ is the viscosity of the medicament 20. a is the molecular radius of the agent 20. Here, μ and a are values determined according to the kind of the drug 20.

As shown in equation (4), the diffusion coefficient D depends on the temperature in the space 10. Specifically, the lower the temperature in the space 10, the smaller the diffusion coefficient, and the more difficult the drug 20 is to diffuse. The higher the temperature in the space 10, the higher the diffusion coefficient, and the easier the diffusion of the drug 20. More specifically, the diffusion coefficient D has a proportional relationship with the absolute temperature T in the space 10.

In the present embodiment, the determination unit 210 determines the diffusion coefficient D based on the temperature in the 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 showing the correspondence relationship between the diffusion coefficient D and the temperature. Specifically, the second database 232 shows 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 the types of the medicines 20 are only 1, the second database 232 may show the diffusion coefficient D calculated in advance for each temperature.

In addition, the storage unit 230 may store the above equation (4) in place of the second database 232. The determination unit 210 may calculate the diffusion coefficient D using equation (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, the change in concentration due to the diffusion of the drug 20 can be reflected in the simulation result. Specifically, the greater the diffusion coefficient, the more easily the drug 20 spreads. Thus, for example, the greater the concentration estimate of the pharmaceutical agent 20 at a location remote from the generation source 11 compared to a concentration estimate without using the diffusion coefficient as input data. In addition, the smaller the diffusion coefficient, the more difficult the drug 20 is to spread. Thus, for example, the concentration estimated value of the medical agent 20 at a position distant from the generation source 11 is smaller than that in the case where the diffusion coefficient is not used as input data. In either case, by using the diffusion coefficient as input data, the concentration close to the actually measured value can be estimated.

[ adsorption Release coefficient ]

Next, the adsorption/release coefficient of the drug 20 will be described.

Fig. 6 is a sectional view for explaining the adsorption/release coefficient of the drug 20 with respect to a predetermined surface 40. The predetermined surface 40 is a building material such as a wall 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, after a predetermined time has elapsed, the drug 20 located in the vicinity of the surface 40 may be adsorbed to the surface 40 as in the case of the drug 20x, or may remain in the vicinity of the surface 40 without being adsorbed to the surface 40 as in the case of the drug 20 y.

The adsorption/release coefficient is a parameter indicating the degree of adsorption and release of the drug 20 to the predetermined surface 40. The adsorption release coefficient is determined as the distance of the agent 20 approaching the surface 40 per unit time. Specifically, the adsorption-release coefficient is defined based on an expression representing the adsorption amount shown in the following expression (5).

[ numerical formula 5]

F＝h_pΔCA···(5)

In formula (5), h_pIs the adsorption-release coefficient (unit [ m/s ]) of the drug 20]). Coefficient of adsorption and releaseA positive value means that the agent 20 is adsorbed on the surface 40. In the case where the adsorption release coefficient is a negative value, it means that the medicinal agent 20 is released from the surface 40. The larger the absolute value of the adsorption-release coefficient is, the larger the adsorption amount or release amount is. The smaller the absolute value of the adsorption-release coefficient, the smaller the adsorption amount or release amount.

F is the amount of adsorption of the drug 20 (unit [ m ]³/s]). Δ C is the concentration difference ratio of the drug 20. A is the area of the surface 40 shown in FIG. 5 (unit [ m ]²])。

The adsorption of the pharmaceutical agent 20 includes physical adsorption and chemical adsorption. Examples of physical adsorption are: the medicament 20 is captured by the minute pores or irregularities provided on the surface 40. The physical adsorption is determined by, for example, an adsorption isotherm formula based on BET represented by the following formula (6).

[ numerical formula 6]

In formula (6), p₀Is the saturated vapor pressure of the agent 20. p is the vapor pressure of the agent 20. v. of₁Is the saturated adsorption capacity. v is the amount of adsorption. c is represented by the following formula (7).

[ number formula 7]

In formula (7), c₀Is a constant. R is a gas constant. T is the absolute temperature. E₁Is the heat of adsorption of the medicament 20 to the surface 40. E is the heat of condensation of the medicament 20. Here, E₁And E is a value determined according to the kind of the drug 20.

Further, the chemical adsorption is, for example: the chemical 20 is captured by chemical coupling such as ion coupling with minute moisture or the like adhering to the surface 40. The amount of adsorption in the chemical adsorption depends on the surface temperature of the surface 40 or the atmospheric temperature in the vicinity of the surface 40.

