Calcine quality evaluation method and device, computer equipment and computer storage medium

Abstract

The invention discloses a calcine quality evaluation method and device, computer equipment and a computer storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring the roasted sand sulfur content in roasted sand to be detected, and calculating the sulfur volatilization rate according to the roasted sand sulfur content; acquiring a sample image corresponding to the roasted product to be detected, and calibrating all ferric oxide particles for the sample image; according to the ferric oxide particle calibration result of the sample image, obtaining the ferric oxide particle porosity and the ferric oxide particle aperture parameter; on the basis of a stepwise regression method, the leaching rate of the roasted sand to be detected is calculated according to the sulfur volatilization rate, the porosity of the ferric oxide particles and the pore diameter parameters of the ferric oxide particles; and determining a quality evaluation result of the roasted product to be detected according to the leaching rate and a preset evaluation threshold value. Therefore, on the basis of offline detection, the leaching rate is predicted through multi-dimensional parameters, and a detection-analysis-prediction whole-process system is constructed to evaluate the quality of the calcine, so that the evaluation efficiency is improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a roasted sand quality evaluation method, device, computer equipment and computer storage medium. Background Art

[0002] Currently, roasted sand quality is evaluated by measuring porosity using mercury intrusion porosimetry (MIP) or scanning electron microscopy (SEM). Mercury intrusion porosity is calculated based on the volume of mercury intruded into pores, but it suffers from low accuracy for micropore detection and requires expensive equipment. Traditional SEM image analysis relies on manual threshold segmentation, which is time-consuming and results in large uniformity quantification errors, making it ineffective for quickly and effectively evaluating roasted sand quality. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and provide a sand quality evaluation method, device, computer equipment and computer storage medium for predicting the leaching rate through offline detection and then evaluating the quality of roasted sand.

[0004] The present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for evaluating the quality of roasted sand, comprising:

[0006] Obtaining the calcined sulfur content in the calcined sand to be tested, and calculating the sulfur volatilization rate based on the calcined sulfur content;

[0007] Obtaining a sample image corresponding to the calcined sand to be tested, and calibrating all ferric oxide particles in the sample image;

[0008] Obtaining the porosity and pore size parameters of the ferric oxide particles according to the ferric oxide particle calibration result of the sample image;

[0009] Based on the stepwise regression method, the leaching rate of the calcined sand to be tested is calculated according to the sulfur volatilization rate, the porosity of the ferric oxide particles and the pore size parameters of the ferric oxide particles;

[0010] The quality evaluation result of the roasted sand to be tested is determined according to the leaching rate and a preset evaluation threshold.

[0011] In one embodiment, the step of obtaining the calcined sulfur content in the calcined sand to be tested and calculating the sulfur volatilization rate based on the calcined sulfur content includes:

[0012] Obtaining the original sulfur content of the calcined sand to be tested before roasting, and calculating the sulfur content difference between the original sulfur content and the calcined sand sulfur content;

[0013] The ratio of the sulfur content difference to the original sulfur content is calculated, and the ratio is used as the sulfur volatilization rate.

[0014] In one embodiment, obtaining the porosity of the ferric oxide particles according to the calibration result of the ferric oxide particles in the sample image includes:

[0015] Obtaining the area of each ferric oxide particle and the area of the pores between each ferric oxide particle according to the ferric oxide particle calibration result of the sample image;

[0016] The porosity of the ferric oxide particles is obtained according to the quotient of the pore area and the region area.

[0017] In one embodiment, the pore size parameters of the iron oxide particles include an average pore size and pore size uniformity. The pore size parameters of the iron oxide particles are obtained according to the calibration results of the iron oxide particles in the sample image, including:

[0018] Obtain a binary image according to the sample image;

[0019] Performing pore size distribution statistics on the binary image based on a preset pore identification threshold to obtain a plurality of pore size values;

[0020] The average pore size and the pore size uniformity are calculated based on the pore size values.

[0021] In one embodiment, calculating the pore size uniformity according to each of the pore size values comprises:

[0022] determining a first target pore size from each of the pore size values based on a first predetermined pore size distribution range;

[0023] determining a second target pore size from each of the pore size values based on a second predetermined pore size distribution range;

[0024] The quotient of the first target aperture and the second target aperture is calculated to obtain the aperture uniformity.

