Online laser detection and grading regulation and control system for particle size distribution of crushed raw ore

Abstract

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

Description

Technical Field

[0001] The present invention relates to the field of automated detection technology, and more particularly to an online laser detection and classification control system for particle size distribution of crushed ore. Background Art

[0002] In the ore processing and beneficiation industries, the particle size distribution of crushed ore is a key factor influencing subsequent process efficiency and product quality. Accurate particle size distribution measurement and classification control not only optimizes crushing equipment operating parameters and improves screening efficiency, but also effectively reduces energy consumption, minimizes equipment wear, and improves final product quality. Therefore, the development of efficient, real-time online particle size measurement and control systems has become a key focus of industry technological development.

[0003] One significant issue is the "halo effect" caused by fine dust adhering to the edges of ore particles. During the crushing process, the mechanical crushing and friction of the ore generate a large amount of fine dust. This dust adheres to the particle surface, forming an irregular scattering layer. When the laser beam strikes the particle edge, the light intensity distribution exhibits a non-uniform radial diffusion at the edge due to dust scattering, resulting in a blurred halo region. This halo effect makes it difficult to accurately identify the true boundaries of particles in dynamic scattered light field data. Traditional boundary detection algorithms, such as those based on threshold segmentation or simple gradient analysis, often misidentify halo regions as part of the particle boundary, causing the boundary position to deviate from the true value and leading to errors in the calculation of geometric characteristic parameters such as particle size and shape. For example, in a crushing environment with high dust concentration, the spatial impact of the halo effect can reach 10%-30% of the particle diameter, magnifying the identification of small particles while blurring the boundaries of large particles, seriously affecting the accuracy of the particle size distribution curve. In addition, the intensity and diffusion characteristics of the halo effect vary with factors such as ore type, particle size range, dust concentration, and laser incident angle. Traditional algorithms lack adaptive adjustment capabilities, resulting in instability in detection results.

[0004] Another issue is the single nature of existing technologies in particle size distribution analysis. Traditional particle size distribution analysis methods mainly focus on the size distribution of particles, such as calculating the particle size distribution histogram through equivalent diameters, but often ignore important geometric features such as the shape factor and boundary complexity of the particles. This single analysis method cannot fully characterize the characteristics of the original ore particle population. Especially when it is necessary to meet specific process requirements (such as flotation, screening, etc.), there is a lack of comprehensive consideration of particle shape and surface roughness, resulting in a low degree of match between the analysis results and actual process requirements. For example, in the flotation process, irregularly shaped particles and regularly shaped particles of the same size exhibit different behaviors, and traditional methods cannot effectively distinguish these differences, which limits the optimization of the grading control strategy.

[0005] In addition, in terms of graded regulation, existing technologies usually adopt static or semi-static regulation strategies, lacking real-time particle size distribution feedback and dynamic adjustment mechanisms. Traditional methods often only focus on the average value or main peak position of the particle size distribution, ignoring the uniformity and multimodal characteristics of the distribution, resulting in a less refined regulation strategy. For example, when the particle size distribution is large and uneven, traditional methods may simply increase the crushing force, but are unable to formulate differentiated strategies for optimizing the distribution morphology. At the same time, in the adjustment of the operating parameters of the crushing equipment, existing technologies mostly rely on trial and error or experience adjustment, lacking a prediction and feedback correction mechanism for the adjustment effect. This adjustment method is not only inefficient, but may also lead to production fluctuations, increased energy consumption or increased equipment wear. In addition, the parameter adjustment of crushing equipment and screening equipment often lacks coordinated optimization, making it difficult to achieve the optimal overall regulation effect.

[0006] In view of this, the present invention proposes an online laser detection and classification control system for the particle size distribution of crushed ore to solve the above problems. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: an online laser detection and classification control system for the particle size distribution of crushed ore, comprising: Laser scanning module, used to perform online laser scanning on the conveying flow field of the crushed ore to obtain dynamic scattered light field data containing the edge features of the ore particles; a light field preprocessing module, configured to identify halo effect areas and generate halo effect compensation coefficients based on the non-uniform characteristics of light intensity distribution in the dynamic scattered light field data; an edge reconstruction module for reconstructing the boundaries of the halo effect region at the edge of the particle in the dynamic scattering light field data based on the halo effect compensation coefficient and the geometric constraint condition of laser scattering, so as to obtain accurate particle boundary data; A particle size distribution analysis module is used to extract geometric characteristic parameters of the raw ore particles based on the precise particle boundary data and generate a real-time particle size distribution curve; A classification strategy generation module is used to determine the classification control strategy of the raw ore particles according to the deviation characteristics of the real-time particle size distribution curve and the preset particle size classification standard; The dynamic control module is used to adjust the operating parameters of the crushing equipment in real time based on the hierarchical control strategy to optimize the particle size distribution of the raw ore particles.

[0008] Furthermore, the method for identifying the halo effect area includes: Extracting light intensity gradient mutation regions as candidate halo effect regions based on the gradient characteristics of light intensity distribution in the dynamic scattered light field data; Calculating a radial range of light intensity diffusion according to radial diffusion characteristics of light intensity in the candidate halo effect region; determining a spatial boundary of a halo effect region according to the radius range and the incident angle of the laser beam; The halo effect compensation coefficient is generated according to the non-uniformity of the light intensity distribution within the spatial boundary.