In the present embodiment, the adsorption/desorption coefficient h is_pTo be used as a materialAdsorption factors such as physical adsorption and chemical adsorption are comprehensively determined as macroscopic coefficients. Specifically, the adsorption/release coefficient h is determined by converting the movement distance of the drug 20 based on the concentration difference between the ends of the surface 40 having the area a shown in fig. 6_p. Specifically, the adsorption/release coefficient h is determined based on the measured value of the concentration by using the formula (5)_p。

FIG. 7 is a graph for explaining the adsorption/release coefficient h of the drug 20 to a predetermined surface 40_pA cross-sectional view of the concentration difference ratio Δ C used. As shown in fig. 7, the space in the vicinity of the surface 40 is virtually defined as a first region and a second region.

The first region is the region that confronts the surface 40. The second region is a region that is further from the surface 40 than the first region, sharing a face with the first region. The first region and the second region are, for example, rectangular parallelepiped spaces having the same size. The width w of the first region is less than the adsorption and release coefficient h_pThe analysis time is, for example, 1mm or less.

At this time, the concentration difference ratio Δ C is expressed by the following formula (8).

[ number formula 8]

In formula (8), C_1tAnd C_2tThe concentrations of the pharmaceutical agent 20 in the first and second regions, respectively, at time t. From the expressions (5) and (8), the concentration C of the second region_2tThe larger the adsorption amount, the more the adsorption amount is on the surface 40.

FIG. 8 is a graph showing the adsorption release coefficient h_pGraph of temperature dependence of (a). In FIG. 8, the horizontal axis represents temperature, and the vertical axis represents adsorption/release coefficient h_p. As shown in FIG. 8, the higher the temperature, the adsorption-release coefficient h_pThe smaller the size, the more difficult adsorption of the medicament 20 to the surface 40 occurs. The lower the temperature, the adsorption-release coefficient h_pThe larger the more likely adsorption of the medicament 20 to the surface 40 occurs. The adsorption release coefficient has a substantially linear relationship with temperature.

In the present embodiment, the determination unit 210 determines the adsorption/release coefficient h based on at least one of the material, shape, temperature, and humidity of the surface 40_p. Specifically, the determination unit 210 determines the adsorption/release coefficient h by referring to the third database 233 stored in the storage unit 230_p。

The third database 233 shows the adsorption release coefficient h_pA database of temperature dependence. Specifically, the third database 233 shows the adsorption/release coefficient h predetermined 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 drug 20_p. In the case where the types of the medicines 20 are only 1, the third database 233 may show the predetermined adsorption/release coefficient h for each combination of the material, shape, temperature, and humidity of the surface 40_p。

In addition, in order to save memory resources, the adsorption release coefficient h corresponding to all combinations may not be shown in the third database 233_pBut shows the adsorption-release coefficient h corresponding to a part of the combinations_p。

For example, in the third database 233, the following may also be shown: when the temperature was 20 ℃ and the humidity was 50% RH, the adsorption/desorption coefficients of the materials A, B and C were 0.0006m/s, 0.0011m/s and 0.02m/s, respectively. The determination unit 210 may calculate the adsorption/release coefficient corresponding to the input data by correcting the values read from the third database 233 based on a function indicating the correspondence relationship between the shape, temperature, and humidity of the surface 40 and the adsorption/release coefficient. Alternatively, the determination unit 210 may calculate the adsorption/release coefficient corresponding to the input data by interpolation processing using the values stored in the third database 233.

For example, as shown in fig. 8, the adsorption-release coefficient has a substantially linear relationship with temperature. Specifically, the higher the temperature, the higher the adsorption-release coefficient h_pThe smaller the size, the more difficult adsorption of the medicament 20 to the surface 40 occurs. The lower the temperature, the adsorption-release coefficient h_pThe larger the size of the tube is,the more likely adsorption of the medicament 20 to the surface 40 occurs.

Further, since the higher the humidity is, the more the surface moisture content of the surface 40 is, the more easily the drug 20 is captured, and therefore, the adsorption/release coefficient h is macroscopically expressed_pThe larger. That is, the higher the humidity, the more likely adsorption of the pharmaceutical agent 20 to the surface 40 occurs, or the more difficult release of the pharmaceutical agent 20 occurs. The lower the humidity is, the less the surface moisture amount of the surface 40 is, and the more difficult the drug 20 is captured, so that the adsorption/release coefficient h macroscopically_pThe smaller. That is, the lower the humidity, the more difficult adsorption of the pharmaceutical agent 20 to the surface 40 occurs or the more easily release of the pharmaceutical agent 20 occurs.

In addition, the rougher the surface of the surface 40, specifically, the larger the surface roughness of the surface 40, the adsorption/release coefficient h_pThe larger the more likely adsorption of the medicament 20 to the surface 40 occurs. The smaller the surface roughness of the surface 40, the adsorption-release coefficient h_pThe smaller the size, the more difficult adsorption of the medicament 20 to the surface 40 occurs.

