Cloud particle number concentration calculation method based on variable volume

Abstract

The invention discloses a variable volume-based cloud particle number concentration calculation method, which comprises the following steps of: placing standard plates with different diameters at different Z positions for imaging to obtain a series of standard plate dot images; dividing all the collected dot images of the standard plate into three categories according to the diameters of the dot images of the standard plate, and calculating the Brenner value of each image in the three categories; setting three Brenner thresholds for the three categories, performing threshold filtering on the images obtained in the previous step, performing statistics on a Z position maximum value and a Z position minimum value corresponding to the diameter of the standard plate, defining an effective sampling depth of the standard plate, and obtaining a corresponding relation set of the diameters of the images corresponding to the three categories and the effective sampling depth; fitting the corresponding relation set to obtain the corresponding relation between the diameter and the sampling depth in each category; and calculating the sampling volumes of the cloud particles with different diameters so as to calculate the number concentration of the cloud particles. The cloud particle number concentration is calculated more accurately by adopting the variable volume, and errors are reduced.

Description

Technical Field

[0001] This invention belongs to the field of cloud and fog particle number calculation, specifically relating to a method for calculating cloud and fog particle number concentration based on variable volume. Background Technology

[0002] Particle number concentration (NC) is a physical quantity that reflects the number of particles per unit volume. It is of great significance in fields such as weather forecasting and weather modification. It is a key physical quantity for studying the microscopic physical structure of clouds and fog and can be directly used to calculate parameters such as liquid water content (LWC), median diameter (MVD), and equivalent diameter (ED).

[0003] The particle number concentration is generally calculated by first dividing the particles into n bins according to their diameter, then counting the number of particles in each bin per unit time, Bin_Cnt[i], and finally obtaining the particle number concentration N[i] for each interval and the total particle number concentration NC per unit time, where V represents the sampling volume of the particles. The calculation formula is as follows: .

[0004] Current methods for calculating particle number concentration are based on a fixed volume. However, in microscopic imaging systems based on the Mie scattering principle, the depth of field corresponding to particles of different diameters is different due to the influence of the optical imaging system. This directly leads to different sampling volumes corresponding to particles of different diameters. Therefore, the method of calculating particle number concentration using a fixed volume has a large error. Summary of the Invention

[0005] In view of the aforementioned shortcomings of existing particle number concentration calculation methods, the purpose of this invention is to propose a cloud and fog particle number concentration calculation method based on variable volume.

[0006] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a method for calculating cloud and fog particle number concentration based on variable volume, comprising: Step 1: Place standard plates of different diameters at different Z positions for imaging to obtain a series of standard plate dot images ranging from clear to blurry; Step 2: Divide all collected standard plate dot images into three categories based on the diameter of the standard plate dot images: Class A: [2um, 10um] Class B: [10um, 100um] Class C: [100um, 500um]; Step 3: Calculate the Brenner value for each image in the three categories, and record the Z position for each image. Step 4: Set three Brenner thresholds for the three categories, perform threshold filtering on the images obtained in Step 3, and count the maximum and minimum Z-position values ​​corresponding to the diameter of the standard plate. Define the effective sampling depth of the standard plate and obtain the correspondence set between the diameter and effective sampling depth of the images corresponding to the three categories. Step 5: Fit the correspondence set obtained in Step 4 to obtain the correspondence between diameter and sampling depth in each category; Step 6: Calculate the sampling volume of cloud and fog particles with different diameters, and then calculate the cloud and fog particle number concentration.

[0007] Furthermore, in step 3, calculating the Brenner value for each image in the three categories includes: Mean filtering is applied to each image in the three categories, and the structured elements are: Calculate the Brenner function value: Furthermore, in step 4, the effective sampling depth of the standard plate is Z. d =Z max -Z min ; The correspondence between the diameter and effective sampling depth of the images for the three categories is as follows: ; Furthermore, the fitting formula for fitting the correspondence set obtained in step 4 in step 5 is as follows: .

[0008] Furthermore, the formula for calculating the sampling volume of cloud particles with different diameters is as follows: In the formula, Area represents the target surface size of the CMOS, and Ratio represents the magnification of the lens.

[0009] The aforementioned method for calculating cloud and fog particle number concentration based on variable volume can achieve the following beneficial effects: using variable volume to calculate cloud and fog particle number concentration is more accurate and has smaller errors.

[0010] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0011] Figure 1This is a schematic diagram illustrating the principle of microscopic imaging in this embodiment; Figure 2 This is a schematic diagram of standard plate dot images acquired at different Z positions in this embodiment. Detailed Implementation

[0012] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention.

[0013] This embodiment presents a method for calculating cloud and fog particle number concentration based on variable volume, including: Step 1: Place standard plates of different diameters at different Z positions for imaging to obtain a series of standard plate dot images ranging from clear to blurry; For details, please refer to Figure 1 In this embodiment, a microscope lens is used to perform bright-field microscopic imaging of micron-sized cloud and fog particles to calculate the cloud and fog particle number concentration. This method results in a larger depth of field (and correspondingly larger volume) for larger particles and a smaller depth of field (and correspondingly smaller volume) for smaller particles. Therefore, a fixed-volume calculation method cannot be used; the particle number concentration should be calculated based on a variable volume. The main device parameters for microscopic imaging are shown in Table 1.