[0009] Furthermore, the boundary reconstruction method includes: adjusting the light intensity distribution of the halo effect area in the dynamic scattered light field data according to the halo effect compensation coefficient to generate compensated light field data; constructing a candidate set of edge points based on light intensity gradient characteristics of particle edges in the compensated light field data; According to the geometric constraint conditions of the laser scattering, edge points that meet the continuity conditions in the candidate set are screened to form an initial boundary curve; According to the curvature change trend of the initial boundary curve, the curvature mutation points are eliminated to obtain the accurate particle boundary data.

[0010] Furthermore, the method for extracting geometric feature parameters includes: Constructing a boundary polygon model of the raw ore particles based on the precise particle boundary data; Calculating the equivalent diameter, shape factor, and boundary complexity of the raw ore particles based on the vertex distribution characteristics of the boundary polygon model, wherein the shape factor is determined by the ratio of the perimeter to the area of ​​the boundary polygon, and the boundary complexity is calculated by the Fourier descriptor of the boundary curve; The real-time particle size distribution curve is generated according to the statistical distribution of the equivalent diameter, shape factor and boundary complexity.

[0011] Furthermore, the method for determining the hierarchical control strategy includes: Extracting peak characteristics and width characteristics of the particle size distribution according to the real-time particle size distribution curve; determining a deviation direction of the particle size distribution according to a deviation between the peak characteristic and the preset particle size classification standard; Determining the uniformity level of the particle size distribution based on the degree of matching between the width feature and the preset particle size classification standard; The hierarchical control strategy is generated according to the offset direction and the uniformity level, wherein the hierarchical control strategy includes a rotation speed adjustment amplitude of the crushing device and a sieve hole size adjustment step of the screening device.

[0012] Furthermore, the method for calculating the radius range of the light intensity diffusion includes: constructing a light intensity attenuation curve according to the radial distribution of light intensity within the candidate halo effect region; determining a diffusion boundary of the halo effect according to the inflection point position of the light intensity attenuation curve; Calculating the radius range of the light intensity diffusion according to the distance between the diffusion boundary and the incident point of the laser beam; According to the dynamic change trend of the radius range, the pseudo halo effect area caused by environmental noise is eliminated.

[0013] Furthermore, the method for removing curvature mutation points includes: Extracting candidate mutation points whose curvature values ​​exceed a preset curvature threshold according to the local curvature of the initial boundary curve; Determining whether the candidate mutation point is caused by a halo effect residual based on the consistency of the light intensity gradient of the boundary points before and after the candidate mutation point; If the candidate mutation point is caused by the halo effect residual, the candidate mutation point is smoothed by interpolation; If the candidate mutation point is not caused by the halo effect residual, the candidate mutation point is retained as the true feature point of the particle boundary.

[0014] Furthermore, the method for generating the real-time particle size distribution curve includes: constructing a histogram of particle size distribution according to the statistical distribution of the equivalent diameters; performing shape correction on the histogram according to the distribution characteristics of the shape factors; performing complexity smoothing on the histogram according to the distribution characteristics of the boundary complexity; The real-time particle size distribution curve is fitted based on the corrected and smoothed histogram.

[0015] Furthermore, the method for determining the uniformity level includes: Calculating a coefficient of variation of the width of the particle size distribution based on the width characteristic; determining an initial uniformity level of the particle size distribution based on a ratio of the width variation coefficient to a preset uniformity threshold; determining a multimodal characteristic of the particle size distribution based on whether a secondary peak exists in the real-time particle size distribution curve; If multimodal characteristics exist, the initial uniformity level is adjusted according to the height and width of the secondary peak to obtain a final uniformity level.

[0016] Furthermore, the real-time adjustment method of the operating parameters includes: Determine the adjustment direction and adjustment range of the crushing equipment speed according to the hierarchical control strategy; According to the adjustment direction, predict the change trend of the particle size distribution of the raw ore particles after the rotation speed is adjusted; Calculating a feedback correction coefficient for speed adjustment based on a deviation between the change trend and the preset particle size classification standard; According to the feedback correction coefficient, the speed adjustment range is dynamically adjusted, and the sieve hole size of the screening equipment is synchronously updated to obtain optimized operating parameters.