In the present embodiment, by using the adsorption/release coefficient as input data for the simulation, the decrease in concentration due to adsorption of the drug 20 to the surface 40 can be reflected in the simulation result. Specifically, the larger the adsorption-release coefficient is, the smaller the concentration estimated value is compared with the concentration estimated value in the case where the adsorption-release coefficient is not used as input data. The smaller the adsorption-release coefficient, the larger the concentration estimation value is compared to the concentration estimation value in the case where the adsorption-release coefficient is not used as input data. In either case, by using the adsorption/desorption coefficient as input data, the concentration close to the actually measured value can be estimated.

[ influence of the Environment on the production of drugs and bacteria ]

Next, the influence of the environment on the drug 20 and the bacteria 30 will be described. In the present embodiment, the environment affecting the chemical 20 and the bacteria 30 includes air temperature, humidity, and ultraviolet ray amount, surface temperature, organic matter amount, and the like.

[ temperature and surface temperature ]

First, the influence of the temperature and the surface temperature on the drug 20 and the bacteria 30 will be described with reference to fig. 9.

FIG. 9 is a graph showing the temperature dependence of the survival state of bacteria 30. In fig. 9, the horizontal axis represents temperature. The temperature is the temperature of the air to which the bacteria 30 are exposed and the surface temperature of the surface 40 of the member to which the bacteria 30 are exposed. In fig. 9, the vertical axis represents the degree of survival of the bacteria 30, that is, the degree of immobility of the bacteria 30. The larger the value of the vertical axis, the easier the bacterium 30 to survive.

As shown in fig. 9, the lower the temperature, the more easily the bacteria 30 survive, and the higher the temperature, the more difficult the bacteria 30 survive. In other words, the lower the temperature, the less easily the bacteria 30 are removed, and the higher the temperature, the more easily the bacteria 30 are removed. This is considered to be because the higher the temperature is, the more thermal energy is supplied to both the drug 20 and the bacteria 30, and the higher the reactivity between the drug 20 and the bacteria 30 is, the higher the sterilization performance is.

From the above, the environmental factor f in the above equation (1) is set to a smaller value as the temperature is higher, and the environmental factor f in the above equation (1) is set to a larger value as the temperature is lower. Thus, the reduction amount Y of the bacteria 30 calculated by the formula (1) is a value obtained in consideration of the influence of the temperature. Therefore, according to the sterilization performance prediction system 100 of the present embodiment, the accuracy of predicting the living state of the bacteria 30 can be improved.

[ humidity ]

Next, the influence of humidity on the drug 20 and the bacteria 30 will be described with reference to fig. 10.

Fig. 10 is a schematic diagram showing a state where the bacteria 30 are covered with the water droplets 50. When the humidity of the space 10 is high, the surface moisture amount becomes large. Therefore, as shown in fig. 10, water droplets 50 are likely to adhere to the surface 40, and the bacteria 30 are likely to be covered with the water droplets 50.

As the influence of humidity on the bacteria 30, there is an influence that the bacteria 30 are swollen by the water droplets 50. At this time, the permeability of the cell membrane of the bacteria 30 increases, and the drug 20 is easily permeated by the osmotic pressure. Therefore, it is considered that the reactivity between the drug 20 and the bacteria 30 is increased and the bacteria-removing performance is improved.

Further, there is an influence of humidity on the medicine 20, in which 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 droplets 50 increases, and the drug concentration in the vicinity of the bacteria 30 increases. Therefore, it is considered that the time for which the drug 20 is in contact with the bacteria 30 is long, and the bacteria-removing performance is improved.

Fig. 11 is a graph showing the humidity dependence of the survival state of the bacteria 30. In fig. 11, the horizontal axis represents humidity. The vertical axis represents the degree of survival of the bacteria 30, that is, the degree of death of the bacteria 30. The larger the value of the vertical axis, the easier the bacterium 30 to survive.

As shown in fig. 11, the lower the humidity, the more easily the bacteria 30 live, and the higher the humidity, the more difficult the bacteria 30 live. In other words, the lower the humidity, the more difficult the bacteria 30 are to be removed, and the higher the humidity, the more easily the bacteria 30 are to be removed.

From the above, the higher the humidity is, the smaller the value of the environmental factor f in the above equation (1) is set, and the lower the humidity is, the larger the value of the environmental factor f in the above equation (1) is set. Thus, the reduction amount Y of the bacteria 30 calculated by the formula (1) is a value obtained in consideration of the influence of humidity. Therefore, according to the sterilization performance prediction system 100 of the present embodiment, the accuracy of predicting the living state of the bacteria 30 can be improved.

[ amount of ultraviolet rays ]

Next, the influence of the ultraviolet ray amount on the drug 20 and the bacteria 30 will be described with reference to fig. 12.

