WO2017212913A1 - Système d'analyse environnementale et procédé d'analyse environnementale - Google Patents

Système d'analyse environnementale et procédé d'analyse environnementale Download PDF

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Publication number
WO2017212913A1
WO2017212913A1 PCT/JP2017/019238 JP2017019238W WO2017212913A1 WO 2017212913 A1 WO2017212913 A1 WO 2017212913A1 JP 2017019238 W JP2017019238 W JP 2017019238W WO 2017212913 A1 WO2017212913 A1 WO 2017212913A1
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Prior art keywords
particles
light
amount
classification
imaging device
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PCT/JP2017/019238
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English (en)
Japanese (ja)
Inventor
静一郎 衣笠
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アズビル株式会社
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence

Definitions

  • the present invention relates to environmental technology, and relates to an environmental analysis system and an environmental analysis method.
  • a detection record of particles in the clean room may be attached to a product manufactured in the clean room as a reference material.
  • the optical particle detection device for example, sucks a gas in a clean room and irradiates the sucked gas with light. If the gas contains microbial particles or non-microbial particles, the particles irradiated with light emit fluorescence or scattered light is generated in the particles. Therefore, by detecting fluorescence or scattered light, it is possible to detect the number and size of microbial particles and non-microbial particles contained in the gas.
  • optical particle detection devices are used and are also used for detecting particles in liquids.
  • the intensity of the fluorescence emitted by the particle may vary depending on the type of particle.
  • the intensity of scattered light generated by the particles may vary depending on the type of particles. Therefore, a method for determining whether a particle is a biological particle or a non-biological particle based on the intensity of fluorescence and the intensity of scattered light has been proposed (see, for example, Patent Documents 1 to 3).
  • An object of the present invention is to provide an environmental analysis system and an environmental analysis method capable of easily analyzing an environment where particles can be generated.
  • a light source that irradiates a fluid with inspection light
  • a photodetector that detects reaction light generated in particles in the fluid irradiated with the inspection light
  • a reaction A particle classification unit for classifying particles based on light
  • a counting unit for counting the amount of classified particles
  • a threshold storage unit for storing an alarm threshold for the amount of particles for each classification
  • An environment analysis system is provided that includes an imaging device control unit that causes an imaging device to image a source when the threshold is exceeded.
  • the photographing device may photograph a moving image.
  • the environment analysis system may further include a moving image storage unit that stores a moving image.
  • the imaging device control unit may cause the imaging device to store the captured moving image in the moving image storage unit.
  • the threshold storage unit further stores a warning threshold lower than the warning threshold, and if the amount of particles exceeds the warning threshold for each classification, the imaging device control unit generates a source in the imaging device. May be taken.
  • the imaging device control unit may cause the imaging device to stop imaging of the generation source.
  • the above-described environmental analysis system may include a plurality of imaging devices, and the plurality of imaging devices may image each generation source of the classified particles.
  • an environment analysis method comprising: causing the imaging device to image the source.
  • the above-described environmental analysis method may further include causing the image capturing device to store the captured moving image in the moving image storage unit when the amount of particles exceeds an alarm threshold value for each classification.
  • the environmental analysis method further includes preparing a warning threshold value lower than the warning threshold value, and causing the imaging device to image the source when the amount of particles exceeds the warning threshold value for each classification. May be.
  • the above-described environmental analysis method may further include causing the imaging device to stop imaging of the source when the amount of particles falls below a warning threshold value for each classification.
  • a plurality of imaging devices may be prepared, and a plurality of imaging devices may be caused to image each generation source of the classified particles.
  • an environment analysis system and an environment analysis method that can easily analyze an environment in which particles can be generated.
  • FIG. 4 is a schematic graph in which an identification boundary for linearly separating a biological particle class and a non-biological particle class is added to the graph shown in FIG. 3.
  • FIG. 4 is a schematic graph in which an identification boundary for linearly separating a biological particle class and a non-biological particle class is added to the graph shown in FIG. 3.