[0017] The technical effects and advantages of the online laser detection and classification control system for crushed ore particle size distribution of the present invention are as follows: The present invention effectively eliminates the interference of the halo effect, greatly improves the reliability of the detection results, and ensures the accurate identification of particle boundaries even in a crushing environment with high dust concentration, thereby significantly reducing the particle size distribution deviation caused by boundary misjudgment and ensuring the stability of product quality. Secondly, by solving the halo effect problem, the present invention avoids the phenomenon of small particles being magnified and identified or the blurring of large particle boundaries, provides more real and consistent data support for downstream processes, effectively improves process efficiency and product performance, especially in scenarios with high requirements for particle size accuracy, and can significantly reduce process fluctuations caused by detection errors. In addition, the present invention optimizes the accuracy of the control process by improving the accuracy of boundary judgment, avoids blind adjustments caused by errors, significantly improves production efficiency, and at the same time reduces energy waste and equipment wear caused by excessive or insufficient crushing, extends the service life of the equipment, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the online laser detection and classification control system for the particle size distribution of crushed ore of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0020] This application provides an online laser detection and classification control system for crushed ore particle size distribution. The system's execution entities include, but are not limited to, the following: laser scanning devices, edge computing units, particle size analysis platforms, classification controllers, crushing equipment, etc., which can be considered general computing nodes in this application.

[0021] See also Figure 1 The present invention provides an online laser detection and classification control system for the particle size distribution of crushed ore, comprising: Laser scanning module, used to perform online laser scanning on the conveying flow field of the crushed ore to obtain dynamic scattered light field data containing the edge features of the ore particles; The light field preprocessing module is used to identify the halo effect area caused by fine dust attached to the edges of raw ore particles based on the non-uniform characteristics of the light intensity distribution in the dynamic scattered light field data, and generate the halo effect compensation coefficient; The edge reconstruction module is used to reconstruct the boundaries of the halo effect area at the edge of particles in the dynamic scattered light field data based on the halo effect compensation coefficient and the geometric constraints of laser scattering to obtain accurate particle boundary data; The particle size distribution analysis module is used to extract the geometric characteristic parameters of the raw ore particles based on the precise particle boundary data and generate a real-time particle size distribution curve; A classification strategy generation module is used to determine the classification control strategy of the raw ore particles based on the deviation characteristics between the real-time particle size distribution curve and the preset particle size classification standard; The dynamic control module is used to adjust the operating parameters of the crushing equipment in real time based on the hierarchical control strategy to optimize the particle size distribution of the raw ore particles.

[0022] The present invention uses laser scanning technology and advanced light field processing algorithms to achieve precise detection of the particle size of crushed ore, identifies and compensates for the halo effect of the particle edge at the edge, making boundary reconstruction more accurate, and extracts geometric features based on precise particle boundary data to generate a high-precision particle size distribution curve. The crushing equipment parameters are dynamically adjusted according to the deviation between the real-time particle size distribution and the target requirements to form a closed-loop control, ultimately achieving precise regulation of the particle size distribution of the crushed ore and optimization of the production process.

[0023] In an embodiment of the present invention, a method for identifying a halo effect area includes: According to the gradient characteristics of the light intensity distribution in the dynamic scattered light field data, the light intensity gradient mutation area is extracted as the candidate halo effect area; Calculate the radius range of light intensity diffusion according to the radial diffusion characteristics of light intensity in the candidate halo effect area; Determine the spatial boundary of the halo effect area based on the radius range and the incident angle of the laser beam; According to the non-uniformity of the light intensity distribution within the spatial boundary, a halo effect compensation coefficient is generated, wherein the halo effect compensation coefficient is inversely proportional to the radius range of the light intensity diffusion.

[0024] In this embodiment, first, a gradient analysis is performed on the dynamic scattered light field data obtained by the laser scanning module, and the first-order derivative of the spatial distribution of light intensity is calculated to form a light intensity gradient field; by setting a threshold (such as twice the local mean), areas with abnormally high gradient values ​​are screened, which usually correspond to the geometric boundaries of the particles or halo effect areas; connected domain analysis and morphological processing are performed on the gradient mutation areas, adjacent small areas are merged, and too small noise areas are eliminated to obtain a series of candidate halo effect areas; radial light intensity analysis is performed on each candidate area, and the radial distribution profile of light intensity is extracted along different angular directions with the area centroid as the center; Gaussian fitting or exponential decay model is applied to fit the radial distribution to obtain characteristic parameters of light intensity attenuation with distance, such as attenuation coefficient, characteristic length, etc.; the radius range of light intensity diffusion is determined in combination with the fitting parameters, that is, the radius range where the light intensity value drops to the background noise The distance from the sound level defines the spatial influence range of the halo effect; considering the incident angle of the laser beam and the particle surface, the spatial distribution characteristics of the halo effect are calculated by the principle of geometric optics, and the directional preference of the radius range is adjusted; actual measurements show that the closer the incident angle is to vertical, the more obvious the halo effect is, so an angle correction factor is introduced to enhance the accuracy of the model; according to the determined spatial boundary, the non-uniformity of the light intensity distribution within the boundary is analyzed, such as statistical indicators such as standard deviation, entropy value, and Gini coefficient; a halo effect compensation coefficient calculation model is established, which is inversely proportional to the radius range of the light intensity diffusion, that is, the larger the diffusion radius, the smaller the compensation coefficient, indicating that a stronger boundary correction is needed; an adaptive threshold adjustment method is applied to dynamically optimize the compensation coefficient according to the ore type, particle size range and scanning conditions to ensure that the halo effect can be effectively identified and compensated under different working conditions.