[0025] In one embodiment, the stepwise regression method is used to calculate the leaching rate of the calcined sand to be tested according to the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles, including:

[0026] Calculating the product of a first preset coefficient and the sulfur volatilization rate to obtain a first product;

[0027] Calculating the product of the second preset coefficient and the porosity of the ferric oxide particles to obtain a second product;

[0028] Calculating the product of a third preset coefficient and the average pore size to obtain a third product;

[0029] Calculating a product of a fourth preset coefficient and the pore uniformity to obtain a fourth product;

[0030] The sum of the fifth preset coefficient, the first product, the second product and the third product is calculated, and the difference between the sum and the fourth product is calculated to obtain the leaching rate.

[0031] In one embodiment, obtaining a sample image corresponding to the roasted sand to be detected includes:

[0032] Obtaining a roasted sand sample of the roasted sand to be tested according to preset sample parameters;

[0033] A sample image corresponding to the roasted sand sample is obtained based on preset acquisition parameters.

[0034] In a second aspect, the present invention provides a roasted sand quality evaluation device, comprising:

[0035] A first acquisition module is used to obtain the roasted sand sulfur content in the roasted sand to be detected, and calculate the sulfur volatilization rate according to the roasted sand sulfur content;

[0036] a calibration module, configured to obtain a sample image corresponding to the calcined sand to be detected, and calibrate all ferric oxide particles from the sample image;

[0037] A second acquisition module is used to obtain the porosity and pore size parameters of the ferric oxide particles according to the calibration result of the ferric oxide particles in the sample image;

[0038] a calculation module, configured to calculate the leaching rate of the calcine to be tested according to the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles based on a stepwise regression method;

[0039] A determination module is used to determine the quality evaluation result of the roasted sand to be tested according to the leaching rate and a preset evaluation threshold.

[0040] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the roasted sand quality evaluation method as described in the first aspect is implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the roasted sand quality evaluation method as described in the first aspect.

[0042] The present invention discloses a method, device, computer equipment, and computer storage medium for evaluating the quality of roasted sand. The method comprises the following steps: obtaining the roasted sand sulfur content in the roasted sand to be tested, calculating the sulfur volatility rate based on the roasted sand sulfur content; obtaining a sample image corresponding to the roasted sand to be tested, and calibrating all ferric oxide particles in the sample image; obtaining the porosity and pore size parameters of the ferric oxide particles based on the ferric oxide particle calibration results of the sample image; calculating the leaching rate of the roasted sand to be tested based on the sulfur volatility rate, the porosity, and the pore size parameters of the ferric oxide particles based on a stepwise regression method; and determining the quality evaluation result of the roasted sand to be tested based on the leaching rate and a preset evaluation threshold. In this way, based on offline detection, the leaching rate is predicted by the sulfur volatility rate, the ferric oxide porosity, and the pore size parameters, and a "detection-analysis-prediction" full-process system is constructed to evaluate the quality of roasted sand, thereby improving evaluation efficiency and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0044] Figure 1 A schematic flow chart of the method for evaluating the quality of calcined sand proposed in this embodiment is shown;

[0045] Figure 2 Another schematic flow chart of the method for evaluating the quality of calcined sand proposed in this embodiment is shown;

[0046] Figure 3 Another schematic flow chart of the method for evaluating the quality of calcined sand proposed in this embodiment is shown;

[0047] Figure 4 Another schematic flow chart of the method for evaluating the quality of calcined sand proposed in this embodiment is shown;

[0048] Figure 5 Another schematic flow chart of the method for evaluating the quality of calcined sand proposed in this embodiment is shown;

[0049] Figure 6 A schematic structural diagram of the roasted sand quality evaluation device proposed in this embodiment is shown.

[0050] Description of the accompanying drawings:

[0051] 600 - roasted sand quality evaluation device; 601 - first acquisition module; 602 - calibration module; 603 - second acquisition module; 604 - calculation module; 605 - determination module. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the figures is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by those skilled in the art based on the embodiments of the present invention without inventive effort are intended to be within the scope of protection of the present invention.

