Particle identification device

Abstract

The invention provides a particle identification device. The device is characterized by comprising a laser, a focusing lens, a particle cavity, an air pump, an imaging objective lens, an optical camera and a signal processing module. On the basis of a diffraction imaging theory and a microscopic imaging method of particle lateral scattered light, scattered light of particle airflow in a particle cavity is collected by using an imaging objective lens and imaged on an optical camera, extinction characteristics of a scattered light field are analyzed in a signal processing module and compared with calibration data in a standard database, and then the particle flow is detected. And feature identification of different particle types in the air is realized. The invention provides an accurate particle identification device which can be applied to the fields of air quality monitoring, biological medicine, nanotechnology, indoor environment control, pollutant control of industrial production workshops and the like.

Description

(I) Technical field

[0001] The present invention relates to a particle identification device, mainly to the field of optical scattering measurement of particles, and more specifically to a diffraction image identification device of the coherent scattered light intensity of different types of particles in the air, the diffraction image is highly correlated with the morphology, type, refractive index, etc. of the particles. Based on the principle of diffraction imaging of scattered light from particles, a very high identification accuracy can be achieved. (II) Background technology

[0002] Particle characterization technology is widely used in the fields of chemical energy, electronics and electrical, biomedicine, aerospace, environmental pollution, scientific research, etc. Particles are usually formed in the natural environment, but more particles are formed in various industrial production processes. The particle size and type are important parameters, which can directly reflect the properties of the particles. At present, the detection methods for particles include: direct imaging method, optical holography method, light scattering method, sedimentation method, ultrasonic detection method, etc. Among them, the light scattering method has the advantages of fast measurement speed, wide application range, high degree of automation, and online measurement, so it is widely used in particle measurement.

[0003] For example, Chinese patent CN219065204U discloses a particle counter and particle counting system, which reduces the influence of stray light and improves the accuracy of particle counting, but the particle counter and counting system can only realize particle counting and cannot characterize particles. Chinese patent CN210090251U discloses a high-altitude particle identification device based on microscopic magnification and viewing angle sensing, which uses microscopic camera technology to magnify and shoot the scattered light image generated by particles, thereby realizing the detection of high-altitude atmospheric particle morphology. However, the imaging quality is closely related to factors such as particle movement rate and camera exposure time, so that the accuracy and precision of the experimental results will inevitably be affected by the imaging quality. At the same time, the particle identification device can only realize particle morphology measurement and cannot identify the type of particles.

[0004] Chinese patent CN115219436A discloses a polarized optical scattering measurement device and aerosol classification and identification method. The device and method can identify the types of particles, but the structure is complex, the components are numerous, and the price is high, which greatly affects the production cost and mass production possibility of the particle identification device. At the same time, due to the lack of air path, the particles will inevitably be deposited in the device, which will greatly affect the service life of the equipment and the accuracy of the experimental results.

[0005] In order to realize the rapid identification of different types of particles in the air, the present invention proposes a particle identification device. Based on the scattered light diffraction imaging theory of particles, statistical moments are introduced to describe the distribution characteristics of the scattered light diffraction image of particles, and the relationship between the diffraction image texture feature parameters and the particle types is established. By measuring the scattered light diffraction image of unknown particles and obtaining the statistical moment feature parameters, the rapid identification of particles can be realized. Compared with the traditional particle identification scheme, the technical advantages of the device include: using the scattered light diffraction imaging theory of particles, the relationship between the diffraction image texture feature parameters and the particle types is established, and the real-time identification of the particle types is realized; the particle loading and discharge system is introduced to realize the guidance of the particle flow, which improves the particle flow transmission efficiency and the accuracy of particle identification, and reduces the influence of particle residues on the experimental device; the device structure is simple and compact, and has strong robustness to external disturbances. The technical advantages of the device proposed by the present invention enable it to be applicable to the fields of air quality monitoring, biomedicine, nanotechnology, indoor environment control, and pollutant control in industrial production workshops. (III) Summary of the invention

[0006] The purpose of the present invention is to overcome the problems existing in the prior art, such as the lack of particle type identification methods, the experimental results are easily affected by various factors such as imaging quality and digital processing methods, the high price, low service life, numerous system components, and difficulty in miniaturization and integration, and to provide a particle identification device. The device is based on the diffraction imaging theory of particle scattered light and the method of microscopic imaging. The device uses an imaging objective to collect the scattered light of the particle airflow in the particle cavity and images it on an optical camera. By analyzing the characteristics of the scattered light diffraction image, a statistical moment is introduced to describe the distribution characteristics of the particle scattered light diffraction image, and the relationship between the diffraction image texture feature parameters and the particle type is established. The particles are classified to achieve the identification of different types of particles in the air.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A particle identification device consists of a particle loading and discharging system, a light scattering system, and a data acquisition and processing system.

[0009] Specifically speaking, the particle loading and discharge system consists of a particle chamber and an air pump.

