A method for detecting the sphericity of multi-size machine-made sand

Abstract

The present invention discloses a method for detecting the sphericity of machine-made sand with multiple particle sizes, which specifically relates to the technical field of building materials, and comprises S1, determining a detection area: for dividing the target machine-made sand to be detected into detection sub-areas according to the different maximum radii of the sand particles; S2, collecting information: for collecting data on the screened sand particles obtained in S1 to obtain sand particle characteristic data, and transmitting the data to a data processing step; the present invention judges the sand particles in multiple aspects, including the influence of particle size distribution, the influence of the narrowness and elongation of the particle shape, the influence of the flatness of the particle shape, and the influence of the sphericity of the particle shape, and can flexibly respond to machine-made sand samples with different particle sizes and shapes, and fully meet the detection requirements for the sphericity of machine-made sand with multiple particle sizes.

Description

Technical Field

[0001] The invention relates to the technical field of building materials, and more specifically to a method for detecting the sphericity of machine-made sand with multiple particle sizes. Background Art

[0002] In construction projects, the shape characteristics of machine-made sand have an important influence on the performance of concrete building materials. The particle shape of machine-made sand affects the fluidity, cohesion and water retention of the mixture. Therefore, developing a method for detecting the sphericity of multi-particle-size machine-made sand is of great significance for better controlling the quality of machine-made sand.

[0003] The traditional sand sphericity detection method includes sample preparation, standard screening and sphericity calculation. The sphericity calculation standardizes the artificial sand samples to eliminate moisture, large particle impurities and stone powder smaller than a certain particle size in the samples; the standard screening selects representative artificial sand samples, and passes them through a series of standard sieves. After the screening, the mass of each sieve residue is weighed respectively, and the mass of each sieve residue is weighed with an electronic balance. After the data is recorded in the original record, manual calculation is performed; the sphericity calculation calculates the sphericity of artificial sand samples of each particle size based on the data collected by the funnel emptying time measurement combined with a preset formula or model.

[0004] However, it still has some shortcomings in actual use, such as limited scope of application. Traditional methods often test artificial sand samples with specific particle sizes or shapes, and it is difficult to fully cover the sphericity detection needs of artificial sand with multiple particle sizes; limited measurement accuracy. The calculation method of traditional methods only performs well in measuring spherical particles. When measuring non-spherical particles or tiny particles, there may be large errors; low detection efficiency. Traditional methods use an electronic balance to weigh the mass of each sieve residue and perform manual calculations, but this method is prone to omissions or duplications, which increases the test time and reduces the accuracy of the test results and work efficiency.

[0005] Therefore, there is an urgent need to provide a method for detecting the sphericity of machine-made sand with multiple particle sizes to solve the problems of limited applicability, limited scope of application and low detection efficiency of existing sand sphericity detection methods. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for detecting the sphericity of multi-particle-size machine-made sand, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for detecting the sphericity of machine-made sand with multiple particle sizes, comprising: a running database, a central processing unit and a user information terminal, characterized in that it also includes a storage area determination step, an information collection step, a data processing step, a comprehensive data analysis step and a human-computer interaction step;

[0008] S1. Determine the detection area: It is used to divide the target machine-made sand to be detected into detection sub-areas according to the maximum radius of the sand particles;

[0009] S2, information collection: used to collect data on the sand particles screened in S1 to obtain sand particle characteristic data, and transmit the data to the data processing step;

[0010] S3, data processing: used to process the sand characteristic data obtained in S2 to obtain the particle size distribution influence coefficient, the particle shape narrowness influence coefficient, the particle shape flatness influence coefficient and the particle shape sphericity influence coefficient, and transmit them to the data analysis step;

[0011] S4, data analysis: used to import the particle size distribution influence coefficient value, particle shape narrowness influence coefficient value, particle shape flatness influence coefficient value and particle shape sphericity influence coefficient value obtained in S3 into the comprehensive sand grain sphericity index mathematical model, obtain the comprehensive sand grain sphericity index value, and transmit it to the sand grain sphericity judgment step;