[0054] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0055] Furthermore, the terms “first,” “second,” “third,” etc., are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0056] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present invention pertain. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present invention.

[0057] Example 1

[0058] The embodiments of the present disclosure provide a method for evaluating the quality of roasted sand, which is used to predict the leaching rate through offline detection and then evaluate the quality of roasted sand.

[0059] See Figure 1 A method for evaluating the quality of roasted sand includes steps S101 to S105, and each step is described in detail below.

[0060] Step S101: obtaining the roasted sand sulfur content in the roasted sand to be tested, and calculating the sulfur volatilization rate according to the roasted sand sulfur content.

[0061] In this example, the calcined sulfur content refers to the amount of sulfur contained in the calcined material after the calcination process. The sulfur content of the calcined material is measured, and the sulfur volatility is calculated based on the calcined sulfur content. The sulfur volatility directly quantifies the degree of sulfur removal during the calcination process, reflecting the process's ability to remove sulfur from the raw material.

[0062] See Figure 2 In a specific embodiment, step S101 includes steps S1011 to S1012, and each step is described in detail below.

[0063] Step S1011, obtaining the original sulfur content of the roasted sand to be tested before roasting, and calculating the sulfur content difference between the original sulfur content and the roasted sand sulfur content.

[0064] In this embodiment, the original sulfur content refers to the amount of sulfur contained in the material before undergoing treatment such as roasting. In practice, the sulfur content of the original material can be determined using chemical analysis methods such as gravimetric analysis, titration, and spectroscopic analysis. For example, an ore sample can be dissolved in an appropriate solvent, and the concentration of sulfur ions in the solution can be measured by titration to calculate the original sulfur content.

[0065] Furthermore, the sulfur content difference between the original sulfur content and the roasted sulfur content is calculated, and the sulfur content difference reflects the loss of sulfur element during the roasting process.

[0066] Step S1012: Calculate the ratio of the sulfur content difference to the original sulfur content, and use the ratio as the sulfur volatilization rate.

[0067] In this embodiment, the sulfur volatilization rate S 挥发率 It is an important indicator to measure the degree of sulfur volatilization during the roasting process. Its calculation formula is: S 挥发率 =(m 0S -m 1S ) / m 0S ×100%, where m 0S is the original sulfur content, m 1S is the sulfur content of roasted sand.

[0068] Step S102: obtaining a sample image corresponding to the calcined sand to be detected, and calibrating all ferric oxide particles in the sample image.

[0069] In this embodiment, a sample image corresponding to the calcined sand to be tested is obtained, and all ferric oxide particles in the sample image are calibrated. For example, the sample image can be imported into the ImagePro Plus image processing program, and a series of images can be batch processed according to a script.

[0070] See Figure 3In a specific embodiment, step S102 includes steps S1021 to S1022, and each step is described in detail below.

[0071] Step S1021: obtaining a roasted sand sample of the roasted sand to be tested according to preset sample parameters.

[0072] In this embodiment, the preset sample parameters include a sample type parameter and a processing parameter. The sample type parameter may be an epoxy resin type, and the processing parameter may be a carbon spraying treatment. That is, the calcined sand to be tested is made into an epoxy resin sample, carbon sprayed, and then placed on the sample stage of a Zeiss scanning electron microscope (SEM).

[0073] Step S1022: Acquire a sample image corresponding to the roasted sand sample based on preset acquisition parameters.

[0074] In this embodiment, the preset acquisition parameters include an acceleration voltage parameter, a working distance parameter, and a magnification parameter. For example, a backscattered electron image (BSE) of the ferric oxide region is acquired at an acceleration voltage of 20 kV, a working distance of 8 mm, and a magnification of 500× to obtain a sample image.

[0075] Step S103 , obtaining the porosity and pore size parameters of the ferric oxide particles according to the ferric oxide particle calibration result of the sample image.

[0076] In this example, the porosity and pore size parameters of the iron oxide particles are obtained based on the calibration results of the iron oxide particles in the sample image. Porosity is an important indicator affecting the density of the material, directly affecting the strength, hardness, impermeability, frost resistance, water absorption, and other properties of the calcined sand. The pore size parameters, especially the particle size and pore size distribution, have a significant impact on the specific surface area and reactivity of the calcined sand.