  • FIG. 4 is a schematic graph in which an identification boundary for nonlinearly separating a class of biological particles and a class of non-biological particles is added to the graph shown in FIG. 3.
  • 6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention.
  • 6 is a schematic graph of an identification boundary in an xz two-dimensional coordinate system at an arbitrary y value according to the first embodiment of the present invention.
  • It is a schematic diagram of the particle
  • 1 is a schematic diagram of an environmental analysis system according to a first embodiment of the present invention. It is a schematic diagram of the particle
  • nicotinamide adenine dinucleotide (NADH) and riboflavin contained in the biological particles emit fluorescence. Even if light is applied to non-living particles, the non-living particles may emit light in the fluorescence band. For example, fluorescent particles scattered from a cleaned gown made of polyester emit fluorescence when irradiated with light. Polystyrene particles also fluoresce and reverse their color.
  • nitrogen oxide (NO x ) containing nitrogen dioxide (NO 2 ) in the gas sulfur oxide (SO x ), ozone gas (O 3 ), aluminum oxide-based gas, aluminum alloy, glass powder, and If decontamination gas for decontaminating foreign substances such as Escherichia coli and mold is included, gas-containing substances that may be smaller than these particles that cause Mie scattering receive light and emit light in the fluorescence band .
  • nitrogen dioxide when nitrogen dioxide absorbs light, it emits red-shifted light and returns to the ground state.
  • the absorption spectrum of nitrogen dioxide has a peak in the vicinity of a wavelength of 440 nm, but has a wide band of about 100 to 200 nm. Therefore, when excitation of NADH-derived fluorescence and flavin-derived fluorescence with light having a wavelength of 405 nm in the presence of nitrogen dioxide, fluorescence can be excited even in nitrogen dioxide where NADH and flavin and the absorption spectrum of excitation light overlap.
  • Nitrogen dioxide is generated by the reaction of nitrogen and oxygen in the gas when the substance burns.
  • the gas to be inspected originally does not contain nitrogen dioxide, if the gas to be inspected is irradiated with laser light having a high beam density or strong electromagnetic radiation as excitation light, the substance in the gas will burn. Nitrogen dioxide is generated, and nitrogen dioxide may fluoresce. Further, nitric oxide and ozone react to form nitrogen dioxide, which may emit fluorescence.
  • FIG. 1 shows the light intensity of the wavelength in the band of 530 nm or more on the horizontal axis for the light in the fluorescence bands emitted by each of Staphylococcus epidermidis, Bacillus subtilis spores, Escherichia coli, glass, and aluminum irradiated with excitation light. It is the graph which plotted the light intensity of the wavelength in the band around 440 nm on the vertical axis. As shown in FIG.
  • the ratio of the light intensity of the wavelength in the band of 530 nm or more to the light intensity of the wavelength in the band near 440 nm tends to be small in the non-living material and large in the microbial particle. Therefore, by measuring the intensity of light in the fluorescence band emitted by a substance for each of a plurality of wavelengths and taking a correlation between them, it is possible to identify whether the substance is a living organism or a non-living organism.
  • the intensity of the scattered light generated by the particles varies depending on the type of the particles. Therefore, as shown in FIG. 3, light is irradiated to each of a plurality of types of known biological particles and non-biological particles on a three-dimensional coordinate system including an x-axis, a y-axis, and a z-axis representing the intensity of scattered light. Plotting the measurement value of the light intensity of the fluorescence band having the first wavelength, the measurement value of the light intensity of the fluorescence band having the second wavelength, and the measurement value of the intensity of the scattered light, It is possible to define a function f (x, y, z) that gives an identification boundary with an abiotic.
  • particles that should be classified into the biological particle class are classified as non-biological particles or non-biological particles.
  • particles to be classified into the biological particle class are classified as biological particles.