First, as the influence of the ultraviolet rays 60 on the medicine 20, there is an influence that the ultraviolet rays 60 are absorbed by the medicine 20 to decompose the medicine 20. Fig. 12 is a schematic diagram showing a state in which ultraviolet rays 60 are irradiated to the drug 20 and the bacteria 30. Fig. 12 shows a case where the medicine 20 irradiated with the ultraviolet rays 60 is decomposed and fails.

Fig. 13 is a graph showing the wavelength dependence of hypochlorous acid in absorbing ultraviolet rays. In fig. 13, the horizontal axis represents the wavelength of ultraviolet ray 60, and the vertical axis represents the absorbance of hypochlorous acid to ultraviolet ray.

As shown in fig. 13, hypochlorous acid has an absorption peak at a wavelength of about 290 nm. Hypochlorous acid absorbs ultraviolet rays and is decomposed when irradiated with ultraviolet rays. In addition, fig. 13 shows a graph of absorbance at each concentration of hypochlorous acid, and the following tendency can be observed: hypochlorous acid absorbs ultraviolet light at any concentration.

The more the amount of ultraviolet rays is, the more easily the drug 20 is decomposed and the lower the drug concentration is. The smaller the amount of ultraviolet rays, the more difficult the decomposition of the drug 20 and the more difficult the decrease in the drug concentration. Therefore, in the case where the influence on the medicine 20 is taken into consideration, the environmental factor f in the formula (1) is set to a larger value as the amount of ultraviolet rays is larger, and the environmental factor f in the formula (1) is set to a smaller value as the amount of ultraviolet rays is smaller. Further, as shown in fig. 13, since the absorbance has wavelength dependency, the environmental factor f may be set to a value corresponding to the wavelength of the ultraviolet ray 60, for example.

Further, as the influence of the ultraviolet rays 60 on the bacteria 30, there is an influence that the ultraviolet rays 60 are absorbed by DNA and protein of the constituent bacteria 30 to weaken them. Fig. 14 and 15 are graphs showing the wavelength dependence of ultraviolet absorption by DNA and protein, respectively. In each of fig. 14 and 15, the horizontal axis represents the wavelength of ultraviolet light and visible light, and the vertical axis represents the absorbance of DNA or protein absorbing ultraviolet light.

As shown in fig. 14, the DNA has an absorption peak at a wavelength of about 260 nm. When ultraviolet light is irradiated to DNA, the DNA absorbs the ultraviolet light, and is damaged or weakened. As shown in fig. 15, the protein has an absorption peak at a wavelength of about 280 nm. When ultraviolet light is irradiated to a protein, the protein absorbs the ultraviolet light, and is damaged or weakened.

The more the amount of ultraviolet rays, the more easily the bacterium 30 is damaged or weakened and the more easily it dies. The smaller the amount of ultraviolet rays, the less likely the bacterium 30 is damaged or weakened and the more difficult it is to die. Therefore, in the case where the influence on the bacteria 30 is taken into consideration, the environmental factor f in the formula (1) is set to a smaller value as the amount of ultraviolet rays is larger, and the environmental factor f in the formula (1) is set to a larger value as the amount of ultraviolet rays is smaller. Further, as shown in fig. 14 and 15, since the absorbance has wavelength dependency, the environmental factor f may be set to a value corresponding to the wavelength of the ultraviolet ray 60, for example.

Further, the relationship between the ultraviolet ray amount and the environmental factor f differs depending on the influence on the drug 20 and the influence on the bacteria 30. Therefore, the environmental factor f is set according to the magnitude relation of the influence of the ultraviolet ray 60 on the drug 20 and the bacteria 30, respectively. For example, in the case where the decomposition of the drug 20 is more severe than the weakening of the bacteria 30 by the ultraviolet rays 60, the bacteria 30 have a low sterilization performance as a result, and therefore the environmental factor f is set to a large value. In addition, in the case where the bacteria 30 are weakened more sharply than the degradation of the medicine 20 by the ultraviolet rays 60, the bacteria 30 have high bacteria removal performance as a result, and therefore the environmental factor f is set to a small value. As described above, according to the sterilization performance prediction system 100 of the present embodiment, the accuracy of predicting the living state of the bacteria 30 can be improved.

[ amount of organic matter ]

Next, the influence of the amount of organic matter on the medicine 20 will be described with reference to fig. 16.

Fig. 16 is a schematic diagram showing a state in which the bacteria 30 are covered with the organic material 70. As shown in fig. 16, when the bacteria 30 are covered with the organic material 70, the chemical 20 hardly reaches the bacteria 30. Specifically, when the chemicals 20 permeate the organic material 70, the chemicals react with the organic material 70, and the amount of the chemicals 20 reaching the bacteria 30 decreases. Therefore, it is considered that the larger the amount of the organic substance 70, the less the contact of the drug 20 with the bacteria 30, and the lower the sterilization performance.