[0025] This method takes into account the physical characteristics of laser scanning in real industrial environments. Through gradient analysis, radial diffusion analysis, and incident angle correction, it accurately identifies and quantitatively characterizes the halo effect at particle edges. This method is suitable for processing crushed ore containing large amounts of fine dust. Compared to traditional edge detection methods, it can effectively distinguish between true boundaries and halo interference, providing reliable pre-processing support for subsequent boundary reconstruction.

[0026] In an embodiment of the present invention, a boundary reconstruction method includes: According to the halo effect compensation coefficient, the light intensity distribution of the halo effect area in the dynamic scattered light field data is adjusted to generate compensated light field data; Construct a candidate set of edge points based on the light intensity gradient characteristics of the particle edges in the compensated light field data; According to the geometric constraints of laser scattering, edge points that meet the continuity conditions are screened from the candidate set to form an initial boundary curve; According to the curvature change trend of the initial boundary curve, the curvature mutation points are eliminated to obtain accurate particle boundary data.

[0027] In this embodiment, first, according to the halo effect compensation coefficient determined in the previous step, a light intensity distribution adjustment function is designed, which usually adopts the form of Gaussian inverse transform or radial attenuation compensation; the adjustment function is applied to the halo effect area in the dynamic scattered light field data to weaken the halo diffusion effect, strengthen the real boundary signal, and generate compensated light field data; the compensation process pays special attention to maintaining the texture features and micro-structure information in the original data to avoid excessive smoothing resulting in loss of details; a multi-scale gradient operator (such as Sobel, Canny or LoG operator) is applied to the compensated light field data to extract the light intensity gradient features; based on the gradient amplitude and direction information, an adaptive threshold method is used to select pixel points with significant gradient to form a candidate set of edge points; a density analysis is performed on the edge points in the candidate set to eliminate isolated noise points and overly dense clustered areas; geometric constraints of laser scattering are introduced, including scattering angle restrictions, the relationship between reflection intensity and incident angle, and the relationship between reflection intensity and incident angle. The method uses physical models such as the particle system and material scattering characteristics; based on geometric constraints, calculates the physical rationality score of each candidate edge point, and selects points with high scores as valid edge points; applies a path connection algorithm (such as the minimum energy path or B-spline interpolation) to the valid edge points, connects adjacent points to form a continuous initial boundary curve; calculates the local curvature of each point on the initial boundary curve, and constructs a curvature distribution map; analyzes the curvature change trend and identifies the curvature mutation points, which usually correspond to unnatural distortion of the boundary or residual influence of the halo effect; combines curvature analysis with local light field characteristics to judge the authenticity of the mutation points and distinguish between actual particle shape features and pseudo features caused by halo; eliminates curvature mutation points determined to be pseudo features, retains real feature points, and reconstructs boundary segments through smooth interpolation to ultimately form accurate particle boundary data; the boundary data is stored in the form of closed curves or point sequences, containing spatial coordinates and confidence information, providing a basis for subsequent geometric feature extraction.

[0028] By incorporating a physical model of laser scattering as a constraint, the boundary recognition process relies not only on image features but also on the laws of optical scattering, significantly improving boundary recognition accuracy in complex industrial environments. The introduction of curvature analysis further optimizes boundary details, making the reconstructed particle shape more realistic and providing high-quality basic data for subsequent particle size analysis.

[0029] In an embodiment of the present invention, a method for extracting geometric feature parameters includes: Construct boundary polygon models of ore particles based on precise particle boundary data; According to the vertex distribution characteristics of the boundary polygon model, the equivalent diameter, shape factor and boundary complexity of the original ore particles are calculated. The shape factor is determined by the ratio of the perimeter to the area of ​​the boundary polygon, and the boundary complexity is calculated by the Fourier descriptor of the boundary curve. Generate real-time particle size distribution curves based on the statistical distribution of equivalent diameter, shape factor, and boundary complexity.

[0030] In this embodiment, starting from the precise particle boundary data, the Douglas-Peucker algorithm or the vertex curvature sampling method is applied to simplify the continuous boundary curve into a polygonal model with representative vertices, while controlling the simplification error within an acceptable range (usually not exceeding 1% of the original boundary perimeter); for particles with complex shapes, an adaptive sampling strategy can be adopted to retain more vertices in areas with large curvature changes to ensure that detailed features are not lost; the constructed boundary polygon model is geometrically analyzed to calculate basic geometric properties such as area, perimeter, center of gravity position, and orientation; the equivalent diameter is calculated based on the area, that is, the diameter of a circle equal to the area of ​​the particle, which is the main parameter to characterize the particle size; the shape factor is calculated, which is defined as the ratio of the square of the perimeter of the boundary polygon to the area (normalized to compare with a circle, where the circle is 1). The shape factor reflects the degree of irregularity of the particle, and the larger the value, the more irregular the shape; Fourier transform is applied to the boundary curve to extract Fourier descriptors, which represent Frequency characteristics of boundary shape; the first N low-frequency descriptors and specific high-frequency descriptors are selected to form a feature vector, and the modulus of the feature vector is calculated as the boundary complexity index; the complexity index reflects the richness of boundary details and the complexity of the shape, and is an important feature for distinguishing different crushing mechanisms and ore types; a statistical analysis is performed on the equivalent diameters of a large number of particles to construct a particle size distribution histogram, which is usually divided into 10-20 particle size grades; the kernel density estimation or parameterized fitting method (such as lognormal distribution, Rosin-Rammler distribution, etc.) is applied to smooth the histogram into a continuous particle size distribution curve; the particle size distribution curve is shape-corrected in combination with the shape factor distribution information, considering the actual process influence of non-spherical particles; the boundary complexity distribution is introduced to further refine the particle size distribution characteristics and distinguish between smooth and rough surface particle groups; the final generated real-time particle size distribution curve not only contains size distribution information, but also integrates shape and surface characteristics, providing comprehensive feature characterization for graded regulation.