  • FIG. 5 when the biological particle class and the non-biological particle class are separated nonlinearly, the particles that should be classified into the biological particle class are classified as non-biological particles. It becomes possible to reduce that the particle
  • a function f (x, y, z) that gives an identification boundary for nonlinearly separating a living thing and a non-living object is a support vector machine (SVM) that obtains an identification boundary from the learning data so that the distance to each data point is maximized.
  • SVM support vector machine
  • Nonlinear classifiers are not limited to support vector machines. For example, boosting with increased accuracy by combining many discriminators, neural networks that simulate the characteristics of brain functions on a computer, and other methods such as decision trees, nearest neighbor search, and case-based reasoning A discriminator can be used.
  • the particles irradiated with light include microbial particles, non-microbial particles having a larger particle size than microbial particles, and non-microbial particles having a smaller particle size than microbial particles, in a three-dimensional coordinate system
  • particles that give light intensity plotted in a space surrounded by a function f (x, y, z) that gives an identification boundary can be classified into a biological class.
  • particles that give light intensity plotted outside the space enclosed by the function f (x, y, z) can be classified into the non-living class.
  • a function f (x, y, z) that gives an identification boundary between a living organism and a non-living organism is defined in advance in the three-dimensional coordinate system, and then the first generated when the inspection light is irradiated to an unknown particle.
  • the measured particle is determined to be a living organism, and if the measured value is plotted outside the space enclosed by the function f (x, y, z), the measured particle is considered to be an abiotic organism. It is possible to determine.
  • the multivariable function f (x, y, z) that gives an identification boundary is a multivalent function that outputs two values of the dependent variable z for a set of independent variables (x, y)
  • FIG. 6 and FIG. 7 the first and second fluorescence band light intensities having a set of (x 1 , y 1 ) values are provided, and the first of the scattered light intensities at the discrimination boundary is given.
  • Particles that give a measure of scattered light intensity greater than the boundary value are non-biological particles.
  • the intensity of light in the first and second fluorescence bands having a set of (x 1 , y 1 ) values is given, and the intensity of scattered light is smaller than the first boundary value of the intensity of scattered light.
  • a particle that provides a measurement and also provides a measurement of the intensity of scattered light that is greater than the second boundary value of the intensity of scattered light at the identification boundary is a biological particle. Further, the scattered light is given a set of (x 1 , y 1 ) values of the first and second fluorescence band light intensity and is smaller than the second boundary value of the scattered light intensity at the identification boundary. Particles that give a strength measurement of are non-biological particles. However, the shape of the identification boundary can vary depending on the sample.
  • the particle detection device 1 included in the environmental analysis device includes a light source 10 that irradiates a fluid with inspection light, and a fluid that is irradiated with the inspection light.
  • a detector 15 for detecting reaction light generated in the particles in the particles a particle classification unit 301 for classifying particles based on the reaction light, a counting unit 302 for counting the amount of classified particles, and a particle classification unit for each classification.
  • a threshold storage unit 352 that stores an alarm threshold for the amount, and an alarm unit 303 that issues an alarm when the amount of particles exceeds the alarm threshold for each classification.
  • the particle classification unit 301, the counting unit 302, and the alarm unit 303 are included in, for example, a central processing unit (CPU) 300.
  • the threshold storage unit 352 is included in the storage device 350 connected to the CPU 300, for example.
  • the output device 401 is connected to the CPU 300.
  • the output device 401 for example, a display, a printer, an acoustic device, or the like can be used.
  • reaction light generated in the particles to be measured irradiated with inspection light include fluorescence at a plurality of wavelengths and scattered light.
  • the photodetector 15 is, for example, the intensity of the first reaction light having the first wavelength, the intensity of the second reaction light having the second wavelength, and the intensity of the third reaction light having the third wavelength. Measure the measured value.
  • the first wavelength, the second wavelength, and the third wavelength are different.
  • the “light in the fluorescence band” includes fluorescence, autofluorescence, and light that is not necessarily fluorescence, but whose wavelength band overlaps with fluorescence.
  • the first classification particles are biological particles and the second classification particles are non-biological particles will be described.
