Correction method for particle size parameters obtained by cross-section static image analysis method

Abstract

The invention relates to the technical field of test analysis, and discloses a method for correcting particle granularity parameters obtained by a section static image analysis method, which comprises the following steps: S10, randomly generating a plurality of three-dimensional samples, and calculating actual granularity parameters of each three-dimensional sample; s20, performing random cutting on each three-dimensional sample, and calculating a section granularity parameter of each three-dimensional sample; s30, establishing a mathematical relationship model of the actual granularity parameter and the section granularity parameter; and S40, using the mathematical relationship model to correct particle granularity parameters obtained by a section static image analysis method. In this way, granularity parameters closer to real three-dimensional distribution can be obtained through correction. The method can effectively improve the accuracy of particle size analysis of the dense material cross-section image.

Description

Technical Field

[0001] This application relates to the field of testing and analysis technology, and for example to a method for correcting particle size parameters obtained by cross-sectional static image analysis. Background Technology

[0002] Particle size analysis is a crucial testing and analytical technique in many fields, including scientific research and industrial production. Its main purpose is to accurately determine the particle size distribution of a particle population, encompassing important information such as particle size and its distribution. Common particle size analysis methods include laser particle size analysis, sedimentation methods, sieving methods, static image analysis, and dynamic image analysis.

[0003] Static image analysis is a particle size analysis method based on image processing technology. Its core principle is as follows: First, static images of the particles are acquired. These images can be obtained using imaging equipment such as cameras, microscopes, and scanning electron microscopes. During imaging, the particles are positioned on a specific plane, forming a clear two-dimensional cross-sectional image. Subsequently, image analysis software is used to process and analyze these images, identifying the particle outlines in the images and calculating the geometric parameters of each particle, such as area, perimeter, and equivalent circle diameter. Then, statistical methods are used to obtain the overall particle size distribution of the particle population. Static image analysis yields the particle size distribution in a two-dimensional image space, which differs somewhat from the actual particle size distribution in three-dimensional space. The probability of particles of different sizes appearing on the cross-section varies. Clearly, larger particles are more easily cut or polished and exposed on the cross-section. At the same time, particles are not necessarily cut or polished precisely at their maximum diameter; rather, they are often cut or polished at locations other than their maximum diameter, resulting in a smaller diameter on the cross-section. The degree of reduction in the particle's cross-sectional diameter depends on the cutting location or the degree of polishing. The two effects mentioned above ultimately lead to a difference between the particle size distribution on the cross section and the actual distribution, that is, the particle size parameters obtained by the cross section static image analysis method are not consistent with the actual parameters.

[0004] There are generally two methods for statistically analyzing particle frequency: number frequency and volume frequency. Number frequency refers to the proportion of particles of different sizes to the total number of particles within a certain range. Volume frequency refers to the proportion of the volume of particles of different sizes to the total volume of particles within a certain range. The average particle size obtained using the number method is called the number-average particle size, and the average particle size obtained using the volume method is called the volume-average particle size. Because particles of different sizes have different volumes, the number distribution and volume distribution of sample particles generally differ, ultimately leading to differences in their number-average particle size and volume-average particle size.

[0005] Besides average particle size, the sorting factor is another key parameter for measuring particle size. It also has a quantitative sorting factor obtained by the quantitative method and a volumetric sorting factor obtained by the volumetric method, and there are generally differences between them.

[0006] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art: Existing static image analysis, particle frequency statistics, and sorting coefficient methods all yield particle size parameters that differ from actual parameters, leading to inaccurate particle size analysis results.

[0007] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0009] This disclosure provides a method for correcting particle size parameters obtained by cross-sectional static image analysis to improve the accuracy of particle size analysis results.

[0010] In some embodiments, the method for correcting particle size parameters obtained by cross-sectional static image analysis includes: S10, randomly generating multiple three-dimensional samples and calculating the actual particle size parameters of each three-dimensional sample; S20, randomly cutting each three-dimensional sample and calculating the cross-sectional particle size parameters of each three-dimensional sample; S30, establishing a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters; and S40, using the mathematical relationship model to correct the particle size parameters obtained by cross-sectional static image analysis.