[0010] The particle chamber body is provided with an air inlet and an air outlet for particle circulation, a particle flow channel will be formed between the air inlet and the air outlet, and the center line of the air inlet and the air outlet is perpendicular to the optical axis.

[0011] The vacuum pump is used to adjust the flow rate of the particle flow. By adjusting the positive or negative pressure applied by the vacuum pump, the particle flow rate is ensured to be within a certain range. At the same time, it is used to remove the particles after measurement to avoid unnecessary particle residues affecting the experimental results.

[0012] Specifically speaking, the light scattering system consists of a laser source and a focusing lens.

[0013] The laser is used to generate a monochromatic light beam with stable wavelength, small divergence angle, high brightness and good coherence. The scattered light signal after irradiating the particle flow is strong, which helps the particle flow to form a clear diffraction image, facilitating subsequent analysis and processing.

[0014] The focusing lens is used to focus the monochromatic light emitted by the laser, and the focus of the focusing lens is located in the central area of ​​the particle flow.

[0015] Specifically speaking, the data acquisition and processing system consists of an imaging objective, an optical camera and a signal processing module.

[0016] The imaging objective lens is used to collect the scattered light generated by the laser irradiating the particle flow and focus it on the optical camera. At the same time, the imaging objective lens is also used to amplify the scattered light to form a clear scattered image.

[0017] The optical camera is one of the core components of the device. It is located at the focus of the imaging objective lens and forms a scattered image by recording the scattered light focused by the imaging objective lens.

[0018] The calculation formula of the statistical moment in the present invention is:

[0019] Where: μ 1x is the first-order moment in the x direction, μ 1y is the first-order moment in the y direction, μ 2x is the second-order central moment in the x direction, μ 2y is the second-order central moment in the y direction, μ 4x is the fourth-order standard moment in the x direction, μ 4y is the fourth-order standard moment in the y direction, n x is the number of pixels in the x direction, n y is the number of pixels in the y direction, n is the total number of pixels in the area, n ij For each pixel’s grayscale value, x i0 is the index of the center position of each pixel in the x direction, and y j0 is the index of the center position of each pixel in the y direction.

[0020] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0021] 1. Compared with the prior art, the particle identification device provided by the present invention utilizes the diffraction imaging principle of particle coherent scattered light and the method of microscopic imaging, introduces statistical moments to describe the distribution characteristics of the diffraction image of particle scattered light, establishes the correlation between the texture feature parameters of the diffraction image and the particle type, and solves the problem of the difficulty in identifying the types of particles in the air.

[0022] 2. The invention introduces a particle loading and discharge system to solve the problem of particle accumulation in the device, which can effectively reduce the interference of residual particles on the measurement results and the impact on optical components, and can improve the reliability and life of the device.

[0023] 3. The scheme of the present invention has a simple structure and low cost. It also has significant advantages such as fast real-time measurement and high precision. It has broad application prospects and high application value in the fields of air pollution monitoring and nanotechnology. (IV) Description of the drawings

[0024] Figure 1 It is a particle identification device, which consists of a laser, a focusing lens, a particle cavity, an air pump, an imaging lens, an optical camera, and a signal processing module.

[0025] Figure 2 It is the scattered light diffraction image of standard aqueous solution particles.

[0026] Figure 3 This is the scattered light diffraction image of polyethylene terephthalate particles.

[0027] Figure 4 yes Figure 2 The detection results of the particle scattered light diffraction image obtained after Hough transform.

[0028] Figure 5 yes Figure 3 The detection results of the particle scattered light diffraction image obtained using Hough transform. (V) Specific implementation methods

[0029] The present invention is further described below with reference to the accompanying drawings and examples.

[0030] like Figure 1 As shown, a particle identification device includes a particle loading and discharge system, a light scattering system, and a data acquisition and processing system;

[0031] The particle loading and discharge system is composed of a particle chamber (1) and an air pump (2); the particle chamber (1) is composed of an air inlet (101), an air outlet (102) and a cavity (103); the air outlet (102) of the particle chamber (1) is connected to the air pump (2); after the air pump (2) is started, the particles to be tested enter the cavity (103) of the particle chamber (1) through the air inlet (101) of the particle chamber (1), and the air pump (2) discharges the tested particles; the air inlet and the air outlet of the particle chamber are both perpendicular to the optical path; and the air pump is an air pump with an adjustable air pumping speed.

[0032] The light scattering system comprises a laser light source (3) and a focusing lens (4). The laser light generated by the laser light source (3) is focused by the focusing lens (4) onto a particle located at the center of a cavity (103) of a particle cavity (1), causing the particle to scatter light. The wavelength of the light from the laser light source avoids the wavelength strongly absorbed by the particle.