[0077] See Figure 4 In a specific embodiment, step S103 includes steps S1031 to S1032, and each step is described in detail below.

[0078] Step S1031 : obtaining the area of each ferric oxide particle and the area of the pores between each ferric oxide particle according to the ferric oxide particle calibration result of the sample image.

[0079] In this embodiment, the region area of each ferric oxide particle and the pore area between each ferric oxide particle are obtained in the sample image after the ferric oxide particles are calibrated, and the total region area is calculated based on all the region areas, and the total pore area is calculated based on all the pore areas.

[0080] Step S1032: obtaining the porosity of the ferric oxide particles according to the quotient of the pore area and the region area.

[0081] In this embodiment, the porosity is the percentage of the pore volume inside the material to the total volume. pore With the total area A FeO The quotient of is taken as the porosity P of the ferric oxide particles. The calculation expression of the porosity P of the ferric oxide particles is: P = A pore / A Fe O ×100%.

[0082] See Figure 5 In a specific embodiment, the pore size parameters of the ferric oxide particles include an average pore size and pore size uniformity. Step S103 includes steps S501 to S503. Each step is described in detail below.

[0083] Step S501: obtaining a binary image according to the sample image.

[0084] In this embodiment, the sample image is binarized to obtain a binary image.

[0085] Step S502 : performing pore size distribution statistics on the binary image based on a preset pore identification threshold to obtain a plurality of pore size values.

[0086] In this example, the binary image was imported into ImagePro Plus software, and pore size distribution statistics were performed on the binary image based on a preset pore identification threshold to obtain the pore size values of multiple ferric oxide particles. The preset gap identification threshold can be used to ignore gaps smaller than the threshold to avoid the influence of low noise.

[0087] Step S503: Calculate the average pore size and the pore size uniformity according to the pore size values.

[0088] In this example, the average pore diameter Davg and pore uniformity U are calculated based on the individual pore diameter values. These values provide quantitative physical structural indicators of calcined sand quality, complementing the shortcomings of traditional chemical analysis methods and providing more comprehensive evaluation results. Uniformity U reflects the degree of dispersion in the pore size distribution; smaller U indicates more concentrated pore sizes, which is more conducive to gold particle exposure.

[0089] In a specific embodiment, step S503 includes: determining a first target aperture from each of the aperture values based on a first preset aperture distribution range; determining a second target aperture from each of the aperture values based on a second preset aperture distribution range; and calculating a quotient of the first target aperture and the second target aperture to obtain the aperture uniformity.

[0090] In this embodiment, a first target aperture is determined from each aperture value based on a first predetermined aperture distribution range, such that all aperture values within the first predetermined aperture distribution range are smaller than the first target aperture. For example, if the first predetermined aperture distribution range is 90%, then 90% of the aperture values in the distribution of all aperture values are smaller than the first target aperture.

[0091] Furthermore, a second target aperture size is determined from each aperture value based on a second predetermined aperture distribution range, such that all aperture values within the second predetermined aperture distribution range are smaller than the second target aperture size. For example, if the second predetermined aperture distribution range is 10%, then 10% of the aperture values in the distribution of all aperture values are smaller than the second target aperture size.

[0092] Furthermore, the first target aperture D 90 With the second target aperture D 10 The quotient of is taken as the pore uniformity U. The expression is U=D 90 / D 10 .

[0093] Step S104 , based on a stepwise regression method, the leaching rate of the calcined sand to be tested is calculated according to the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles.

[0094] In this embodiment, based on the stepwise regression method, microstructural indicators such as sulfur volatilization rate, porosity of ferric oxide particles, and pore size parameters of ferric oxide particles are incorporated into the model to calculate the leaching rate, providing multi-dimensional data support for the evaluation of roasted sand quality.

[0095] It should be noted that in practical applications, it is recommended to prioritize controlling the sulfur volatilization rate to above 97%, the porosity to over 50%, and optimizing the roasting process to achieve a uniform pore size distribution. The corresponding relationship between pore size uniformity U and the expected range of leaching rate is shown in Table 1.