[0011] In some embodiments, the device for correcting particle size parameters obtained by cross-sectional static image analysis includes: a sample generation module configured to randomly generate multiple three-dimensional samples and calculate the actual particle size parameters of each three-dimensional sample; a sample cutting module configured to randomly cut each three-dimensional sample and calculate the cross-sectional particle size parameters of each three-dimensional sample; a relationship establishment module configured to establish a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters; and a parameter correction module configured to correct the particle size parameters obtained by cross-sectional static image analysis using the mathematical relationship model.

[0012] The method for correcting particle size parameters obtained by cross-sectional static image analysis provided in this disclosure can achieve the following technical effects: First, three-dimensional samples containing particles of different sizes are randomly generated using computer forward modeling, and their actual particle size parameters are statistically analyzed. Second, the three-dimensional samples are randomly cut multiple times, and the cross-sectional particle size parameters obtained from each cut are calculated. Then, based on a large amount of forward modeling data, a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters is established using regression analysis. Finally, the particle size parameters obtained from the test sample through cross-sectional static image analysis are substituted into this mathematical relationship model to obtain particle size parameters that more closely approximate its true three-dimensional distribution. This method can effectively improve the accuracy of particle size analysis of cross-sectional images of dense materials (such as rocks and metals).

[0013] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0014] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a schematic diagram of a method for correcting particle size parameters obtained by cross-sectional static image analysis, provided in an embodiment of this disclosure. Figure 2 This is a schematic diagram of a three-dimensional sample composed of particles of different sizes provided in the embodiments of this disclosure; Figure 3 This is a schematic diagram of the random cutting process of a three-dimensional sample and the resulting cross-section provided in the embodiments of this disclosure; Figure 4 This is a schematic diagram illustrating the cutting probability of particles of different sizes on a cross section, as provided in the embodiments of this disclosure. Figure 5 This is a schematic diagram of a method for randomly generating multiple three-dimensional samples provided in an embodiment of this disclosure; Figure 6 This is a schematic diagram of the particle number and volume frequency distribution and their differences in the same three-dimensional sample provided in the embodiments of this disclosure; Figure 7 This is a schematic diagram of a method for calculating the cross-sectional particle size parameters of each three-dimensional sample provided in an embodiment of this disclosure; Figure 8 This is a schematic diagram of the cross-sectional shape obtained by cutting particles of the same diameter at different positions according to an embodiment of this disclosure; Figure 9 This is a schematic diagram of the actual particle size frequency distribution and cross-sectional particle size frequency distribution of the same three-dimensional sample provided in the embodiments of this disclosure, and their differences; Figure 10 This is a fitting relationship between the actual number of average particle size and the cross-sectional number of average particle size provided in the embodiments of this disclosure; Figure 11 This is the fitting relationship between the actual volume average particle size and the cross-sectional volume average particle size provided in the embodiments of this disclosure; Figure 12 This is the fitting relationship between the actual quantity sorting coefficient and the cross-sectional quantity sorting coefficient provided in the embodiments of this disclosure; Figure 13 This is the fitting relationship between the actual volume sorting coefficient and the cross-sectional volume sorting coefficient provided in the embodiments of this disclosure; Figure 14 This is a schematic diagram of a correction device for particle size parameters obtained by cross-sectional static image analysis provided in an embodiment of this disclosure.

[0015] Figure label: 140. A calibration device for particle size parameters obtained by cross-sectional static image analysis; 141. Sample generation module; 142. Sample cutting module; 143. Relationship establishment module; 144. Parameter calibration module. Detailed Implementation

[0016] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0017] The terms "first," "second," etc., used in the specification and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0018] Unless otherwise stated, the term "multiple" means two or more.

[0019] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0020] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0021] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0022] Combination Figure 1 As shown, this disclosure provides a method for correcting particle size parameters obtained by cross-sectional static image analysis, including: S10: Randomly generate multiple three-dimensional samples and calculate the actual granularity parameters of each three-dimensional sample; S20, randomly cut each three-dimensional sample and calculate the cross-sectional particle size parameters of each three-dimensional sample; S30, Establish a mathematical relationship model between actual particle size parameters and cross-sectional particle size parameters; S40 uses a mathematical relationship model to correct the particle size parameters obtained by the cross-sectional static image analysis method.