[0012] S5, sphericity judgment: used to compare the comprehensive sand grain sphericity index value obtained in S4 with the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, calculate the difference between the comprehensive sand grain sphericity index value and the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, and when the difference is less than the preset difference, perform the human-computer interaction step on the judgment result;

[0013] S6, human-computer interaction, outputs information to the user information terminal based on the judgment result obtained in S5, is used to transmit the calculated difference value and sand data to the user information terminal, and provides reference data for alerting the administrator to make adjustment measures.

[0014] Preferably, the detection area is determined to obtain at least one detection range for the target detection system determined for the target detection area, the target machine-made sand to be detected is placed on the top screen of the automatic screening device, the device is started for screening, and the maximum radius of the sand particles is divided into various detection sub-areas according to the maximum aperture of the screening aperture;

[0015] The specific steps for determining the detection area are as follows:

[0016] a1. Pour the machine-made sand sample to be tested into a tray, put it into an oven, and dry it at 110°C until the weight remains unchanged to obtain a dried machine-made sand sample. This is to ensure that the moisture in the sample can be fully evaporated without destroying the properties of the machine-made sand sample;

[0017] a2. Then take out the dried machine-made sand sample obtained in a1, cool it to room temperature in a dryer, and obtain a dried machine-made sand sample. This step is to prevent errors in the sample due to temperature differences;

[0018] a3. The dried machine-made sand sample obtained in a2 is passed through a square hole sieve with a nominal diameter of 10.0 mm to remove particles with too large a particle size, and then the stone powder and fine particles smaller than 75 μm are removed by water washing; after water washing, the sample needs to be put into an oven again and dried at a temperature of 110°C until the weight remains unchanged, and then the sample is taken out of the oven again, placed in a dryer and naturally cooled to room temperature, and then cooled at room temperature for use, to obtain the processed machine-made sand sample;

[0019] a4. Select square hole sieves that meet the requirements of national standards. Select sieves with apertures of 4.75mm, 1.18mm, 0.630mm, and 0.315mm respectively, and combine them into a set of sieves from top to bottom. These square hole sieves are required by the national standards "Metal Wire Woven Mesh Test Sieve" (GB / T6003.1) and "Metal Perforated Plate Test Sieve" (GB / T 6003.2) to obtain a set of sieves;

[0020] a5. Pour the processed machine-made sand sample obtained in a3 into the top screen of the sieve set obtained in a4, fix the sieve set on a shaking screen machine for screening test, and screen for 10 minutes at a vibration frequency of 50 Hz. After the screening, collect the machine-made sand samples of each particle size for later use, and divide them into different detection sub-areas according to the maximum diameter of the screening aperture and the maximum radius of the sand particles.

[0021] Preferably, the sand particle characteristic data include parameters influencing particle size distribution, parameters influencing particle shape narrowness, parameters influencing particle shape flatness and parameters influencing particle shape sphericity.

[0022] Preferably, the parameters affecting the particle size distribution include the mass of the sand particles, denoted as m; the volume of the sand particles, denoted as V; the parameters affecting the flatness of the grains include the major diameter of the sand particles, denoted as D; the minor diameter of the sand particles, denoted as d; the circumference of the outer contour, denoted as C; the parameters affecting the flatness of the grains include the projected area of ​​the grains when viewed from above, denoted as S1; the projected area of ​​the grains when viewed from the left, denoted as S2; the projected area of ​​the grains when viewed from below, denoted as S3; the parameters affecting the sphericity of the grains include the major diameter of the sand particles, denoted as D; the minor diameter of the sand particles, denoted as d; the thickness of the sand particles, denoted as r; the surface area of ​​the sand particles, denoted as A; and the volume of the sand particles, denoted as V.