According to the above, the environmental factor f in the above equation (1) is set to a larger value as the organic matter amount is larger, and the environmental factor f in the above equation (1) is set to a smaller value as the organic matter amount is smaller. Thus, the reduction amount Y of the bacteria 30 calculated by the formula (1) is a value obtained in consideration of the influence of organic substances. Therefore, according to the sterilization performance prediction system 100 of the present embodiment, the accuracy of predicting the living state of the bacteria 30 can be improved.

[ actions ]

Next, the operation of the sterilization performance prediction system 100 according to the present embodiment will be described with reference to fig. 17 and 18.

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

As shown in fig. 17, the acquisition unit 110 first acquires environmental information, drug information, and bacteria information (S10). Specifically, the acquisition unit 110 acquires temperature information, humidity information, and ultraviolet information as environmental information from a temperature/humidity sensor and an ultraviolet light meter provided in the space 10. The acquisition unit 110 also acquires information on the type of the medicine 20 and the airflow information from the generation source 11 as medicine information. The acquisition unit 110 also acquires information indicating the type of the bacteria 30 as bacteria information from a terminal device or the like. The acquisition unit 110 acquires geometric information and material information of the space 10.

Next, the concentration estimating unit 121 calculates the drug concentration by performing a simulation based on the information acquired by the acquiring unit 110 (S20). Specifically, the density estimating unit 121 performs the density estimation process shown in fig. 18.

As shown in fig. 18, first, the density estimation unit 121 acquires input data of a density estimation simulation (S22). Specifically, the determination unit 210 determines the self-decomposition coefficient k, the diffusion coefficient D, and the adsorption/release coefficient h based on the environmental information, the drug information, and the like_p。

Next, the calculation unit 220 calculates the concentration of the medicine 20 at the target position in the space 10 (S24). Specifically, the calculation unit 220 acquires the acquired temperature information, humidity information, ultraviolet information, geometric information, and material information, and the determined self-decomposition coefficient k, diffusion coefficient D, and adsorption/desorption coefficient h_pAs input data. The arithmetic unit 220 calculates the drug concentration for each local space by performing CFD analysis based on the acquired input data.

Returning to fig. 17, the prediction unit 122 predicts the survival state of the bacteria 30 by referring to the CT value database 131 based on the calculated drug concentration, the type of the bacteria 30, and the environmental information (S30). Specifically, the predicting unit 122 calculates the amount of reduction of the bacteria 30 based on the above formula (1). Alternatively, the prediction unit 122 may calculate the bacteria removal rate of the bacteria 30 based on the above equation (2).

Finally, the output unit 140 outputs the calculated reduction amount or bacteria removal rate of the bacteria 30 as the prediction information (S40). The output unit 140 may also display a three-dimensional distribution map of the bacteria removal rate shown in fig. 19, for example.

Fig. 19 is a three-dimensional distribution diagram of the sterilization rate output by the sterilization performance prediction system 100 according to the present embodiment. In fig. 19, the density difference is represented by the density difference of dots.

The three-dimensional distribution chart is a three-dimensional heat map showing the level of the sterilization rate by the shading of color or brightness. The output unit 140 may output a two-dimensional distribution map of the bacteria removal rate obtained by cutting the bacteria off at an arbitrary plane. Alternatively, the output unit 140 may display a graph showing a temporal change in the sterilization rate at an arbitrary position, for example. The output unit 140 may display a three-dimensional distribution map of the amount of reduction of the bacteria 30, instead of the three-dimensional distribution map of the bacteria removal rate. The output unit 140 may display a three-dimensional distribution map of the drug concentration.

[ Effect and the like ]

As described above, the sterilization performance prediction system 100 according to the present embodiment includes: a storage unit 130 that stores a CT value database 131 that associates the types of bacteria with a sterilization index value that is the product of the drug concentration and the time required for sterilization; and a control section 120. The control unit 120 includes: a concentration estimating unit 121 that calculates a concentration of the medicine at a predetermined position in the space 10 in which the medicine 20 is dispersed; and a prediction unit 122 for predicting the survival state of the bacteria 30 at the predetermined position by referring to the CT value database 131 based on the calculated drug concentration, the type of the bacteria 30 to be sterilized, and the environmental information indicating the environment in the space 10.

Thus, the survival state of the bacteria 30 can be predicted based on the environmental information, taking into account the influence of the environment on the drug 20 or the bacteria 30. Therefore, the sterilization performance prediction system 100 can predict the survival state of the bacteria 30 with high accuracy.

For example, in the CT value database 131, the sterilization index value is associated with the type of bacteria for each type of drug. The prediction unit 122 also predicts the survival state of the bacteria 30 by referring to the CT value database 131 based on the drug information indicating the type of the drug 20 dispersed in the space 10.