  • the storage device 350 of the particle detection device 1 further includes a boundary information storage unit 351 that stores an identification boundary that nonlinearly separates the first class particle class and the second class particle class.
  • the particle classification unit 301 classifies the particles to be measured into one of the first and second classification classes based on the first to third reaction light intensity measurements and the identification boundary.
  • the gas to be inspected by the particle detection device 1 to determine whether or not it contains particles is ejected from the nozzle 40.
  • excitation light having a broadband wavelength is emitted from the light source 10 as inspection light.
  • the light source 10 emits the inspection light having a broadband wavelength toward the flow cell or the like through which the liquid flows.
  • the fluid is a gas
  • the light source 10 for example, a light emitting diode (LED) and a laser can be used.
  • the wavelength of the inspection light is, for example, 250 to 550 nm.
  • the inspection light may be visible light or ultraviolet light.
  • the wavelength of the inspection light is, for example, in the range of 400 to 550 nm, for example, 405 nm.
  • the wavelength of the inspection light is, for example, in the range of 300 to 380 nm, for example, 340 nm.
  • the wavelength of the inspection light is not limited to these.
  • a light source driving power source 11 that supplies power to the light source 10 is connected to the light source 10.
  • a power source control device 12 that controls the power supplied to the light source 10 is connected to the light source driving power source 11.
  • the light detector 15 is included in the fluid ejected from the nozzle 40 and measures the intensity of the light in the first fluorescence band and the intensity of the light in the second fluorescence band generated by the particles to be measured irradiated with the inspection light.
  • a fluorescence intensity measuring device 102 and a scattered light measuring device 105 that measures scattered light generated by the particles to be measured irradiated with the inspection light are provided.
  • the light source 10, the fluorescence intensity measuring device 102, and the scattered light measuring device 105 are provided in the housing 30. Further, the fluorescence intensity measuring device 102 and the scattered light measuring device 105 are electrically connected to the CPU 300.
  • the fluorescence intensity measuring device 102 detects light in the fluorescence band emitted by the particle to be measured.
  • the fluorescence intensity measuring instrument 102 receives a first light receiving element 20A that receives light in the fluorescence band at the first wavelength, and a second light reception that receives light in the fluorescence band at a second wavelength different from the first wavelength. And an element 20B.
  • the first wavelength may have a band. The same applies to the second wavelength.
  • a photodiode, a phototube or the like can be used as the first light receiving element 20A and the second light receiving element 20B. When receiving light, the light energy is converted into electric energy.
  • the amplifier 21A for amplifying the current generated in the first light receiving element 20A is connected to the first light receiving element 20A.
  • An amplifier power supply 22A that supplies power to the amplifier 21A is connected to the amplifier 21A.
  • the amplifier 21A is connected to a light intensity calculating device 23A that receives the current amplified by the amplifier 21A and calculates the intensity of the light received by the first light receiving element 20A.
  • the light intensity calculation device 23A is connected to a light intensity storage device 24A that stores the light intensity calculated by the light intensity calculation device 23A.
  • the amplifier 21B that amplifies the current generated in the second light receiving element 20B is connected to the second light receiving element 20B.
  • An amplifier power supply 22B that supplies power to the amplifier 21B is connected to the amplifier 21B.
  • the amplifier 21B is connected to a light intensity calculation device 23B that receives the current amplified by the amplifier 21B and calculates the intensity of light received by the second light receiving element 20B.
  • a light intensity storage device 24B that stores the light intensity calculated by the light intensity calculation device 23B is connected to the light intensity calculation device 23B.
  • the scattered light measuring device 105 detects scattered light generated by the particles to be measured irradiated with the inspection light.
  • the scattered light measuring device 105 includes a scattered light receiving element 50 that receives scattered light.
  • a scattered light receiving element 50 a photodiode or the like can be used. When light is received, the light energy is converted into electric energy.
  • the scattered light receiving element 50 is connected to an amplifier 51 that amplifies the current generated in the scattered light receiving element 50.