[0023] Preferably, the information collection uses a high-precision electronic scale to automatically measure and record the sieve residue mass, uses a high-resolution camera to take pictures of the machine-made sand samples of each particle size segment after screening, obtains clear particle images, inputs the particle images into image processing software, identifies the sand volume, sand surface area, sand major diameter, sand minor diameter, sand thickness parameters of the particles, and uses a three-view contour projection method to measure the perimeter of the sand outer contour line, the top view particle projection area, the left view particle projection area, and the bottom view particle projection area parameters;

[0024] The information collection step, the specific operation steps are as follows:

[0025] b1. Use a high-precision electronic scale to automatically measure and record the mass of the sieve residue after screening for each particle size segment obtained in step S1. This high-precision electronic scale can accurately measure the required minimum mass accuracy up to 0.01g, and then record the mass data of each particle size segment to obtain the mass of the sand particles;

[0026] b2. Use a high-resolution camera to take photos of the residual sand samples after screening in each particle size segment obtained in step S1. When taking photos, ensure that the light is sufficient and uniform to avoid the influence of shadows and reflections on the image quality and obtain clear particle images;

[0027] b3. Input the particle image obtained in b2 into the image processing software and use the threshold segmentation and edge detection algorithm to separate the particles from the background; use morphological operations to smooth and fill the particles to improve the image quality; use measurement tools to measure various characteristic parameters of individual particles, and number them according to different particles, marking them as 1, 2, ..., i, ..., n in sequence;

[0028] b4. In the image processing software, the volume and surface area of ​​the particles are calculated by three-dimensional reconstruction of the particle image and approximate calculation based on the two-dimensional image;

[0029] b5. In the image processing software, use the linear measurement tool to measure the long and short diameters of the particles;

[0030] b6. In the image processing software, the thickness is approximately estimated by measuring the projection height of the particles at different viewing angles;

[0031] b7. In the image processing software, use the three-view contour projection method to measure the outer contour circumference and projection area of ​​the sand particles. Rotate the particles to different viewing angles of top, left, and bottom and take images. Then, draw the outer contour of the particles in the image processing software and calculate the circumference. At the same time, measure the projection area of ​​the particles at each viewing angle.

[0032] Preferably, the data processing includes the steps of calculating the coefficient of influence of particle size distribution, the coefficient of influence of particle shape narrowness, the coefficient of influence of particle shape flatness and the coefficient of influence of particle shape sphericity.

[0033] Preferably, the particle size distribution influence coefficient calculation step is used to import the particle size distribution influence parameters into the particle size distribution influence coefficient mathematical model to obtain the particle size distribution influence coefficient value; the particle shape narrowness influence coefficient calculation step is used to import the particle shape narrowness influence parameters into the particle shape narrowness influence coefficient mathematical model to obtain the particle shape narrowness influence coefficient value; the particle shape flatness influence coefficient calculation step is used to import the particle shape flatness influence parameters into the particle shape flatness influence coefficient mathematical model to obtain the particle shape flatness influence coefficient value; the particle shape sphericity influence coefficient calculation step is used to import the particle shape sphericity influence parameters into the particle shape sphericity influence coefficient mathematical model to obtain the particle shape sphericity influence coefficient value.

[0034] Preferably, the particle size distribution influence coefficient mathematical model is specifically: , the mathematical model of the influence coefficient of particle shape narrowness is as follows: , the mathematical model of the particle shape flatness influence coefficient is as follows: , the mathematical model of particle shape sphericity influence coefficient is as follows: , where n represents the total number of machine-made sand particles in the detection sub-area, m represents the mass of sand particles, and V i represents the volume of the i-th sand grain, D i represents the major diameter of the i-th sand grain, d i represents the short diameter of the i-th sand grain, Ci represents the perimeter of the outer contour of the i-th sand grain, represents the top-view particle projection area of ​​the i-th sand grain, represents the left-view particle projection area of ​​the i-th sand grain, represents the downward projection area of ​​the i-th sand grain, r i represents the thickness of the i-th sand grain, denoted as r, A i represents the surface area of ​​the i-th sand grain.