[0023] like Figure 2 As shown, a three-dimensional sample containing multiple particles of different sizes is randomly generated by computer. The particles are not necessarily adjacent; they may or may not be adjacent. The particle shape includes, but is not limited to, circles. For ease of calculation, a circle is used uniformly in this embodiment. The three-dimensional sample in this embodiment is square with a side length of 1 meter and a volume of 1 cubic meter, but it can also be of other shapes and sizes.

[0024] After the 3D samples are randomly generated, the actual number and volume of particles are known. Based on the actual number or volume of particles, the actual particle size parameters of these 3D samples are calculated. The actual particle size parameters include: number-average particle size (Dc), volume-average particle size (Dv), number sorting coefficient (Fc), and volume sorting coefficient (Fv). These are calculated multiple times using both number-based and volume-based methods, for example, 10, 100, 1000, or 10000 times. The number-based method derives the number-average particle size and number sorting coefficient based on the number of particles, while the volume-based method derives the volume-average particle size and volume sorting coefficient based on the particle volume parameters. The actual average particle size and sorting coefficient calculated using the actually generated 3D samples differ from the one or more sets of average particle size and sorting coefficients originally generated to produce these 3D samples. Even when calculating samples using a single set of parameters, the actual average particle size and sorting coefficient differ from the originally randomly generated average particle size and sorting coefficient due to the rounding effect during the calculation process.

[0025] like Figure 3As shown, each 3D sample is cut multiple times, with the cut locations randomly generated, which can be anywhere on the particle diameter. Each cut generates a 2D cross-section. Figure 4 As shown, the probability of each particle being cut is positively correlated with its own particle size; that is, larger particles have a higher probability of being cut. After the cutting location is determined, the cross-sectional particle size parameters of each three-dimensional sample are calculated for each cutting section. The cross-sectional particle size parameters include: cross-sectional number average particle size, cross-sectional number sorting coefficient, cross-sectional area average particle size, cross-sectional area sorting coefficient, cross-sectional volume average particle size, and cross-sectional volume sorting coefficient.

[0026] In this way, multiple sets of corresponding datasets of actual particle size parameters and cross-sectional particle size parameters can be generated.

[0027] Using regression analysis, a mathematical relationship model between actual particle size parameters and cross-sectional particle size parameters is established based on the above dataset. Specifically, the relationship between the parameters of cross-sectional number average particle size, cross-sectional number sorting coefficient, cross-sectional area average particle size, cross-sectional area sorting coefficient, cross-sectional volume average particle size, and cross-sectional volume sorting coefficient and the actual number average particle size, actual number sorting coefficient, actual volume average particle size, and actual volume sorting coefficient is established.

[0028] After the mathematical relationship model is established, the particle size parameters obtained by the cross-sectional static image analysis method are corrected using the mathematical relationship model.

[0029] The method for correcting particle size parameters obtained by cross-sectional static image analysis, as provided in this disclosure, firstly, randomly generates three-dimensional samples containing particles of different sizes through computer forward modeling and statistically analyzes their actual particle size parameters. Secondly, the three-dimensional samples are randomly cut multiple times, and the cross-sectional particle size parameters obtained from each cut are calculated. Then, based on a large amount of forward modeling data, a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters is established using regression analysis. Finally, the particle size parameters obtained from the cross-sectional static image analysis of the sample to be tested are substituted into this mathematical relationship model to correct and obtain particle size parameters that are closer to its true three-dimensional distribution. This method can effectively improve the accuracy of particle size analysis of cross-sectional images of dense materials (such as rocks and metals).

[0030] Optionally, combined Figure 5 As shown, in S10, multiple three-dimensional samples are randomly generated, including: S11, randomly generate one or more sets of average particle size and sorting coefficients with a preset probability, and then determine the normal frequency distribution corresponding to each set and the total number of particles in different particle size ranges; S12, the corresponding three-dimensional sample consists of particles conforming to a single frequency distribution of a set of average particle size and sorting coefficients; or, it consists of a mixture of particles conforming to multiple sets of single frequency distributions of average particle size and sorting coefficients, and the overall average particle size and sorting coefficients of the particles are recalculated.