[0014] Table 1 Main Device Parameters The calibration module in this embodiment was performed in an optical laboratory, using "microwires and standard plates for particle volume calibration" (see Table 2). In this embodiment, standard plates of different diameters were placed at different Z positions for imaging, with a Z-direction sampling interval of 20 μm, resulting in a series of standard plate dot images ranging from clear to blurry. Figure 2 In this embodiment, standard plate dot images with a diameter of 10 μm are used as an example, and standard plate dot images are collected at different Z positions.

[0015] Table 2 Sampling range of standard plate dot image Step 2: Divide all collected standard plate dot images into three categories based on the diameter of the standard plate dot images: Class A: [2um, 10um] Class B: [10um, 100um] Class C: [100um, 500um] Step 3: Calculate the Brenner value for each image in the three categories, and record the Z position for each image. Specifically, calculating the Brenner value for each image in the three categories includes: Mean filtering is applied to each image in the three categories, and the structured elements are: Calculate the Brenner function value: Specifically, the Brenner value is the result of the Brenner gradient function calculation used in image processing to evaluate image sharpness. This function quantifies sharpness by calculating the gradient of the grayscale values ​​of image pixels; a larger value indicates a sharper image. Since images contain noise, and noise points affect the calculation of the Brenner function value, a 3x3 mean filter is needed to remove the interference from noise points.

[0016] Step 4: Set three Brenner thresholds for each of the three categories, perform threshold filtering on the image obtained in Step 3, and calculate the maximum value of the Z position corresponding to the diameter of the standard plate (Z). max ) and the minimum value of the Z position (Z min Define the effective sampling depth of the standard plate and obtain the correspondence between the diameter of the corresponding image and the effective sampling depth for the three categories.

[0017] Specifically, the effective sampling depth of the standard plate is Z. d =Z max -Z min The correspondence between the diameter and effective sampling depth of the images for the three categories is as follows: .

[0018] Step 5: Fit the correspondence set obtained in Step 4 to obtain the correspondence between diameter and effective sampling depth in each category; Specifically, the fitting formula is: ; In the formula, These are coefficients to be determined.

[0019] Step 6: Calculate the sampling volume of cloud and fog particles with different diameters, and then calculate the cloud and fog particle number concentration.

[0020] Specifically, the formula for calculating the sampling volume of cloud particles with different diameters is as follows: , In the formula, Area represents the target surface size of the CMOS, and Ratio represents the magnification of the lens.

[0021] The particles are divided into n bins according to their diameter. Then, the number of particles in each bin per unit time is counted as Bin_Cnt[i]. Finally, the particle number concentration N[i] of each interval and the total number concentration NC per unit time are obtained, where V represents the sampling volume of the cloud particles. The formula for calculating the cloud particle number concentration is: .

[0022] Using this embodiment to calculate cloud and fog particle number concentration based on variable volume can reduce errors, calculate particle number concentration more accurately, and improve accuracy.

[0023] The present invention also discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for calculating cloud particle number concentration based on variable volume.

[0024] The above description is merely a preferred embodiment of the present invention. Any simple modifications, equivalent changes, and alterations made by those skilled in the art to the above embodiments without departing from the scope of the present invention and based on the technical essence of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for calculating cloud and fog particle number concentration based on variable volume, characterized in that, include: Step 1: Place standard plates of different diameters at different Z positions for imaging to obtain a series of standard plate dot images ranging from clear to blurry; Step 2: Divide all acquired standard plate dot images into three categories based on the diameter of the standard plate dot images: Class A: [2um, 10um] Class B: [10um, 100um] Class C: [100um, 500um]; Step 3: Calculate the Brenner value for each image in the three categories, and record the Z position for each image. Step 4: Set three Brenner thresholds for the three categories, perform threshold filtering on the images obtained in Step 3, and count the maximum and minimum Z-position values ​​corresponding to the diameter of the standard plate. Define the effective sampling depth of the standard plate and obtain the correspondence set between the diameter and effective sampling depth of the images corresponding to the three categories. Step 5: Fit the correspondence set obtained in Step 4 to obtain the correspondence between diameter and sampling depth in each category; Step 6: Calculate the sampling volume of cloud and fog particles with different diameters, and then calculate the cloud and fog particle number concentration.

2. The method for calculating cloud and fog particle number concentration based on variable volume according to claim 1, characterized in that, Calculating the Brenner value for each image in the three categories involves: Mean filtering is applied to each image in the three categories, and the structured elements are: ； Calculate the Brenner function value: 。 3. The method for calculating cloud particle number concentration based on variable volume according to claim 1, characterized in that, The effective sampling depth of the standard plate is Z. d =Z max -Z min ; The set of correspondences between the diameter and effective sampling depth of the images for the three categories is as follows: 。 4. The method for calculating cloud particle number concentration based on variable volume according to claim 3, characterized in that, The fitting formula for fitting the correspondence obtained in step 4 in step 5 is as follows: 。 5. The method for calculating cloud and fog particle number concentration based on variable volume according to claim 4, characterized in that, The formula for calculating the sampling volume of cloud particles with different diameters is: ； In the formula, Area represents the target surface size of the CMOS, and Ratio represents the magnification of the lens; The formula for calculating the number concentration of particles in clouds and fog is: 。

Images