[0035] Preferably, the comprehensive sand grain sphericity index mathematical model is specifically: , where α represents the particle size distribution influence coefficient value, β represents the particle size distribution influence coefficient value, It represents the influence coefficient of particle flatness, and γ represents the influence coefficient of particle sphericity.

[0036] Preferably, the operation database includes all data of the multi-particle size machine-made sand sphericity detection method, and collects information output by each step in real time. The central processor is used to control the information instructions output by each step, and the user information terminal is an information output device for receiving the multi-particle size machine-made sand sphericity detection method.

[0037] Technical effects and advantages of the present invention:

[0038] The invention can flexibly deal with machine-made sand samples of different particle sizes and shapes by judging the influence of particle size distribution, particle shape narrowness, particle shape flatness and particle shape sphericity on sand particles, and fully meet the detection requirements of sphericity of machine-made sand with multiple particle sizes.

[0039] The present invention utilizes efficient computer technology to provide accurate sphericity evaluation for spherical, non-spherical and tiny particles, significantly reducing errors and improving the precision of data.

[0040] The present invention uses automated equipment and software to quickly complete the weighing of an electronic balance, and the automatic recording and calculation of the sieve residue mass, thereby avoiding missed or repeated counting caused by human factors, greatly shortening the detection time, and improving the accuracy of the detection results and work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] As attached Figure 1 The method for detecting the sphericity of machine-made sand with multiple particle sizes shown includes an operation database, a central processing unit and a user information terminal, and is characterized in that it also includes a storage area determination step, an information collection step, a data processing step, a comprehensive data analysis step and a human-computer interaction step.

[0044] The operation database includes all data of the multi-particle size machine-made sand sphericity detection method, and collects information output from each step in real time. The central processor is used to control the information instructions output from each step. The user information terminal is an information output device for receiving the multi-particle size machine-made sand sphericity detection method.

[0045] S1. Determine the detection area, which is used to divide the target machine-made sand to be detected into detection sub-areas according to the maximum radius of the sand particles.

[0046] In this embodiment, it is specifically necessary to explain that the detection area determination step is used to obtain at least one detection range for the target detection system determined for the target detection area, place the target machine-made sand to be detected on the top screen of the automatic screening device, start the device for screening, and divide the maximum radius of the sand particles into different detection sub-areas according to the maximum aperture of the screening aperture.

[0047] In this embodiment, what needs to be explained specifically is the step of S1, determining the detection area, and the specific operation steps are as follows:

[0048] a1. Pour the machine-made sand sample to be tested into a tray, put it into an oven, and dry it at 110°C until the weight remains unchanged to obtain a dried machine-made sand sample. This is to ensure that the moisture in the sample can be fully evaporated without destroying the properties of the machine-made sand sample;

[0049] a2. Then take out the dried machine-made sand sample obtained in a1, cool it to room temperature in a dryer, and obtain a dried machine-made sand sample. This step is to prevent errors in the sample due to temperature differences;

[0050] a3. The dried machine-made sand sample obtained in a2 is passed through a square hole sieve with a nominal diameter of 10.0 mm to remove particles with too large a particle size, and then the stone powder and fine particles smaller than 75 μm are removed by water washing; after water washing, the sample needs to be put into an oven again and dried at a temperature of 110°C until the weight remains unchanged, and then the sample is taken out of the oven again, placed in a dryer and naturally cooled to room temperature, and then cooled at room temperature for use, to obtain the processed machine-made sand sample;

[0051] a4. Select square hole sieves that meet the requirements of national standards. Select sieves with apertures of 4.75mm, 1.18mm, 0.630mm, and 0.315mm respectively, and combine them into a set of sieves from top to bottom. These square hole sieves are required by the national standards "Metal Wire Woven Mesh Test Sieve" (GB / T6003.1) and "Metal Perforated Plate Test Sieve" (GB / T 6003.2) to obtain a set of sieves;