[0033] The data acquisition and processing system comprises an imaging objective lens (5), an optical camera (6) and a signal processing module (7). After the scattered light intensity distribution image of the particles at a fixed angle is collected by the optical camera (6) through the imaging objective lens (5), the signal processing module (7) performs grayscale processing on the image, and uses Hough transform to determine the image recognition area. By calculating the statistical moment of the grayscale value distribution in the area, the particle identification result is given in real time. The magnification of the imaging objective lens ensures that the complete particle scattering stripes can be seen in the field of view; the photosensitive element of the optical camera is one of CCD or CMOS; the observation angle of the particle scattered light intensity distribution image is one or a combination of angles between 0° and 180°.

[0034] The optical camera (6) is an ordinary optical camera, located at the focus of the imaging objective lens, wherein the ordinary optical camera adopts a color CMOS scientific camera, which supports up to 1936*1216 pixels, a resolution of 5.86μm, and a frame rate of 40fps. The signal processing module is a PC.

[0035] To facilitate understanding of the present invention, the present invention is further described below in conjunction with specific embodiments. The present invention can be implemented in many different forms and is not limited to the embodiments described in this specification.

[0036] Step 1: Turn on the atomization device to generate water droplets with a particle size of 4 μm, or turn on the particle generation device to generate 10 μm polyethylene terephthalate particles, turn on the vacuum pump, pass the standard sample particles into the particle cavity, use the focused laser to irradiate the sample particles, and the imaging objective lens collects the scattered light generated by the sample particles onto the optical camera. The scattered light diffraction image of the standard sample particles is gray-scaled. The gray-scale image of the 4 μm water droplet scattered light diffraction image is as follows: Figure 2As shown in the figure, the grayscale image of the scattered light diffraction image of 10μm polyethylene terephthalate particles is as follows Figure 3 shown.

[0037] Step 2: Use Hough transform to determine the position of the diffraction image of scattered light from a single particle. The results are as follows: Figure 4 , Figure 5 shown.

[0038] Step 3: Process the scattered light diffraction image and introduce statistical moments to describe the distribution of the particle scattered light diffraction image using the following formula:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] Where: μ 1x is the first-order moment in the x direction, μ 1y is the first-order moment in the y direction, μ 2x is the second-order central moment in the x direction, μ 2y is the second-order central moment in the y direction, μ 4x is the fourth-order standard moment in the x direction, μ 4y is the fourth-order standard moment in the y direction, n x is the number of pixels in the x direction, n y is the number of pixels in the y direction, n is the total number of pixels in the area, n ij For each pixel’s grayscale value, x i0 is the index of the center position of each pixel in the x direction, and y j0 is the index of the center position of each pixel in the y direction.

[0046] Step 4: Select an appropriate statistical moment threshold to establish the association between the characteristic parameters of the diffraction image statistical moment and the particle type.

[0047] Step 5: arbitrarily select water particles or polyethylene terephthalate particles to be identified, use the moment calculation formula to calculate the scattered light diffraction image of the particles to be identified, and compare and judge with the statistical moment threshold in the standard library to achieve the identification of unknown particle types.

Claims

1. A particle identification device, characterized in that: It includes a particle loading and discharge system, a light scattering system, and a data acquisition and processing system; The particle loading and discharge system is composed of a particle chamber (1) and an air pump (2); the particle chamber (1) is composed of an air inlet (101), an air outlet (102) and a cavity (103); the air outlet (102) of the particle chamber (1) is connected to the air pump (2); after the air pump (2) is started, the particles to be tested enter the cavity (103) of the particle chamber (1) through the air inlet (101) of the particle chamber (1), and the air pump (2) discharges the tested particles; The light scattering system is composed of a laser light source (3) and a focusing lens (4). The laser light generated by the laser light source (3) is focused by the focusing lens (4) onto the particles located at the center of the cavity (103) of the particle cavity (1), so that the particles scatter light. The data acquisition and processing system is composed of an imaging objective lens (5), an optical camera (6) and a signal processing module (7). After the optical camera (6) acquires the scattered light intensity distribution image of the particles at a fixed angle through the imaging objective lens (5), the signal processing module (7) performs grayscale processing on the image, and uses Hough transform to determine the image recognition area. By calculating the statistical moment of the grayscale value distribution in the area and comparing it with the calibration data in the standard database, the particle recognition result is given in real time.

2. A particle identification device according to claim 1, characterized in that: The air inlet and the air outlet of the particle cavity are both perpendicular to the light path.

3. A particle identification device according to claim 1, characterized in that: The air pump is an air pump with adjustable air pumping speed.

4. A particle identification device according to claim 1, characterized in that: The wavelength of the laser light avoids the wavelength strongly absorbed by the particles.

5. A particle identification device according to claim 1, characterized in that: The magnification of the imaging objective lens ensures that complete particle scattering stripes can be seen in the field of view.

6. A particle identification device according to claim 1, characterized in that: The photosensitive element of the optical camera is one of CCD and CMOS.

7. A particle identification device according to claim 1, characterized in that: The observation angle of the particle scattered light intensity distribution image is one or a combination of angles between 0° and 180°.

8. A particle identification device according to claim 1, characterized in that: The signal processing module includes statistical moment calculation of image gray value distribution.

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