[0031] Multiple sets of average particle size and sorting coefficients are randomly generated with preset probabilities. For example, the first set of average particle size and sorting coefficients is generated with a 50% probability, and the corresponding three-dimensional sample consists of particles with a single frequency distribution that conforms to this set of parameters. The second set of average particle size and sorting coefficients is generated with a 50% probability, and the corresponding three-dimensional sample consists of a mixture of particles with single frequency distributions that conform to both sets of parameters (average particle size and sorting coefficients). The particle frequency distribution (including quantity frequency distribution and volume frequency distribution) is as follows: Figure 6 As shown, the particle size range is from 0 (1 mm) to 9 (1.95 μm). Particle size refers to the diameter of the particle, measured in Φ. This Φ unit is a logarithmic scale with respect to diameter, which effectively handles a wide range of particle sizes, making the representation of particle size more compact and intuitive. It is most commonly used in geology and materials science. The relationship between particle size Φ and actual particle size D (usually in millimeters) is as follows: .

[0032] The overall particle size distribution conforms to a preset distribution, such as a normal distribution with an actual quantity average particle size of 0 and an actual quantity sorting coefficient of 1, or a normal distribution with an actual volume average particle size of 2 and an actual volume sorting coefficient of 1.5. The quantity average particle size and volume average particle size refer to the average particle size obtained by using quantity parameters and volume parameters as statistical quantities, and the corresponding sorting coefficients are similarly calculated.

[0033] The average particle size and the sorting coefficient essentially correspond to the mean and variance of the normal frequency distribution function of particle size, respectively. Once the average particle size and the sorting coefficient are determined, their corresponding normal frequency distribution is also determined, and the total number of particles in different particle size ranges is also determined.

[0034] Optionally, the randomly generated average particle size ranges from -5 to 5, and the sorting coefficient ranges from 0.3 to 2.

[0035] Optionally, in S12, when multiple groups of particles conforming to a single frequency distribution are mixed, the mixing coefficient is generated randomly, taking a value between 0 and 1, and the sum of all mixing coefficients is 1. This setting is more in line with actual real-world conditions.

[0036] Optionally, combined Figure 7 As shown in Figure S20, the calculation of the cross-sectional grain size parameters for each three-dimensional sample includes: S21, determine the cutting position of the particles to be cut in each three-dimensional sample; S22, Determine the cutting section based on the cutting location; S23, calculate the cross-sectional grain size parameters of each three-dimensional sample on the cutting section.

[0037] like Figure 8 As shown, after determining the cutting position of the 3D sample, it is determined whether each particle has been cut and the specific cutting position of the cut particles. Based on the cutting position, the cutting cross-section can be determined, and then the cross-sectional particle size parameters of each 3D sample on the cutting cross-section can be calculated.

[0038] Calculate the particle size frequency distribution of the cross section: calculate the diameter and area of ​​all particles on the cutting cross section, as well as the volume of the sphere corresponding to the cutting diameter.

[0039] The particle size frequency distribution is obtained by statistically analyzing the number, total area, and total volume of particles on the cross-section according to preset diameter groups. For example, particle size ranges are statistically grouped at intervals of 0.1 Φ, such as 0.1 Φ-0.2 Φ, 0.2 Φ-0.3 Φ, and 0.3 Φ-0.4 Φ, etc. The number, total area, and total volume of particles within these ranges are calculated to obtain the particle size frequency distribution. The statistical range of particle size frequency distribution after cutting is 12.3 (0.20 micrometers) to -10 (1 meter), which is larger than the original range. This is because the diameter of the particles after cutting is smaller than their maximum diameter.