Thus, for example, when the type of the medicine 20 to be dispensed in the space 10 can be changed, the survival state of the bacteria 30 can be predicted with high accuracy according to the changed type of the medicine 20.

The environmental information includes, for example, first environmental information that affects the state of bacteria 30 to be sterilized and second environmental information that affects the chemical 20 dispersed in the space 10.

Thus, the survival state of the bacteria 30 can be predicted by taking into account both the influence of the environment on the drug 20 and the influence of the environment on the bacteria 30. Therefore, according to the sterilization performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

For example, the first environmental information is information indicating at least one of the air temperature, the humidity, and the ultraviolet ray amount at the predetermined position, and the surface temperature when the predetermined position includes the surface 40 of the member existing in the space 10.

As a result, the survival state of the bacteria 30 can be predicted by taking at least one of the atmospheric temperature, the humidity, the ultraviolet dose, and the surface temperature, which have a large influence on the bacteria 30, into consideration. Therefore, according to the sterilization performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

For example, the second environmental information is information indicating at least one of the temperature, the humidity, and the ultraviolet ray amount at the predetermined position, and the surface temperature and the amount of organic matter adhering to the surface 40 when the predetermined position includes the surface 40 of the member existing in the space 10.

As a result, the survival state of the bacteria 30 can be predicted by taking at least one of the atmospheric temperature, the humidity, the amount of ultraviolet light, the surface temperature, and the amount of organic matter, which have a large influence on the chemical 20, into consideration. Therefore, according to the sterilization performance prediction system 100, the survival state of the bacteria 30 can be predicted with higher accuracy.

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

This enables the environment information to be acquired with high accuracy using the sensor. Since the accuracy and reliability of the environment information are high, the accuracy of the prediction result based on the environment information can be improved.

For example, the concentration estimating unit 121 acquires at least one of the amount of generated medicine 20, the wind direction and the wind speed of the airflow for moving the medicine 20, the self-decomposition coefficient of the medicine 20, the diffusion coefficient of the medicine 20, the adsorption/release coefficient of the medicine 20 with respect to a predetermined surface in the space 10, and the temperature, humidity, and ultraviolet ray amount in the space 10 as input data, and calculates the medicine concentration using the acquired input data.

This makes it possible to estimate the concentration of the drug 20 dispersed in the space 10 with high accuracy. According to the concentration estimation method of the present embodiment, the concentration of the drug 20 diffusing in the space 10 can be estimated with high accuracy without requiring an actual measurement value of the concentration of the drug 20. Therefore, the survival state of the bacteria 30 can be predicted with high accuracy based on the high-accuracy drug concentration.

For example, the sterilization performance prediction method according to the present embodiment includes the steps of: calculating the concentration of the agent at a prescribed position within the space 10 in which the agent 20 is dispersed; and a prediction step of predicting the survival state of the bacteria 30 at the predetermined position by referring to the CT value database 131 in which the types of the bacteria and a sterilization index value, which is a product of the concentration of the drug required for sterilization and the time, are associated with each other based on the calculated drug concentration, the type of the bacteria 30 to be sterilized, and the environmental information indicating the environment in the space 10.

Thus, the survival state of the bacteria 30 can be predicted with high accuracy, as in the sterilization performance prediction system 100.

(embodiment mode 2)

Next, embodiment 2 is explained.

In the sterilization performance prediction system 100 according to embodiment 1, temperature information and the like acquired from a sensor and the like are acquired as the environmental information. In contrast, in the sterilization performance prediction system according to the present embodiment, the spatial information indicating the type of the space 10 in which the medicine 20 is dispersed is acquired as the environmental information. Thus, the sterilization performance prediction system according to the present embodiment can reduce the amount of processing with a small amount of information, and can predict the survival state of the bacteria 30 with high accuracy.

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. Hereinafter, differences from embodiment 1 will be mainly described, and descriptions of common points will be omitted or simplified.

The acquisition unit 310 acquires environmental information, drug information, and bacteria information. The acquisition of the drug information and the bacteria information is the same as the acquisition unit 110 of embodiment 1. In the present embodiment, the acquisition unit 310 acquires spatial information as environmental information. The space information is an example of environmental information, and is information indicating the type of the space 10 in which the medicine 20 is dispersed.

The acquisition unit 310 is implemented by, for example, a communication interface for communicating with a terminal device such as a smartphone operated by a user, or a user interface such as a touch panel or a physical operation button. For example, the acquisition unit 310 causes the 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 acquires the input information as the 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 for spreading the medicine 20. The acquisition unit 310 may select, as the spatial information, an instruction to select 1 scene from a plurality of scenes prepared in advance.