[0052] a5. Pour the processed machine-made sand sample obtained in a3 into the top screen of the sieve set obtained in a4, fix the sieve set on a shaking screen machine for screening test, and screen for 10 minutes at a vibration frequency of 50 Hz. After the screening, collect the machine-made sand samples of each particle size for later use, and divide them into different detection sub-areas according to the maximum diameter of the screening aperture and the maximum radius of the sand particles.

[0053] S2, information collection, is used to collect data on the sand particles screened in S1 to obtain sand particle characteristic data, and transmit it to the data processing step.

[0054] In this embodiment, it should be specifically explained that the sand particle characteristic data includes a parameter affecting particle size distribution, a parameter affecting particle shape narrowness, a parameter affecting particle shape flatness, and a parameter affecting particle shape sphericity.

[0055] In this embodiment, it is specifically necessary to explain that the parameters affecting the particle size distribution include the mass of the sand particles, denoted as m; the volume of the sand particles, denoted as V; the parameters affecting the degree of flatness of the grains include the major diameter of the sand particles, denoted as D; the minor diameter of the sand particles, denoted as d; the circumference of the outer contour, denoted as C; the parameters affecting the degree of flatness of the grains include the projected area of ​​the grains when viewed from above, denoted as S1; the projected area of ​​the grains when viewed from the left, denoted as S2; the projected area of ​​the grains when viewed from the bottom, denoted as S3; the parameters affecting the sphericity of the grains include the major diameter of the sand particles, denoted as D; the minor diameter of the sand particles, denoted as d; the thickness of the sand particles, denoted as r; the surface area of ​​the sand particles, denoted as A; and the volume of the sand particles, denoted as V.

[0056] In this embodiment, it is specifically necessary to explain that the information collection uses a high-precision electronic scale to automatically measure and record the mass of the sieve residue, uses a high-resolution camera to take pictures of the machine-made sand samples in each particle size segment after screening, obtains clear particle images, and inputs the particle images into the image processing software to identify the sand grain volume, sand grain surface area, sand grain major diameter, sand grain minor diameter, sand grain thickness parameters of the particles, and uses the three-view contour projection method to measure the circumference of the outer contour line of the sand grain, the top view particle projection area, the left view particle projection area, and the bottom view particle projection area parameters.

[0057] In this embodiment, what needs to be explained specifically is the step of S2, information collection, and the specific operation steps are as follows:

[0058] b1. Use a high-precision electronic scale to automatically measure and record the mass of the sieve residue after screening for each particle size segment obtained in step S1. This high-precision electronic scale can accurately measure the required minimum mass accuracy up to 0.01g, and then record the mass data of each particle size segment to obtain the mass of the sand particles;

[0059] b2. Use a high-resolution camera to take photos of the residual sand samples after screening in each particle size segment obtained in step S1. When taking photos, ensure that the light is sufficient and uniform to avoid the influence of shadows and reflections on the image quality and obtain clear particle images;

[0060] b3. Input the particle image obtained in b2 into the image processing software and use the threshold segmentation and edge detection algorithm to separate the particles from the background; use morphological operations to smooth and fill the particles to improve the image quality; use measurement tools to measure various characteristic parameters of individual particles, and number them according to different particles, marking them as 1, 2, ..., i, ..., n in sequence;

[0061] b4. In the image processing software, the volume and surface area of ​​the particles are calculated by three-dimensional reconstruction of the particle image and approximate calculation based on the two-dimensional image;

[0062] b5. In the image processing software, use the linear measurement tool to measure the long and short diameters of the particles;

[0063] b6. In the image processing software, the thickness is approximately estimated by measuring the projection height of the particles at different viewing angles;

[0064] b7. In the image processing software, use the three-view contour projection method to measure the outer contour circumference and projection area of ​​the sand particles. Rotate the particles to different viewing angles of top, left, and bottom and take images. Then, draw the outer contour of the particles in the image processing software and calculate the circumference. At the same time, measure the projection area of ​​the particles at each viewing angle.