[0096] Table 1:

[0097] Uniformity Index Pore size distribution characteristics Expected range of leaching rates U≤3 Concentrated distribution (e.g. 2-5μm accounts for >80%) ≥80％ 3<U≤5 Relatively dispersed (process optimization required) 70％-80％ U>5 Discrete distribution (need to be re-baked) <70％

[0098] In a specific embodiment, step S104 includes: calculating the product of a first preset coefficient and the sulfur volatilization rate to obtain a first product; calculating the product of a second preset coefficient and the porosity of the ferric oxide particles to obtain a second product; calculating the product of a third preset coefficient and the average pore size to obtain a third product; calculating the product of a fourth preset coefficient and the pore size uniformity to obtain a fourth product; calculating the sum of a fifth preset coefficient, the first product, the second product and the third product, and calculating the difference between the sum and the fourth product to obtain the leaching rate.

[0099] In this example, sulfur volatility and porosity are the main factors affecting the leaching rate, and the pore size parameter plays a synergistic role by affecting the mass transfer process. Therefore, the calculation expression of the leaching rate R is: R = K5 + K1 × S + K2 × P + K3 × Davg - K4 × U, where K1 ranges from 1 to 5, K2 ranges from 3 to 5, K3 ranges from 0 to 1, K4 ranges from 0 to 1, and K5 ranges from 1 to 5. Through multi-parameter coupling and dynamic adjustment, the accuracy, reliability, and practicality of the leaching rate prediction are significantly improved. In addition, the preset coefficients allow the parameter weights to be adjusted according to different process conditions (such as temperature and pressure) to ensure the adaptability of the model.

[0100] It should be noted that the prediction results of the leaching rate in this embodiment are shown in Table 2. As can be seen from Table 2, according to the constructed model, the predicted gold leaching rate data is relatively close to the actual gold leaching rate data, with a small error.

[0101] Table 2:

[0102]

[0103] Step S105: determining a quality evaluation result of the calcine to be tested according to the leaching rate and a preset evaluation threshold.

[0104] In this embodiment, if the leaching rate is less than a preset evaluation threshold, the quality evaluation result of the calcined sand to be tested is determined to be low quality, and the roasting temperature and roasting time need to be adjusted according to relevant parameters for optimization. This shortens the optimization cycle for a single batch from 3 days to 8 hours, eliminates the need for online testing equipment, and significantly reduces the cost of single-batch testing. The preset evaluation threshold can be 80%.

[0105] The roasted sand quality evaluation method proposed in this embodiment obtains the roasted sand sulfur content in the roasted sand to be tested and calculates the sulfur volatility based on the roasted sand sulfur content; obtains a sample image corresponding to the roasted sand to be tested and calibrates all ferric oxide particles in the sample image; obtains the porosity and pore size parameters of the ferric oxide particles based on the ferric oxide particle calibration results in the sample image; calculates the leaching rate of the roasted sand to be tested based on the sulfur volatility, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles using a stepwise regression method; and determines the quality evaluation result of the roasted sand to be tested based on the leaching rate and a preset evaluation threshold. In this way, based on offline detection, the leaching rate is predicted by sulfur volatility, ferric oxide porosity, and pore size parameters, and a "detection-analysis-prediction" full-process system is constructed to evaluate the quality of roasted sand, improving evaluation efficiency and reducing costs.

[0106] Example 2

[0107] In addition, the present disclosure provides a roasted sand quality evaluation device 600, see Figure 6 ,include:

[0108] A first acquisition module 601 is used to obtain the roasted sand sulfur content in the roasted sand to be detected, and calculate the sulfur volatilization rate according to the roasted sand sulfur content;

[0109] The calibration module 602 is used to obtain a sample image corresponding to the calcined sand to be detected, and calibrate all ferric oxide particles in the sample image;

[0110] A second acquisition module 603 is configured to acquire porosity and pore size parameters of the ferric oxide particles according to the calibration result of the ferric oxide particles in the sample image;

[0111] A calculation module 604 is configured to calculate the leaching rate of the calcine to be tested based on the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles using a stepwise regression method;

[0112] The determination module 605 is used to determine the quality evaluation result of the roasted sand to be tested according to the leaching rate and a preset evaluation threshold.