The control unit 320 is different from the control unit 120 according to embodiment 1 in that a prediction unit 322 is provided instead of the prediction unit 122. The prediction unit 322 predicts the survival state of the bacteria 30 by referring to the scene database 331 stored in the storage unit 330 based on the spatial information acquired by the acquisition unit 310 as the environmental information. Specifically, the prediction unit 322 refers to the scene database 331 based on the spatial information to determine the environmental factor f in the expression (1) shown in embodiment 1. The prediction unit 322 predicts the survival state of the bacteria 30 using the formula (1) based on the determined environmental factor f.

The storage section 330 also stores a scene database 331. The storage unit 330 is implemented by a nonvolatile memory such as an HDD or a semiconductor memory.

The scene database 331 is an example of scene information in which at least one of a predetermined temperature, humidity, and ultraviolet ray amount, a surface temperature of a member existing in the space, and an amount of organic matter adhering to a surface of the member is associated with each scene, which is each type of the space 10. Fig. 21 is a diagram showing the scenario database 331 stored in the sterilization performance prediction system 300 according to the present embodiment.

In the scene database 331 shown in fig. 21, 3 scenes, i.e., a kitchen, a living room, and a toilet, are prepared in advance. For each of the 3 scenes, temperature, humidity, ultraviolet amount, and organic matter amount and weighting coefficients are stored in a correlated manner. The weighting factor is an environmental factor f.

The weighting coefficients stored in the scene database 331 are values predetermined based on the corresponding temperature, humidity, ultraviolet amount, and organic matter amount. The specific determination method is the same as the determination method of the environmental factor f described in embodiment 1.

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

The representative values of the temperature, the humidity, the ultraviolet amount, and the organic matter amount stored in the scene database 331 may also be updated according to the space 10. When the representative value is updated, the environment factor f is newly determined and updated.

As described above, in the sterilization performance prediction system 300 according to the present embodiment, for example, the storage unit 330 further stores the scene database 331, and the scene database 331 associates predetermined air temperature, humidity, and ultraviolet ray amount, and at least one of the surface temperature of the member existing in the space 10 and the amount of the organic matter adhering to the surface 40 of the member with each type of the space 10. The environmental information is information indicating the kind of the space 10 in which the medicine 20 is dispersed. The prediction unit 122 predicts the survival state of the bacteria 30 by referring to the scene database 331 based on the environmental information.

Thus, the environment factor f is determined in advance for each scene, and thus, for example, the user can determine the environment factor f without requiring an actual measurement value such as a temperature simply by selecting a scene. Therefore, the amount of calculation can be reduced, and the survival state of the bacteria 30 can be calculated with high accuracy.

(others)

The sterilization performance prediction system, the sterilization performance prediction method, and the like according to the present invention have been described above based on the above-described embodiments, but the present invention is not limited to the above-described embodiments.

For example, in the above-described embodiment, the example in which the CT value database 131 indicating the CT values corresponding to the types of bacteria and the types of medicines is stored in the storage unit 130 is shown, but the present invention is not limited to this. For example, when the medicine 20 to be used is fixed in a specific type, the storage unit 130 may store correspondence information in which the type of bacteria and the CT value are associated with each other.

For example, in the above-described embodiment, the environmental information affecting both the drug 20 and the bacteria 30 is acquired, but only the environmental information affecting the drug 20 or only the environmental information affecting the bacteria 30 may be acquired.

For example, in the above-described embodiment, the CFD analysis is used to estimate the drug concentration, but the present invention is not limited thereto. For example, it may be estimated that the drug is uniformly dispersed in the space 10 based on the amount of the drug 20 generated.

The method of communication between devices described in the above embodiments is not particularly limited. When wireless communication is performed between devices, a method of wireless communication (communication standard) is short-range wireless communication such as Zigbee (registered trademark), Bluetooth (registered trademark), or wireless LAN (Local Area Network). Alternatively, the wireless communication method (communication standard) may be communication via a wide area communication network such as the internet. Instead of wireless communication, wired communication may be performed between the apparatuses. Specifically, the wired communication is Power line carrier communication (PLC), communication using a wired LAN, or the like.

In the above-described embodiment, the process executed by a specific processing unit may be executed by another processing unit. Further, the order of more processes may be changed, or a plurality of processes may be executed in parallel. The distribution of the components included in the sterilization performance prediction system among a plurality of apparatuses is merely an example. For example, a component provided in one device may be provided in another device. In addition, the sterilization performance prediction system may also be implemented as a single device.

For example, the processing described in the above embodiment may be realized by performing centralized processing using a single device (system), or may be realized by performing distributed processing using a plurality of devices. The processor that executes the program may be single or plural. That is, the collective processing may be performed, or the distributed processing may be performed.

In the above-described 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. Each component may be realized by reading out a software program recorded in a recording medium such as an HDD or a semiconductor memory by a program execution Unit such as a CPU (Central Processing Unit) or a processor and executing the software program.