[0031] This method extracts multidimensional geometric features of raw ore particles using a boundary polygon model. It not only focuses on traditional particle size distribution but also incorporates new features such as shape factor and boundary complexity to comprehensively characterize the geometric properties of particle groups. In particular, Fourier descriptors are used to analyze boundary complexity. The real-time particle size distribution curve generated based on these multidimensional features contains richer information than traditional distribution curves based solely on size, providing a more accurate basis for the development of subsequent grading and control strategies.

[0032] In an embodiment of the present invention, a method for determining a hierarchical control strategy includes: According to the real-time particle size distribution curve, the peak characteristics and width characteristics of the particle size distribution are extracted; Determine the deviation direction of the particle size distribution based on the deviation between the peak characteristics and the preset particle size classification standard; Determine the uniformity of the particle size distribution based on the degree of match between the width feature and the preset particle size classification standard; According to the deviation direction and uniformity level, a hierarchical control strategy is generated, wherein the hierarchical control strategy includes the speed adjustment amplitude of the crushing equipment and the sieve size adjustment step of the screening equipment.

[0033] In this embodiment, feature extraction is performed on the real-time particle size distribution curve to identify peak features such as the main peak position, peak height, and half-peak width, which reflect the main size concentration interval of the particle population; at the same time, width features such as the standard deviation, skewness coefficient, and peak state coefficient of the distribution curve are extracted to characterize the discrete degree and morphological characteristics of the particle size distribution; the particle size distribution is compared with the preset particle size classification standard, which is usually based on downstream process requirements or product specification definitions and includes target particle size range and distribution morphology requirements; the deviation between the peak feature and the preset standard, especially the difference between the main peak position and the target particle size, is calculated to determine whether the particle size distribution is generally larger or smaller, that is, the direction of the offset; an offset degree assessment is introduced to quantify the deviation as a percentage or standard score as a reference for the intensity of regulation; the matching degree between the width feature and the preset standard is analyzed, and matching indicators such as the root mean square error (RMSE) or distribution overlap are calculated; based on the matching indicators, uniformity, etc. The grades are divided into "highly uniform", "moderately uniform", "uneven" and "highly uneven" levels; corresponding grading control strategies are formulated for different combinations of offset directions and uniformity levels; for example, for the case of "slightly large and uneven", it may be necessary to increase the crushing force and strengthen the screening link; while for the case of "slightly small but highly uniform", it may only be necessary to fine-tune the crushing parameters; specific strategies include the speed adjustment range of the crushing equipment (such as increase / decrease by 5%, 10%, 15%, etc.). Generally, the higher the speed, the finer the particles after crushing; at the same time, the sieve size adjustment step of the screening equipment is determined (such as ±2mm, ±5mm, etc.) to control the upper limit particle size of the finished product and adjust the product grading accuracy; when formulating the strategy, the physical limitations and response characteristics of the equipment adjustment are considered to ensure the executability of the control instructions; at the same time, production efficiency and energy consumption indicators are taken into account to minimize energy consumption and equipment wear while meeting the particle size requirements.

[0034] This method systematically identifies a hierarchical control strategy by analyzing the discrepancies between the real-time particle size distribution characteristics and the target requirements. In particular, the introduction of two key dimensions, deviation direction and uniformity level, enables the control strategy to precisely address different types of distribution deviations. Compared to traditional methods, this dual-dimensional strategy generation approach focuses not only on adjusting the average particle size but also on optimizing the distribution morphology, enabling it to more comprehensively meet the refined particle size distribution requirements of downstream processes.

[0035] In an embodiment of the present invention, a method for calculating the radius range of light intensity diffusion includes: Constructing a light intensity attenuation curve according to the radial distribution of light intensity in the candidate halo effect region; Determine the diffusion boundary of the halo effect according to the inflection point position of the light intensity attenuation curve; Calculate the radius range of light intensity diffusion based on the distance between the diffusion boundary and the incident point of the laser beam; According to the dynamic change trend of the radius range, the pseudo halo effect area caused by environmental noise is eliminated.