  • An amplifier power supply 52 that supplies power to the amplifier 51 is connected to the amplifier 51.
  • the amplifier 51 is connected to a light intensity calculation device 53 that receives the current amplified by the amplifier 51 and calculates the intensity of scattered light received by the scattered light receiving element 50.
  • a light intensity storage device 54 that stores the intensity of scattered light calculated by the light intensity calculation device 53 is connected to the light intensity calculation device 53.
  • the identification boundary stored in the boundary information storage unit 351 is given by, for example, a multivariable function having the first fluorescent band light intensity, the second fluorescent band light intensity, and the scattered light intensity as variables. .
  • the boundary information storage unit 351 stores, for example, a three-dimensional coordinate system including a multivariable function.
  • the three-dimensional coordinate system includes an x coordinate indicating the intensity of light in the first fluorescence band, a y coordinate indicating the intensity of light in the second fluorescence band, and a z coordinate indicating the intensity of scattered light.
  • the three-dimensional coordinate system is represented by a three-dimensional table including N ⁇ N ⁇ N cells, where N is an integer. In this case, for example, an index from 0 to N-1 is assigned to cells in the x direction, and an index from 0 to N-1 is assigned to cells in the y direction. The direction cells are also indexed from 0 to N-1.
  • the three-dimensional table is composed of 256 ⁇ 256 ⁇ 256 cells
  • the cells in the x direction are indexed from 0 to 255
  • the cells in the y direction are also numbered 0 to 255. Indexes from 0 to 255 are also assigned to cells in the z direction.
  • the intensity of light is represented by a voltage signal within a range of 0 to 5 V, for example.
  • the following equation (1) is used.
  • I [N I * (S D / S M )] (1)
  • N I is the number of index, for example, 256.
  • S D is a measured value of light intensity represented by a voltage signal.
  • S M is the maximum value that the light intensity represented by the voltage signal can take.
  • the index I calculated by the equation (1) is an integer from 0 to 255.
  • Each cell included in the region of the biological particle class defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is provided with an identifier of the biological particle class.
  • Each cell included in the non-biological particle region defined by the multivariable function that nonlinearly separates the biological particle class and the non-biological particle class is assigned an identifier of the non-biological particle class.
  • each cell included in a region where the fluorescence intensity is determined to be equal to or less than the predetermined threshold value determined by the predetermined threshold value of the fluorescence intensity is given an identifier of a class of non-fluorescent particles.
  • the identifier of the class of biological particles, the identifier of the class of non-biological particles, or the identifier of the class of non-fluorescent particles is identified from the specified cells. Is possible to get.
  • the particle classification unit 301 shown in FIG. 8 uses the above equation (1), for example, to measure the intensity of light in the first fluorescence band emitted from the particle to be measured and the intensity of light in the second fluorescence band.
  • a cell of coordinates (x, y, z) in the three-dimensional table stored in the boundary information storage unit 351 corresponding to the measurement value and the measurement value of the intensity of scattered light is specified. Further, when the identifier in the cell of the specified coordinates (x, y, z) is a biological particle class, the particle classification unit 301 classifies the particle to be measured into the biological particle class.
  • the particle classification unit 301 classifies the measured particle into a class of a non-biological particle. Further, the particle classification unit 301 classifies the particles to be measured into the non-fluorescent particle class when the identifier in the cell of the specified coordinates (x, y, z) is the non-fluorescent particle class.
  • the particle classification unit 301 may classify particles that are not classified as fluorescent particles as non-fluorescent particles.
  • the counting unit 302 included in the CPU 300 counts the amount of particles for each classification. For example, the counting unit 302 counts the number or concentration of particles classified into the biological particle class per unit time or operation time. The counting unit 302 counts the number or concentration of particles classified into the non-biological particle class per unit time or per operation time. Further, the counting unit 302 counts the number or concentration of particles classified into the non-fluorescent particle class per unit time or operation time. The counting unit 302 may count the amount obtained by subtracting the total amount of fluorescent particles from the total amount of particles as the amount of non-fluorescent particles. The counting unit 302 outputs the amount of particles counted for each classification from the output device 401.