[0065] S3, data processing, is used to process the sand particle characteristic data obtained in S2 to obtain the particle size distribution influence coefficient, the particle shape narrowness influence coefficient, the particle shape flatness influence coefficient and the particle shape sphericity influence coefficient, and transmit them to the data analysis step.

[0066] In this embodiment, it should be specifically explained that the data processing includes the steps of calculating the coefficient of influence of particle size distribution, the coefficient of influence of particle elongation, the coefficient of influence of particle flatness, and the coefficient of influence of particle sphericity.

[0067] In this embodiment, it is specifically necessary to explain that the particle size distribution influence coefficient calculation step is used to import the particle size distribution influence parameter into the particle size distribution influence coefficient mathematical model to obtain the particle size distribution influence coefficient value; the particle shape narrowness influence coefficient calculation step is used to import the particle shape narrowness influence parameter into the particle shape narrowness influence coefficient mathematical model to obtain the particle shape narrowness influence coefficient value; the particle shape flatness influence coefficient calculation step is used to import the particle shape flatness influence parameter into the particle shape flatness influence coefficient mathematical model to obtain the particle shape flatness influence coefficient value; the particle shape sphericity influence coefficient calculation step is used to import the particle shape sphericity influence parameter into the particle shape sphericity influence coefficient mathematical model to obtain the particle shape sphericity influence coefficient value.

[0068] In this embodiment, it should be specifically noted that the mathematical model of the particle size distribution influence coefficient is: , the mathematical model of the influence coefficient of particle shape narrowness is as follows: , the mathematical model of the particle shape flatness influence coefficient is as follows: , the mathematical model of particle shape sphericity influence coefficient is as follows: , where n represents the total number of machine-made sand particles in the detection sub-area, m represents the mass of sand particles, and V i represents the volume of the i-th sand grain, D i represents the major diameter of the i-th sand grain, d i represents the short diameter of the i-th sand grain, Ci represents the perimeter of the outer contour of the i-th sand grain, represents the top-view particle projection area of ​​the i-th sand grain, represents the left-view particle projection area of ​​the i-th sand grain, represents the downward projection area of ​​the i-th sand grain, r i represents the thickness of the i-th sand grain, denoted as r, A i represents the surface area of ​​the i-th sand grain.

[0069] S4, data analysis, is used to import the particle size distribution influence coefficient value, particle shape narrowness influence coefficient value, particle shape flatness influence coefficient value and particle shape sphericity influence coefficient value obtained in S3 into the comprehensive sand grain sphericity index mathematical model, obtain the comprehensive sand grain sphericity index value, and transmit it to the sand grain sphericity judgment step.

[0070] In this embodiment, it should be specifically explained that the mathematical model of the comprehensive sand grain sphericity index is specifically: , where α represents the particle size distribution influence coefficient value, β represents the particle size distribution influence coefficient value, It represents the influence coefficient of particle flatness, and γ represents the influence coefficient of particle sphericity.

[0071] S5, sphericity judgment, is used to compare the comprehensive sand grain sphericity index value obtained in S4 with the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, calculate the difference between the comprehensive sand grain sphericity index value and the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, and when the difference is less than the preset difference, the judgment result is subjected to a human-computer interaction step.

[0072] In this embodiment, it is specifically necessary to explain that the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operating database is the average value of the comprehensive sand grain sphericity index values ​​of the same historical sand grain maximum radius, which removes the maximum and minimum values, where the maximum and minimum values ​​are extreme data values; the preset difference value is the minimum value of the difference between the historical comprehensive sand grain sphericity index value and the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operating database.