[0040] Calculate cross-sectional particle size parameters: Based on the cross-sectional particle size frequency distribution obtained in the above steps, use particle size parameter calculation methods (such as the moment method and graphical method) to calculate the number average particle size, number sorting coefficient, area average particle size, area sorting coefficient, volume average particle size, and volume sorting coefficient of the particles on the cutting cross-section. Figure 9 Showing Figure 6 The frequency distribution of the total number, area, and volume of particles in the cross-section (calculated assuming the cross-sectional diameter is the maximum diameter) is shown. The average particle size (Djc) based on the number of particles in the cross-section is 4.82 Φ, the average particle size (Djs) based on the area of ​​the particles in the cross-section is 3.86 Φ, and the average particle size (Djv) based on the volume of the particles in the cross-section is 3.27 Φ. The sorting factor (Fjc) based on the number of particles in the cross-section is 0.87, the sorting factor (Fjs) based on the area of ​​the particles in the cross-section is 0.86 Φ, and the sorting factor (Fjv) based on the volume of the particles in the cross-section is 0.98. Figure 6 The average particle size (Dc) obtained by the quantitative method is 1.27Φ, and the average particle size (Dv) obtained by the volumetric method is -0.67Φ. The sorting coefficients (Fc) obtained by both the quantitative and volumetric methods are 0.97. It can be seen that there are significant differences between the particle parameters obtained from the cross-sectional data and the actual particle parameters.

[0041] Calculate multiple times using both quantity and volume methods, such as 10, 100, 1000, or 10000 times, to obtain the number-average particle size, quantity sorting coefficient, area-average particle size, area sorting coefficient, volume-average particle size, and volume sorting coefficient of the particles on the cutting cross section.

[0042] Thus, through S10 and S20, a series of data can be obtained, including: actual quantity average particle size, actual quantity sorting coefficient, actual volume average particle size, actual volume sorting coefficient, cross-sectional quantity average particle size, cross-sectional quantity sorting coefficient, cross-sectional area average particle size, cross-sectional area sorting coefficient, cross-sectional volume average particle size, and cross-sectional volume sorting coefficient.

[0043] Combination Figures 10 to 13 As shown, it illustrates the fitting relationship between the actual particle size parameters obtained in step S10 and the cross-sectional particle size parameters obtained in step S20. Although according to... Figures 10 to 13 The relationships shown can be used to correct for cross-sectional grain size parameters, yielding parameters that better match actual results; however, the coefficients of determination in these fitting analyses are not high enough (R²). 2 (Between 0.54 and 0.88). Therefore, based on the large amount of data obtained above, multiple regression analysis was further used to analyze and process these data, and mathematical relationship models were established between actual particle size parameters (including number-average particle size, number sorting coefficient, volume-average particle size, and volume sorting coefficient) and cross-sectional particle size parameters (including cross-sectional number-average particle size, cross-sectional number sorting coefficient, cross-sectional area-average particle size, cross-sectional area sorting coefficient, cross-sectional volume-average particle size, and cross-sectional volume sorting coefficient): First mathematical relational model: Dyc = a1 × Djc + b1 × Fjc + c1 (R 2 > 0.95); Second mathematical relational model: Fyc = a² × Djc + b² × Fjc + c² (R 2 > 0.65); Third mathematical relational model: Dyv = a³ × Djv + b³ × Fjv + c³ (R 2 > 0.95); Fourth mathematical relational model: Fyv = a⁴ × Djv + b⁴ × Fjv + c⁴ (R 2 > 0.55); Fifth mathematical relational model: Dyv = a5 × Djs + b5 × Fjs + c5 (R 2 > 0.98); Sixth mathematical relational model: Fyv = a6 × Djs + b6 × Fjs + c6 (R 2 > 0.98); In the formula, Dyc is the actual number of average particle size, Fyc is the actual number of sorting coefficients, Dyv is the actual volume average particle size, and Fyv is the actual volume sorting coefficient; Djc is the cross-sectional number of average particle size, Fjc is the cross-sectional number of sorting coefficients, Djs is the cross-sectional area average particle size, Fjs is the cross-sectional area sorting coefficient, Djv is the cross-sectional volume average particle size, and Fjv is the cross-sectional volume sorting coefficient; a1, a2, a3, a4, a5, a6, b1, b2, b3, b4, b5, b6, c1, c2, c3, c4, c5, and c6 are all known coefficients. R 2 The coefficient of determination is 0, which represents the worst fit and 1 represents the best fit.

[0044] Optionally, the value ranges of the above coefficients are as follows: .