[0036] In this embodiment, for each candidate halo effect area, its center point (usually the point with the highest light intensity) is determined as the starting point of the radial analysis; with the center point as the origin, light intensity values ​​are sampled along multiple radial directions (such as 8 or 16 evenly distributed directions) to obtain raw data on the change of light intensity with distance; a smoothing filter (such as Savitzky-Golay filter) is applied to the sampled data in each direction to reduce the influence of noise and improve the stability of subsequent analysis; a light intensity attenuation curve is constructed based on the filtered data, and the curve is usually in the form of exponential decay or power law decay; a curve fitting method (such as nonlinear least squares method) is applied to fit the measured data into a mathematical model, such as ,in The first and second derivatives of the intensity attenuation curve are calculated to identify the inflection point of the curve, i.e., the location where the attenuation rate changes most significantly. The inflection point typically corresponds to the boundary region where the halo effect transitions to the background and is a key feature point in determining the diffusion boundary. In complex situations (e.g., multiple inflection points), the most reasonable diffusion boundary is determined by combining physical models and threshold methods, such as the point where the light intensity drops to 10% of the central value. The distance from the diffusion boundary to the incident point in each radial direction is calculated to obtain the directional radius value. The radius values ​​in all directions are combined to calculate the mean, standard deviation, and directional preference to form a complete description of the radius range. The changing trend of the radius range in multiple frames of continuous data is analyzed to construct a time series model. Abnormal fluctuations in radius values ​​are identified, which are often caused by environmental noise (e.g., dust clouds or illumination changes). Statistical test methods (e.g., the 3σ criterion or DBSCAN clustering) are used to identify and eliminate pseudo-halo effect regions whose radius changes do not match the physical model expectations. Stable true halo effect regions are retained for subsequent processing to improve the system's anti-interference ability.

[0037] This method accurately calculates the radius of the halo effect through detailed radial light intensity analysis and dynamic trend monitoring. In particular, the introduction of inflection point analysis and time series models enables the system to distinguish between true particle edge haloes and pseudo-halos caused by environmental noise, significantly improving detection reliability in complex industrial environments. This method is highly adaptable and can handle halo effects in different ore types and dust concentrations, providing accurate reference information for subsequent boundary reconstruction.

[0038] In an embodiment of the present invention, a method for removing curvature mutation points includes: According to the local curvature of the initial boundary curve, candidate mutation points whose curvature values ​​exceed a preset curvature threshold are extracted; According to the consistency of the light intensity gradient of the boundary points before and after the candidate mutation point, it is judged whether the candidate mutation point is caused by the halo effect residual; If the candidate mutation point is caused by the halo effect residual, the candidate mutation point is smoothed by interpolation; If the candidate mutation point is not caused by the halo effect residual, the candidate mutation point is retained as the true feature point of the particle boundary.

[0039] In this embodiment, a curvature calculation algorithm is first applied to the initial boundary curve, such as the three-point method or B-spline fitting followed by second-order derivative, to calculate the local curvature of each point on the curve; a curvature distribution histogram is established to determine the range and statistical characteristics of normal curvature; a curvature threshold is set, which is usually 3-5 times the average curvature or an empirical value based on background knowledge, and the threshold needs to be adjusted according to the type of ore and crushing characteristics; points whose absolute value of curvature exceeds the threshold are extracted as candidate mutation points, which usually correspond to sharp corners, depressions or abnormal fluctuations on the boundary; for each candidate mutation point, the light intensity gradient characteristics of the front and rear boundary segments are analyzed, including gradient size, direction and consistency; the gradient correlation or coherence index of the area near the mutation point is calculated to quantify the degree of consistency of the optical characteristics of the local boundary; highly consistent gradients usually indicate continuous real boundaries, while inconsistent gradients may be false boundaries caused by residuals of the halo effect; and simultaneously, the corresponding gradients in the original light field data before compensation are analyzed. The characteristics of the position are used to find possible evidence of halo interference; the gradient consistency and the original light field characteristics are combined to determine the cause of the candidate mutation points and classify them as "halo effect residuals" or "real feature points"; for the mutation points determined to be halo effect residuals, appropriate interpolation methods are applied for smoothing, such as cubic spline interpolation or local weighted regression; the continuity and smoothness of the front and back boundaries are considered during interpolation to ensure a natural transition of the corrected boundaries; the mutation points determined to be real feature points are retained, and these points may represent the real morphological characteristics of the particles, such as broken sections, crystal structures or mineral joints; to ensure the quality of processing, a confidence scoring mechanism can be introduced to quantitatively evaluate the reliability of the judgment results, and low-confidence areas can be manually reviewed or more complex analysis algorithms can be applied; ultimately, accurate particle boundary data processed by mutation points are formed, which not only retains the real morphological characteristics of the particles, but also eliminates the residual influence of the halo effect.

[0040] This method distinguishes halo effect residuals from true particle features through intensity gradient consistency analysis, avoiding the oversmoothing and feature loss that can occur with simple curvature thresholding methods. This approach effectively eliminates the interference of halo effects while preserving the true morphological characteristics of raw ore particles, making the reconstructed boundaries more accurate and reliable. This differentiated processing strategy, particularly for complex-shaped particles, preserves important morphological information, providing a high-quality data foundation for subsequent particle size and shape analysis.