  • the counting unit 302 may output the amount of particles counted for each classification as a numerical value or a graph as shown in FIG.
  • the counting unit 302 stores the amount of particles counted for each classification in the data storage unit 353 included in the storage device 350 illustrated in FIG.
  • the threshold storage unit 352 included in the storage device 350 stores an alarm threshold for the amount of particles for each classification.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the biological particle class per unit time or per operation time.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-biological particle class per unit time or per operation time.
  • the threshold storage unit 352 stores an alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class per unit time or per operation time.
  • the threshold storage unit 352 may store an alarm threshold for the total amount of particles before classification.
  • the alarm threshold for the number or concentration of particles classified into the biological particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the biological particle class is set high.
  • the alarm threshold for the number or concentration of particles classified into the non-living particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the non-living particles class is set high.
  • the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set low.
  • the alarm threshold for the number or concentration of particles classified into the non-fluorescent particle class is set high.
  • the alarm unit 303 included in the CPU 300 issues an alarm when the amount of particles exceeds the alarm threshold for each classification. For example, as shown in FIG. 11, the alarm unit 303 outputs an output when the amount of particles classified into the biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. An alarm is issued via the device 401. In addition, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-biological particle class counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352. To emit. Further, the alarm unit 303 issues an alarm via the output device 401 when the amount of particles classified into the non-fluorescent particle class counted by the counter unit 302 exceeds the alarm threshold value stored in the threshold value storage unit 352. To emit.
  • the environmental analysis apparatus captures the moving images captured by the capturing apparatuses 3A, 3B, and 3C and the capturing apparatuses 3A, 3B, and 3C that capture the moving images of the particle generation sources. And a moving image storage unit 4 to be stored.
  • the number of imaging devices included in the environmental analysis device is not particularly limited, and may be one or more.
  • the particle detection device 1, the imaging devices 3 ⁇ / b> A, 3 ⁇ / b> B, 3 ⁇ / b> C and the moving image storage unit 4 are connected via a network 2, for example.
  • the CPU 300 of the particle detection device 1 causes the imaging device control unit 304 to cause the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the alarm threshold value for each classification. Is further provided.
  • the photographing devices 3A, 3B, and 3C are arranged in a clean room, for example.
  • the photographing devices 3A, 3B, and 3C are arranged so as to be able to photograph a moving image around the air intake port of the particle detection device 1, for example. Air taken in from the air intake port is ejected from the nozzle 40 shown in FIG.
  • the air intake port of the particle detection apparatus 1 shown in FIG. 13 is preferably a place where it is expected to be a particle generation source and its vicinity. Examples of the place where the generation source of particles is expected include a manufacturing apparatus including a conveying device and a place where an operator such as a work place or a passage frequently appears.
  • the source of particles may be predicted for each type of particle classified.
  • the imaging device control unit 304 illustrated in FIG. 8 is configured when the amount of particles counted by the counting unit 302 exceeds the alarm threshold stored in the threshold storage unit 352 for each classification. 12 and the imaging apparatuses 3A, 3B, and 3C shown in FIG. 13 start imaging of the generation source of the particles, and the captured moving image is stored in the moving image storage unit 4.
  • the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIGS. 12 and 13 when the amount of particles counted by the counting unit 302 is lower than the alarm threshold stored in the threshold storage unit 352 for each classification.
  • the imaging device control unit 304 shown in FIG. 8 determines that a predetermined time elapses when the amount of particles counted by the counting unit 302 falls below the alarm threshold stored in the threshold storage unit 352 for each classification. After that, the photographing apparatuses 3A, 3B, and 3C shown in FIGS. 12 and 13 may stop photographing the particle generation source and storing the moving image.
  • the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C illustrated in FIGS. 12 and 13 to start capturing the particle generation source, and saves the captured moving image as a moving image by multitasking. Save to Part 4.