[0045] The average particle size calculated using these mathematical models shows a high degree of fit with the actual average particle size sequence, R0. 2 Generally higher than 0.9, especially the volume average particle size and volume sorting coefficient obtained using the fifth and sixth mathematical relation models, which are close to the actual results in terms of fit. 2 Approaching 1. Using the above mathematical relationship model, the particle size parameters obtained by the cross-sectional static image analysis method can be corrected, thereby obtaining particle size parameters that are more consistent with the actual situation.

[0046] When using quantitative parameters as statistical quantities, the particle size parameters obtained through cross-sectional static image analysis are corrected using the first and second mathematical relationship models. Specifically, Djc and Fjc are derived using cross-sectional static image analysis.

[0047] When volume parameters are used as statistical quantities, the particle size parameters obtained through cross-sectional static image analysis are corrected using one of the third and fifth mathematical relation models, and one of the fourth and sixth mathematical relation models. Specifically, Djs and Fjs are derived using cross-sectional static image analysis; Djv and Fjv are also derived using cross-sectional static image analysis.

[0048] Both the third and fifth mathematical relation models are used to calculate Dyv, but the fifth model yields more accurate results. Similarly, both the fourth and sixth mathematical relation models are used to calculate Fyv, but the sixth model again provides higher accuracy. Therefore, when using volume parameters as statistics, the fifth and sixth mathematical relation models are preferred for correcting particle size parameters.

[0049] Combination Figure 14As shown, this embodiment of the present disclosure provides a calibration device 140 for particle size parameters obtained by cross-sectional static image analysis, including: a sample generation module 141, a sample cutting module 142, a relationship establishment module 143, and a parameter calibration module 144. The sample generation module 141 is configured to randomly generate multiple three-dimensional samples and calculate the actual particle size parameters of each three-dimensional sample. The sample cutting module 142 is configured to randomly cut each three-dimensional sample and calculate the cross-sectional particle size parameters of each three-dimensional sample. The relationship establishment module 143 is configured to establish a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters. The parameter calibration module 144 is configured to use the mathematical relationship model to calibrate the particle size parameters obtained by cross-sectional static image analysis.

[0050] The particle size parameter correction device for cross-sectional static image analysis provided in this disclosure firstly generates three-dimensional samples containing particles of different sizes through computer forward modeling and statistically analyzes their actual particle size parameters. Secondly, the three-dimensional samples are randomly cut multiple times, and the cross-sectional particle size parameters obtained from each cut are calculated. Then, based on a large amount of forward modeling data, a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters is established using regression analysis. Finally, the particle size parameters obtained from the cross-sectional static image analysis of the sample to be tested are substituted into this mathematical relationship model to correct and obtain particle size parameters that are closer to its true three-dimensional distribution. This effectively improves the accuracy of particle size analysis of cross-sectional images of dense materials (such as rocks and metals).

[0051] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0052] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of additional identical elements in the process, method, or apparatus that includes said element. Throughout this document, each embodiment may focus on its differences from other embodiments, and similar or identical parts between embodiments may be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts may be referred to the description of the method section.

[0053] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0054] The methods and products disclosed in the embodiments herein (including but not limited to devices and equipment) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0055] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for correcting particle size parameters obtained by cross-sectional static image analysis, characterized in that, include: S10, randomly generate multiple three-dimensional samples and calculate the actual granularity parameters of each three-dimensional sample; S20, randomly cut each of the three-dimensional samples and calculate the cross-sectional grain size parameters of each of the three-dimensional samples; S30, Establish a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters; S40, The particle size parameters obtained by the cross-sectional static image analysis method are corrected using the mathematical relationship model.

2. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 1, characterized in that, In S10, the random generation of multiple three-dimensional samples includes: One or more sets of average particle size and sorting coefficients are randomly generated with a preset probability, thereby determining the normal frequency distribution corresponding to each set and the total number of particles in different particle size ranges; The corresponding three-dimensional sample consists of particles conforming to a single frequency distribution with a set of average particle size and sorting coefficients; or, it consists of a mixture of particles conforming to multiple sets of single frequency distributions with average particle size and sorting coefficients, with the overall average particle size and sorting coefficients recalculated.

3. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 2, characterized in that, When multiple groups of particles conforming to a single frequency distribution are mixed, the mixing coefficient is generated randomly, taking values ​​between 0 and 1, and the sum of all mixing coefficients is 1.

4. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 1, characterized in that, In S20, the random cutting of each of the three-dimensional samples includes: Each of the three-dimensional samples is cut multiple times at randomly selected cutting locations; Each cut generates a two-dimensional cutting section; The probability of each particle being cut is positively correlated with its particle size.

5. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 1, characterized in that, In S20, calculating the cross-sectional granularity parameters of each of the three-dimensional samples includes: Determine the cutting position of the particles to be cut in each of the three-dimensional samples; Determine the cutting cross-section based on the cutting position; Calculate the cross-sectional grain size parameters of each of the three-dimensional samples on the cutting section.

6. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 5, characterized in that, The calculation of the cross-sectional grain size parameters of each of the three-dimensional samples on the cutting section includes: Calculate the diameter and area of ​​each particle on the cut section, and the volume of the sphere corresponding to the cut diameter; The number, total area, and total volume of particles on the cutting section are statistically analyzed based on preset diameter groups. Calculate the cross-sectional particle size frequency distribution based on the number, total area, and total volume of the cut cross-section; Based on the particle size frequency distribution of the cross section, calculate the number average particle size, number sorting coefficient, area average particle size, area sorting coefficient, volume average particle size, and volume sorting coefficient of the particles on the cutting cross section.

7. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 1, characterized in that, S30 includes: Regression analysis was used to establish mathematical relationship models between the actual particle size parameters and the cross-sectional particle size parameters. The fit between the average particle size calculated by the mathematical relationship model and the actual average particle size sequence is analyzed, and the coefficient of determination is calculated.

8. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 7, characterized in that, The mathematical relationship model includes: First mathematical relational model: Dyc = a1 × Djc + b1 × Fjc + c1; Second mathematical relational model: Fyc = a² × Djc + b² × Fjc + c²; Third mathematical relational model: Dyv = a3 × Djv + b3 × Fjv + c3; Fourth mathematical relational model: Fyv = a4 × Djv + b4 × Fjv + c4; Fifth mathematical relational model: Dyv = a5 × Djs + b5 × Fjs + c5; Sixth mathematical relational model: Fyv = a6 × Djs + b6 × Fjs + c6; In the formula, Dyc is the actual number of average particle size, Fyc is the actual number of sorting coefficients, Dyv is the actual volume average particle size, Fyv is the actual volume sorting coefficient; Djc is the cross-sectional number of average particle size, Fjc is the cross-sectional number of sorting coefficients, Djs is the cross-sectional area average particle size, Fjs is the cross-sectional area sorting coefficient, Djv is the cross-sectional volume average particle size, Fjv is the cross-sectional volume sorting coefficient; a1 to a6, b1 to b6, and c1 to c6 are all known coefficients.

9. The method for correcting particle size parameters obtained by cross-sectional static image analysis according to claim 8, characterized in that, S40 includes: When the quantity parameter is used as the statistical quantity, the particle size parameter obtained by the cross-sectional static image analysis method is corrected using the first mathematical relationship model and the second mathematical relationship model; wherein, Djc and Fjc are obtained by the cross-sectional static image analysis method; When volume parameters are used as statistical quantities, the particle size parameters obtained by the cross-sectional static image analysis method are corrected using one of the third and fifth mathematical relationship models, as well as one of the fourth and sixth mathematical relationship models; wherein, Djs and Fjs are obtained using the cross-sectional static image analysis method; Djv and Fjv are obtained using the cross-sectional static image analysis method.

10. A correction device for particle size parameters obtained by cross-sectional static image analysis, characterized in that, include: The sample generation module is configured to randomly generate multiple three-dimensional samples and calculate the actual granularity parameters of each three-dimensional sample. The sample cutting module is configured to randomly cut each of the three-dimensional samples and calculate the cross-sectional granularity parameters of each of the three-dimensional samples. The relationship establishment module is configured to establish a mathematical relationship model between the actual particle size parameters and the cross-sectional particle size parameters; The parameter correction module is configured to use the mathematical relationship model to correct the particle size parameters obtained by the cross-sectional static image analysis method.

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