[0113] Optionally, the first acquisition module 601 is further used to obtain the original sulfur content of the roasted sand to be tested before roasting, calculate the sulfur content difference between the original sulfur content and the roasted sand sulfur content; calculate the ratio of the sulfur content difference to the original sulfur content, and use the ratio as the sulfur volatilization rate.

[0114] Optionally, the second acquisition module 603 is further used to obtain the regional area of each ferric oxide particle and the pore area between each ferric oxide particle based on the ferric oxide particle calibration result of the sample image; and obtain the porosity of the ferric oxide particle based on the quotient of the pore area and the regional area.

[0115] Optionally, the pore parameters of the ferric oxide particles include an average pore size and pore uniformity. The second acquisition module 603 is also used to obtain a binary image based on the sample image; perform pore distribution statistics on the binary image based on a preset pore recognition threshold to obtain multiple pore values; and calculate the average pore size and the pore uniformity based on each pore value.

[0116] Optionally, the second acquisition module 603 is also used to determine a first target aperture from each of the aperture values based on a first preset aperture distribution range; determine a second target aperture from each of the aperture values based on a second preset aperture distribution range; and calculate the quotient of the first target aperture and the second target aperture to obtain the aperture uniformity.

[0117] Optionally, the calculation module 604 is also used to calculate the product of the first preset coefficient and the sulfur volatilization rate to obtain a first product; calculate the product of the second preset coefficient and the porosity of the ferric oxide particles to obtain a second product; calculate the product of the third preset coefficient and the average pore size to obtain a third product; calculate the product of the fourth preset coefficient and the pore size uniformity to obtain a fourth product; calculate the sum of the fifth preset coefficient, the first product, the second product and the third product, and calculate the difference between the sum and the fourth product to obtain the leaching rate.

[0118] Optionally, the calibration module 602 is further configured to obtain a roasted sand sample of the roasted sand to be detected according to preset sample parameters; and obtain a sample image corresponding to the roasted sand sample based on preset acquisition parameters.

[0119] The device provided in the embodiment of the present disclosure can execute the steps of the roasted sand quality evaluation method provided in Example 1, which will not be described again to avoid repetition.

[0120] The roasted sand quality evaluation device proposed in this embodiment obtains the roasted sand sulfur content in the roasted sand to be tested and calculates the sulfur volatility based on the roasted sand sulfur content; obtains a sample image corresponding to the roasted sand to be tested and calibrates all ferric oxide particles in the sample image; obtains the porosity and pore size parameters of the ferric oxide particles based on the ferric oxide particle calibration results in the sample image; calculates the leaching rate of the roasted sand to be tested based on the sulfur volatility, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles using a stepwise regression method; and determines the quality evaluation result of the roasted sand to be tested based on the leaching rate and a preset evaluation threshold. In this way, based on offline detection, the leaching rate is predicted using the sulfur volatility, ferric oxide porosity, and pore size parameters, constructing a "detection-analysis-prediction" full-process system to evaluate the quality of roasted sand, improving evaluation efficiency and reducing costs.

[0121] Example 3

[0122] In addition, an embodiment of the present disclosure provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the roasted sand quality evaluation method described in Example 1 is implemented.

[0123] The device provided in the embodiment of the present disclosure can execute the steps of the roasted sand quality evaluation method provided in Example 1, which will not be described again to avoid repetition.

[0124] Example 4

[0125] The embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the roasted sand quality evaluation method described in Example 1 is implemented.

[0126] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0127] The computer-readable storage medium provided in this embodiment can implement the roasted sand quality evaluation method provided in Example 1. To avoid repetition, it will not be described here.

[0128] In all examples shown and described herein, any specific values should be interpreted as merely exemplary and not limiting, and thus other examples of the exemplary embodiments may have different values.

[0129] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0130] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that variations and modifications are possible without departing from the scope of the present invention, and such variations and modifications are fully within the scope of protection of the present invention.