[0041] In an embodiment of the present invention, a method for generating a real-time particle size distribution curve includes: According to the statistical distribution of equivalent diameters, a histogram of particle size distribution is constructed; According to the distribution characteristics of the shape factor, the histogram is corrected in shape, where particles with higher shape factors have higher weights in the histogram; According to the distribution characteristics of boundary complexity, the histogram is smoothed. The particles with higher boundary complexity have larger smoothing windows in the histogram. The real-time particle size distribution curve is fitted based on the corrected and smoothed histogram.

[0042] In this embodiment, equivalent diameter data of a large number of particles are collected, and usually 100-1000 particle samples are required to obtain a statistically significant distribution; the equivalent diameter range is divided into an appropriate number of intervals (usually 10-20), the number of particles in each interval is counted, and an initial particle size distribution histogram is constructed; traditional particle size statistical indicators, such as median diameter (D50) and cumulative distribution curve, are calculated as basic reference data; the shape factor distribution of all particles is analyzed, and the average shape factor and standard deviation of different particle size intervals are calculated; based on process knowledge, a relationship model between shape factor and actual process influence is established, such as in flotation process, irregularly shaped particles often exhibit different behavior than regularly shaped particles of the same size; a shape weight function is designed so that particles with higher shape factors (i.e., more irregular) have higher weights in the histogram, and a typical weight function is W(SF)=1+α×(SF-1), where α is a process-related adjustment coefficient and SF is the shape factor; the shape weight function is applied to correct the initial histogram so that the shape factor is greater than the shape factor. The histogram better reflects the behavioral characteristics of the particle group in the actual process; the distribution characteristics of boundary complexity are analyzed, and the proportion and distribution pattern of different complexity levels are calculated; a complexity-related smoothing window function is designed so that the smoothing window corresponding to particles with higher boundary complexity is larger, which reflects the diversity of complex surface particles in the process; variable window smoothing is applied to the shape-corrected histogram to reduce sharp fluctuations in high-complexity areas and enhance the stability and representativeness of the distribution curve; an appropriate fitting model, such as lognormal distribution, Rosin-Rammler distribution or multi-peak Gaussian mixture model, is used to fit the smoothed histogram; the fitting process uses least squares or maximum likelihood estimation to optimize model parameters to best match the actual data; the fitting quality is evaluated, and indicators such as R² and residual sum of squares are calculated to ensure that the fitting curve accurately reflects the actual distribution; the final real-time particle size distribution curve contains both basic size distribution information and integrates the influence of shape and surface complexity. It is an enhanced distribution representation that comprehensively reflects the particle characteristics.

[0043] This method innovatively incorporates two key features, shape factor and boundary complexity, into the generation of particle size distribution curves, overcoming the limitations of traditional methods that focus solely on size distribution. Shape correction ensures that the process impact of irregular particles is properly represented, while complexity smoothing reflects the regulatory effect of surface properties on distribution stability. This multi-feature-integrated distribution curve contains richer information than traditional curves and can more comprehensively guide the optimization and control of crushing and classification processes.

[0044] In an embodiment of the present invention, a method for determining a uniformity level includes: Based on the width characteristics, the coefficient of variation of the width of the particle size distribution is calculated; Determine the initial uniformity level of the particle size distribution based on the ratio of the width variation coefficient to a preset uniformity threshold; Determine the multimodal characteristics of the particle size distribution based on whether there is a secondary peak in the real-time particle size distribution curve; If multimodal characteristics exist, the initial uniformity level is adjusted based on the height and width of the secondary peaks to obtain the final uniformity level.

[0045] In this embodiment, based on the width characteristics of the particle size distribution curve, such as standard deviation, half-peak width, 90% span (D90-D10), etc., quantitative indicators reflecting the degree of distribution dispersion are calculated; these indicators are standardized and the width variation coefficient is calculated. This coefficient is usually defined as the ratio of the standard deviation to the mean value, which reflects the relative degree of dispersion; preset uniformity thresholds are set. These thresholds are determined based on industry standards, process requirements or historical experience and are usually divided into multiple levels corresponding to different uniformity requirements; the ratio of the width variation coefficient to the preset threshold is calculated, and the initial uniformity level is determined according to the ratio interval, such as "highly uniform" (ratio <0.8), "moderately uniform" (0.8≤ratio <1.2), "uneven" (1.2≤ratio <1.5) and "highly uneven" (ratio ≥1.5); peak analysis is performed on the real-time particle size distribution curve, and a peak detection algorithm (such as based on the second-order derivative or wavelet transform) is used to identify the main peak and possible secondary peaks; peak significance indicators are defined, such as peak height ratio (secondary peak height / main peak height) or peak-to-valley depth, Used to judge the degree of prominence of multimodal characteristics; when the peak significance exceeds the threshold, multimodal characteristics are determined to exist, which usually means that the particle population contains multiple size concentration areas, which may be the result of insufficient crushing or uneven raw materials; analyze the characteristics of secondary peaks, including relative height (height ratio to the height of the main peak), relative width (width ratio to the width of the main peak), and distance (distance from the main peak); establish a quantitative scoring model for multimodal characteristics, such as Score = w1×Height+w2×Width+w3×Distance, where w1, w2, and w3 are weight coefficients; adjust the initial uniformity level based on the multimodal score; usually, obvious multimodal characteristics will lead to a decrease in uniformity level, such as from "moderately uniform" to "uneven"; in special cases, such as when the secondary peak is very small and very close to the main peak, only slight adjustments may be made or the original level may be maintained; the final uniformity level serves as an important basis for generating a graded control strategy, directly affecting the intensity and direction of control.