[0073] S6, human-computer interaction, outputs information to the user information terminal based on the judgment result obtained in S5, is used to transmit the calculated difference value and sand data to the user information terminal, and provides reference data for alerting the administrator to make adjustment measures.

[0074] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0075] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the sphericity of multi-size machine-made sand, comprising an operation database, a central processing unit and a user information terminal, characterized in that: It also includes a storage area determination step, an information collection step, a data processing step, a comprehensive data analysis step, and a human-computer interaction step; S1. Determine the detection area: It is used to divide the target machine-made sand to be detected into detection sub-areas according to the maximum radius of the sand particles; S2, information collection: used to collect data on the sand particles screened in S1 to obtain sand particle characteristic data, and transmit the data to the data processing step; The sand grain characteristic data include parameters affecting particle size distribution, parameters affecting particle shape narrowness, parameters affecting particle shape flatness, and parameters affecting particle shape sphericity; The parameters affecting the particle size distribution include the mass of the sand grains, recorded as m; the volume of the sand grains, recorded as V; the parameters affecting the flatness of the grains include the major diameter of the sand grains, recorded as D; the minor diameter of the sand grains, recorded as d; the perimeter of the outer contour, recorded as C; the parameters affecting the flatness of the grains include the top view particle projection area, recorded as S1; the left view particle projection area, recorded as S2; the bottom view particle projection area, recorded as S3; the parameters affecting the sphericity of the grains include the major diameter of the sand grains, recorded as D; the minor diameter of the sand grains, recorded as d; the thickness of the sand grains, recorded as r; the surface area of ​​the sand grains, recorded as A; and the volume of the sand grains, recorded as V; The information collection uses a high-precision electronic scale to automatically measure and record the sieve residue mass, uses a high-resolution camera to take pictures of the machine-made sand samples of each particle size segment after screening, obtains clear particle images, inputs the particle images into the image processing software, identifies the sand volume, sand surface area, sand long diameter, sand short diameter, sand thickness parameters of the particles, and uses the three-view contour projection method to measure the perimeter of the sand outer contour line, the top view particle projection area, the left view particle projection area, and the bottom view particle projection area parameters; Information collection steps, the specific steps are as follows: b1. Use a high-precision electronic scale to automatically measure and record the mass of the sieve residue after screening for each particle size segment obtained in step S1. This high-precision electronic scale can accurately measure the required minimum mass accuracy up to 0.01g, and then record the mass data of each particle size segment to obtain the mass of the sand particles; b2. Use a high-resolution camera to take photos of the residual sand samples after screening in each particle size segment obtained in step S1. When taking photos, ensure that the light is sufficient and uniform to avoid the influence of shadows and reflections on the image quality and obtain clear particle images; b3. Input the particle image obtained in b2 into the image processing software and use the threshold segmentation and edge detection algorithm to separate the particles from the background; use morphological operations to smooth and fill the particles to improve the image quality; use measurement tools to measure various characteristic parameters of individual particles, and number them according to different particles, marking them as 1, 2, ..., i, ..., n in sequence; b4. In the image processing software, the volume and surface area of ​​the particles are calculated by three-dimensional reconstruction of the particle image and approximate calculation based on the two-dimensional image; b5. In the image processing software, use the linear measurement tool to measure the long and short diameters of the particles; b6. In the image processing software, the thickness is approximately estimated by measuring the projection height of the particles at different viewing angles; b7. In the image processing software, the three-view contour projection method is used to measure the outer contour circumference and projection area of ​​the sand particles. The particles are rotated to different viewing angles of top, left, and bottom and images are taken; then, the outer contour of the particles is drawn in the image processing software and the circumference is calculated; at the same time, the projection area of ​​the particles at each viewing angle is measured; S3, data processing: used to process the sand characteristic data obtained in S2 to obtain the particle size distribution influence coefficient, the particle shape narrowness influence coefficient, the particle shape flatness influence coefficient and the particle