  • the environmental analysis apparatus can capture a particle generation source when the amount of particles counted for each classification exceeds a threshold set for each classification. is there. Therefore, compared with the case where the generation source of particles is constantly monitored, for example, the capacity of the moving image storage unit 4 can be reduced, and the operation cost of the apparatus can be reduced.
  • the capacity of the moving image storage unit 4 can be reduced, and the operation cost of the apparatus can be reduced.
  • an amount of microorganisms greater than or equal to the threshold it is possible to take measures such as cleaning or sterilizing or sterilizing the clean room.
  • a non-biological particle amount exceeding the threshold it is possible to grasp the deterioration status of the device that is the source of the non-biological particle, or take measures such as performing maintenance of the device. It is.
  • the threshold storage unit 352 illustrated in FIG. 8 further stores a warning threshold lower than the alarm threshold as illustrated in FIG.
  • the imaging device control unit 304 illustrated in FIG. 8 causes the imaging devices 3A, 3B, and 3C to image the generation source when the amount of particles exceeds the warning threshold value for each classification.
  • the captured moving image is not stored in the moving image storage unit 4.
  • the image capturing device control unit 304 causes the image capturing devices 3A, 3B, and 3C to continue image capturing of the source, and further captures the captured moving image in the moving image storage unit 4. Save.
  • the imaging device control unit 304 illustrated in FIG. 8 performs the processing illustrated in FIG. 12 and FIG. 13 when the amount of particles counted by the counting unit 302 is lower than the warning threshold stored in the threshold storage unit 352 for each classification.
  • the alarm threshold for the amount of particles for each particle classification stored in the threshold storage unit 352 shown in FIG. 16 is the imaging device 3A shown in FIGS. Set for every 3B and 3C. Specifically, the alarm threshold for each particle classification for which the imaging device 3A starts to shoot moving images, the alarm threshold for each particle classification for which the imaging device 3B starts to shoot moving images, and the imaging device 3C to shoot moving images. Are stored in the threshold value storage unit 352. The same applies to the warning threshold.
  • the CPU 300 shown in FIG. 16 corrects the alarm threshold value and the warning threshold value according to the amount of particles for each classification detected in the past with the air taken in from each of the plurality of air intake ports of the particle detector 1.
  • a correction unit 305 is further provided.
  • the threshold correction unit 305 detects the air taken in at the first air intake port. The correction is made to lower the alarm threshold of the amount of particles classified into the biological particle class.
  • the threshold correction unit 305 converts the air taken in at the second air intake port
  • a correction is made to lower the alarm threshold for the amount of particles classified into the non-biological particle class.
  • the threshold correction unit 305 converts the air taken in at the third air intake port into On the other hand, a correction is made to lower the alarm threshold for the amount of particles classified into the non-fluorescent particle class.
  • the threshold value is corrected based on the past particle detection tendency, and for example, the occurrence of particles of a specific classification can be monitored more frequently. It becomes possible.
  • the environment analysis apparatus As shown in FIG. 17, the environment analysis apparatus according to the fourth embodiment records the entry / exit time of the worker in the clean room using an identification IC tag or the like worn by the worker. 18 is further provided. A record of the entry / exit time of the worker into the clean room of the worker recording unit 306 is stored in the data storage unit 353.
  • the environmental analysis apparatus further includes a merging unit 307 that generates merged data obtained by merging the data of the amount of particles generated for each classification in time series and the data of workers entering and leaving the room in time series.
  • the merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401. According to the fourth embodiment, for example, based on the merged data, it is possible to grasp the classification of particles that are likely to occur when each worker enters the clean room.
  • the time series data of the work process performed in the clean room is stored in the data storage unit 353 shown in FIG.
  • the merging unit 307 generates merged data obtained by merging the data of the amount of particles generated for each classification in the time series and the data of the performed work processes in the time series. .
  • the merging unit 307 stores the merged data in the data storage unit 353 and outputs it from the output device 401.