The components such as the control unit may be constituted by 1 or more electronic circuits. The 1 or more electronic circuits may be general-purpose circuits or dedicated circuits, respectively.

The 1 or more electronic circuits may include, for example, a semiconductor device, an IC (Integrated Circuit), an LSI (Large Scale Integration), or the like. The IC or LSI may be integrated into 1 chip or may be integrated into a plurality of chips. Referred to herein as IC or LSI, but the name may vary depending on the degree of Integration, and is referred to as system LSI, VLSI (Very Large Scale Integration), or ULSI (Ultra Large Scale Integration). In addition, an FPGA (Field Programmable gate array) that is programmed after the manufacture of the LSI can be used for the same purpose.

Further, the whole or specific aspects of the present invention can be implemented 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 drive, an HDD, or a semiconductor memory, in which the computer program is stored. The present invention can also be realized by any combination of systems, apparatuses, methods, integrated circuits, computer programs, and recording media.

In addition, the present invention includes a mode obtained by applying various modifications to the respective embodiments as will occur to those skilled in the art, and a mode realized by arbitrarily combining the components and functions in the respective embodiments within a scope not departing from the gist of the present invention.

Description of the reference numerals

10: a space; 20. 20a, 20b, 20c, 20d, 20x, 20 y: a medicament; 30: bacteria; 40: a surface; 100. 300, and (2) 300: a sterilization performance prediction system; 120. 320, and (3) respectively: a control unit; 121: a concentration estimation unit; 122. 322: a prediction unit; 130. 330: a storage unit; 131: CT value database (corresponding information); 331: scene database (scene information).

Claims (10)

1. A sterilization performance prediction system is provided with:

a storage unit that stores correspondence information that associates a type of bacteria with a sterilization index value that is a product of a drug concentration and time required for sterilization; and

a control part for controlling the operation of the display device,

wherein the control unit includes:

a concentration estimation unit that calculates a concentration of the drug at a predetermined position in a space where the drug is dispersed; and

and a prediction unit that predicts the survival state of the bacteria at the predetermined position with reference to the correspondence information based on the calculated drug concentration, the type of bacteria to be sterilized, and environment information indicating the environment in the space.

2. The sterilization performance prediction system according to claim 1, wherein,

in the correspondence information, the bacteria type and the bacteria elimination index value are associated for each type of the medicine,

the prediction unit may predict the survival state of the bacteria by referring to the correspondence information based on drug information indicating a type of the drug to be dispersed in the space.

3. The sterilization performance prediction system according to claim 1 or 2, wherein,

the environment information includes:

first environmental information that affects the state of bacteria to be sterilized; and

second environmental information affecting the agent dispersed in the space.

4. The sterilization performance prediction system according to claim 3, wherein,

the first environmental information is information indicating at least one of an air temperature, a humidity, and an ultraviolet ray amount at the predetermined position, and a surface temperature when the predetermined position includes a surface of a member existing in the space.

5. The sterilization performance prediction system according to claim 3 or 4, wherein,

the second environment information is information indicating at least one of an air temperature, a humidity, and an ultraviolet ray amount at the predetermined position, and a surface temperature and an amount of organic matter adhering to a surface of a member existing in the space at the predetermined position.

6. The sterilization performance prediction system according to claim 1 or 2, wherein,

the storage unit further stores scene information in which at least one of a predetermined temperature, humidity, and ultraviolet ray amount, a surface temperature of a member existing in the space, and an amount of organic matter adhering to a surface of the member is associated with each type of the space,

the environment information is information indicating a kind of a space where the medicine is spread,

the prediction unit predicts the survival state of the bacteria based on the environmental information and also with reference to the scenario information.

7. The sterilization performance prediction system according to any one of claims 1 to 6, wherein,

the system is also provided with a sensor for acquiring the environmental information.

8. The sterilization performance prediction system according to any one of claims 1 to 7, wherein,

the concentration estimating unit acquires, as input data, at least one of a generated amount of the chemical, a wind direction and a wind speed of an airflow for moving the chemical, a self-decomposition coefficient of the chemical, a diffusion coefficient of the chemical, an adsorption/release coefficient of the chemical with respect to a predetermined surface in the space, a temperature, humidity, and an ultraviolet ray amount in the space, and calculates the chemical concentration using the acquired input data.

9. A sterilization performance prediction method includes the following steps:

calculating a concentration of the medicament at a prescribed location within the space in which the medicament is dispersed; and

and a prediction step of predicting the survival state of the bacteria at the predetermined position with reference to correspondence information that associates the type of the bacteria with a bacteria elimination index value that is a product of the concentration of the drug required for the bacteria elimination and time, based on the calculated drug concentration, the type of the bacteria to be eliminated, and environment information indicating the environment in the space.

10. A program for causing a computer to execute the sterilization performance prediction method according to claim 9.

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