shape sphericity influence coefficient, and transmit them to the data analysis step; The data processing includes the steps of calculating the coefficient of influence of particle size distribution, the coefficient of influence of particle shape narrowness, the coefficient of influence of particle shape flatness and the coefficient of influence of particle shape sphericity; The step of calculating the coefficient of influence of particle size distribution is used to import the parameters of particle size distribution influence into the mathematical model of particle size distribution influence coefficient, and obtain the value of particle size distribution influence coefficient; the step of calculating the coefficient of influence of particle shape narrowness and length is used to import the parameters of particle shape narrowness and length into the mathematical model of particle shape narrowness and length influence coefficient, and obtain the value of particle shape narrowness and length influence coefficient; the step of calculating the coefficient of influence of particle shape flatness is used to import the parameters of particle shape flatness influence into the mathematical model of particle shape flatness influence coefficient, and obtain the value of particle shape flatness influence coefficient; the step of calculating the coefficient of influence of particle shape sphericity influence is used to import the parameters of particle shape sphericity influence into the mathematical model of particle shape sphericity influence coefficient, and obtain the value of particle shape sphericity influence coefficient; The particle size distribution influence coefficient mathematical model is specifically: , the mathematical model of the influence coefficient of particle shape narrowness is as follows: , the mathematical model of the particle shape flatness influence coefficient is as follows: , the mathematical model of particle shape sphericity influence coefficient is as follows: , where n represents the total number of machine-made sand particles in the detection sub-area, m represents the mass of sand particles, and V i represents the volume of the i-th sand grain, D i represents the major diameter of the i-th sand grain, d i represents the short diameter of the i-th sand grain, Ci represents the perimeter of the outer contour of the i-th sand grain, represents the top-view particle projection area of ​​the i-th sand grain, represents the left-view particle projection area of ​​the i-th sand grain, represents the downward projection area of ​​the i-th sand grain, r i represents the thickness of the i-th sand grain, denoted as r, A i represents the surface area of ​​the i-th sand grain; S4, data analysis: used to import the particle size distribution influence coefficient value, particle shape narrowness influence coefficient value, particle shape flatness influence coefficient value and particle shape sphericity influence coefficient value obtained in S3 into the comprehensive sand grain sphericity index mathematical model, obtain the comprehensive sand grain sphericity index value, and transmit it to the sand grain sphericity judgment step; The mathematical model of the comprehensive sand grain sphericity index is specifically: , where α represents the particle size distribution influence coefficient value, β represents the particle size distribution influence coefficient value, represents the influence coefficient of particle flatness, and γ represents the influence coefficient of particle sphericity; S5, sphericity judgment: used to compare the comprehensive sand grain sphericity index value obtained in S4 with the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, calculate the difference between the comprehensive sand grain sphericity index value and the preset comprehensive sand grain sphericity index value of the same sand grain maximum radius in the operation database, and when the difference is less than the preset difference, perform the human-computer interaction step on the judgment result; S6, human-computer interaction: Based on the judgment result obtained in S5, information is output to the user information terminal to transmit the calculated difference value and sand data to the user information terminal, and provide reference data for alerting the administrator to make adjustment measures.

2. The method for detecting sphericity of multi-size machine-made sand according to claim 1, characterized in that: The detection area is determined to obtain at least one detection range for the target detection system determined for the target detection area, the target machine-made sand to be detected is placed on the top screen of the automatic screening device, the device is started for screening, and the maximum radius of the sand particles is divided into various detection sub-areas according to the maximum aperture of the screening aperture.

3. The method for detecting sphericity of multi-size machine-made sand according to claim 1, characterized in that: The operation database includes all data of the multi-particle size machine-made sand sphericity detection method, and collects information output from each step in real time. The central processor is used to control the information instructions output from each step. The user information terminal is an information output device for receiving the multi-particle size machine-made sand sphericity detection method.

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