Claims

1. A method for evaluating calcined sand quality, characterized in that: include: Obtaining the calcined sulfur content in the calcined sand to be tested, and calculating the sulfur volatilization rate based on the calcined sulfur content; Obtaining a sample image corresponding to the calcined sand to be tested, and calibrating all ferric oxide particles in the sample image; Obtaining the porosity and pore size parameters of the ferric oxide particles according to the ferric oxide particle calibration result of the sample image; Based on the stepwise regression method, the leaching rate of the calcined sand to be tested is calculated according to the sulfur volatilization rate, the porosity of the ferric oxide particles and the pore size parameters of the ferric oxide particles; The quality evaluation result of the roasted sand to be tested is determined according to the leaching rate and a preset evaluation threshold.

2. calcined sand quality evaluation method according to claim 1, is characterized in that, The step of obtaining the calcined sulfur content in the calcined sand to be detected and calculating the sulfur volatilization rate according to the calcined sulfur content comprises: Obtaining the original sulfur content of the calcined sand to be tested before roasting, and calculating the sulfur content difference between the original sulfur content and the calcined sand sulfur content; The ratio of the sulfur content difference to the original sulfur content is calculated, and the ratio is used as the sulfur volatilization rate.

3. calcined sand quality evaluation method according to claim 1, is characterized in that, The obtaining of the porosity of the ferric oxide particles according to the ferric oxide particle calibration result of the sample image includes: Obtaining the area of each ferric oxide particle and the area of the pores between each ferric oxide particle according to the ferric oxide particle calibration result of the sample image; The porosity of the ferric oxide particles is obtained according to the quotient of the pore area and the region area.

4. calcined sand quality evaluation method according to claim 1, is characterized in that, The pore size parameters of the ferric oxide particles include an average pore size and pore size uniformity. The pore size parameters of the ferric oxide particles are obtained according to the calibration results of the ferric oxide particles in the sample image, including: Obtain a binary image according to the sample image; Performing pore size distribution statistics on the binary image based on a preset pore identification threshold to obtain a plurality of pore size values; The average pore size and the pore size uniformity are calculated based on the pore size values.

5. calcined sand quality evaluation method according to claim 4, is characterized in that, Calculating the pore size uniformity according to each of the pore size values includes: determining a first target pore size from each of the pore size values based on a first predetermined pore size distribution range; determining a second target pore size from each of the pore size values based on a second predetermined pore size distribution range; The quotient of the first target aperture and the second target aperture is calculated to obtain the aperture uniformity.

6. calcined sand quality evaluation method according to claim 4, is characterized in that, The stepwise regression method is used to calculate the leaching rate of the calcined sand to be tested according to the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles, including: Calculating the product of a first preset coefficient and the sulfur volatilization rate to obtain a first product; Calculating the product of the second preset coefficient and the porosity of the ferric oxide particles to obtain a second product; Calculating the product of a third preset coefficient and the average pore size to obtain a third product; Calculating a product of a fourth preset coefficient and the pore uniformity to obtain a fourth product; The sum of the fifth preset coefficient, the first product, the second product and the third product is calculated, and the difference between the sum and the fourth product is calculated to obtain the leaching rate.

7. The method for evaluating calcined sand quality according to claim 1, wherein The obtaining of a sample image corresponding to the roasted sand to be detected includes: Obtaining a roasted sand sample of the roasted sand to be tested according to preset sample parameters; A sample image corresponding to the roasted sand sample is obtained based on preset acquisition parameters.

8. A roasted sand quality evaluation device, characterized in that: include: A first acquisition module is used to obtain the roasted sand sulfur content in the roasted sand to be detected, and calculate the sulfur volatilization rate according to the roasted sand sulfur content; a calibration module, configured to obtain a sample image corresponding to the calcined sand to be detected, and calibrate all ferric oxide particles from the sample image; A second acquisition module is used to obtain the porosity and pore size parameters of the ferric oxide particles according to the calibration result of the ferric oxide particles in the sample image; a calculation module, configured to calculate the leaching rate of the calcine to be tested according to the sulfur volatilization rate, the porosity of the ferric oxide particles, and the pore size parameters of the ferric oxide particles based on a stepwise regression method; A determination module is used to determine the quality evaluation result of the roasted sand to be tested according to the leaching rate and a preset evaluation threshold.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for evaluating the quality of roasted sand according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the roasted sand quality evaluation method according to any one of claims 1 to 7.

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