  • the merging unit merges the data of the amount of particles generated for each classification in the time series, the data of the work process performed in the time series, and the data of the worker who entered and exited the room in the time series.
  • the merged data may be generated.
  • the first class of particles may be a kind of non-biological particle
  • the second class of particles may be another kind of non-biological particle.
  • the method of classifying the particles is arbitrary. Thus, it should be understood that the present invention includes various embodiments and the like not described herein.

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Abstract

La présente invention concerne un système d'analyse environnemental qui comprend : une source de lumière (10) pour projeter une lumière d'inspection sur un fluide ; un détecteur de lumière (15) pour détecter la lumière de réaction générée par les particules dans un fluide lorsque la lumière d'inspection est projetée sur celui-ci ; une unité de classification de particules (301) qui classe les particules sur la base de la lumière de réaction ; une unité de comptage qui compte la quantité de particules classées ; une unité de stockage de valeur seuil (352) qui stocke la valeur seuil d'alarme pour la quantité de particules pour chaque catégorie ; une unité d'alarme (303) qui génère une alarme si la quantité de particules dépasse une valeur seuil d'alarme pour chaque catégorie ; une unité d'imagerie qui capture une image de la source de génération des particules ; et une unité de commande de dispositif d'imagerie (304) qui entraîne la capture par le dispositif d'imagerie d'une image de la source de génération si la quantité de particules dépasse une valeur seuil d'alarme pour chaque catégorie.
PCT/JP2017/019238 2016-06-09 2017-05-23 Système d'analyse environnementale et procédé d'analyse environnementale WO2017212913A1 (fr)

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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06347396A (ja) * 1993-06-11 1994-12-22 Nec Corp 塵埃濃度管理システムおよび塵埃濃度管理方法
JP2001118155A (ja) * 1999-10-20 2001-04-27 Mitsubishi Electric Corp 人体検知装置、人体検知方法およびその方法をコンピュータに実行させるプログラムを記録したコンピュータ読み取り可能な記録媒体
JP2007147519A (ja) * 2005-11-29 2007-06-14 Nidec Sankyo Corp 粒子計数装置及び粒子計数システム
JP2009030837A (ja) * 2007-07-25 2009-02-12 Toppan Printing Co Ltd 画像処理を用いたクリーンルーム送風量制御システム
JP2010266077A (ja) * 2009-05-12 2010-11-25 Atsuo Nozaki 空気清浄装置及びこれを用いた空気清浄監視システム
JP2013011962A (ja) * 2011-06-28 2013-01-17 Seiko Instruments Inc センサネットワークシステム
JP2014006218A (ja) * 2012-06-27 2014-01-16 Rion Co Ltd 粒子計数システム
JP2016008958A (ja) * 2014-06-26 2016-01-18 アズビル株式会社 粒子検出装置及び粒子の検出方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06347396A (ja) * 1993-06-11 1994-12-22 Nec Corp 塵埃濃度管理システムおよび塵埃濃度管理方法
JP2001118155A (ja) * 1999-10-20 2001-04-27 Mitsubishi Electric Corp 人体検知装置、人体検知方法およびその方法をコンピュータに実行させるプログラムを記録したコンピュータ読み取り可能な記録媒体
JP2007147519A (ja) * 2005-11-29 2007-06-14 Nidec Sankyo Corp 粒子計数装置及び粒子計数システム
JP2009030837A (ja) * 2007-07-25 2009-02-12 Toppan Printing Co Ltd 画像処理を用いたクリーンルーム送風量制御システム
JP2010266077A (ja) * 2009-05-12 2010-11-25 Atsuo Nozaki 空気清浄装置及びこれを用いた空気清浄監視システム
JP2013011962A (ja) * 2011-06-28 2013-01-17 Seiko Instruments Inc センサネットワークシステム
JP2014006218A (ja) * 2012-06-27 2014-01-16 Rion Co Ltd 粒子計数システム
JP2016008958A (ja) * 2014-06-26 2016-01-18 アズビル株式会社 粒子検出装置及び粒子の検出方法

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