[0046] This method comprehensively assesses the uniformity of the particle size distribution by combining the coefficient of variation of width with multimodal analysis. In particular, the introduction of multimodal analysis enables the system to identify and process complex distribution patterns, going beyond simple unimodal distribution assessments. This method is particularly effective in detecting anomalies during the crushing process, such as partial undercrush or the separation of ores of different hardnesses, providing crucial decision-making insights for precise control.

[0047] In an embodiment of the present invention, a method for adjusting operating parameters in real time includes: Determine the adjustment direction and range of the crushing equipment speed according to the hierarchical control strategy; According to the adjustment direction, predict the change trend of the particle size distribution of the raw ore particles after the speed adjustment; Calculate the feedback correction coefficient for speed adjustment based on the deviation between the change trend and the preset particle size classification standard; According to the feedback correction coefficient, the speed adjustment range is dynamically adjusted, and the sieve hole size of the screening equipment is synchronously updated to obtain the optimized operating parameters.

[0048] In this embodiment, the adjustment direction (increase or decrease) and the initial adjustment range of the crushing equipment speed are first determined according to the graded control strategy, such as "increase the speed by 10%" or "reduce the speed by 15%"; an empirical model or mathematical model is established between the crushing equipment speed and the particle size distribution. Generally, an increase in speed will lead to a finer product particle size, but the specific relationship is related to factors such as the equipment type and the ore properties; a machine learning model (such as a random forest or neural network) trained with historical data is used to predict the trend of particle size distribution changes after a given speed adjustment; the prediction results include multiple aspects such as the main peak position offset, distribution width change and output impact; the predicted change trend is compared with the preset particle size classification standard to calculate possible deviations after adjustment, such as problems such as excessive particle size or increased unevenness; based on the deviation analysis, the feedback correction coefficient of the speed adjustment is calculated, which is used to optimize the initial adjustment range to avoid excessive or insufficient adjustment; the calculation of the feedback correction coefficient takes into account multiple factors, including the size of the predicted deviation, the urgency of the adjustment, the response characteristics of the equipment, etc.; the formula is used as Adjusted=Initial×(1+Feedback) calculates the final speed adjustment range, where Feedback is the feedback correction coefficient, Adjusted refers to the final speed adjustment range after the feedback correction coefficient is calculated, and Initial refers to the speed adjustment range preliminarily determined according to the graded control strategy, such as the 10% in "increase the speed by 10%"; at the same time, based on the corrected speed adjustment, the optimal screen size of the screening equipment is calculated to ensure the coordinated optimization of the crushing and screening links; the adjustment of the screen size follows certain rules, such as appropriately reducing the screen size when the crushing speed increases to maintain the consistency of product specifications; generate complete operating parameter adjustment instructions, including the precise speed setting value of the crushing equipment, the screen size adjustment value of the screening equipment, the adjustment time schedule and the expected effect description; design a smooth transition mechanism to avoid the impact of parameter mutations on equipment and production, such as step-by-step adjustment or gradual adjustment strategy; after the adjustment is implemented, the system continues to monitor changes in particle size distribution to form a closed-loop control and continuously optimize the operating parameters.

[0049] This method achieves precise adjustment of crushing equipment operating parameters through a predictive model and feedback correction mechanism. The predictive model provides a forward-looking assessment of the adjustment effect, while the feedback correction factor ensures the rationality of the adjustment range, avoiding the production fluctuations and resource waste that can occur with traditional trial-and-error adjustment methods. In particular, the coordinated optimization of crushing and screening equipment parameters forms an integrated hierarchical control strategy, significantly improving the system's control accuracy and stability of particle size distribution.

[0050] Through the above detailed implementation method, high-precision online detection and intelligent control of the particle size distribution of the raw ore are achieved. The system overcomes the halo effect interference caused by fine dust attached to the edges of particles in traditional methods, and obtains accurate particle boundary data through innovative light field processing and boundary reconstruction algorithms. Based on multi-dimensional geometric feature analysis, the system generates an enhanced particle size distribution curve containing size, shape and complexity information, providing comprehensive data support for graded control. The dynamic control module realizes the coordinated optimization of the parameters of the crushing equipment and screening equipment through a predictive model and feedback mechanism, forming a high-precision closed-loop control. Compared with traditional methods, this system shows higher detection accuracy and control stability in complex industrial environments.

[0051] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0052] It should be noted that the formulas in this manual are all dimensionless and calculated numerically. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0053] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. Online laser detection and classification control system for crushed ore particle size distribution, characterized by: include: Laser scanning module, used to perform online laser scanning on the conveying flow field of the crushed ore to obtain dynamic scattered light field data containing the edge features of the ore particles; a light field preprocessing module, configured to identify halo effect areas and generate halo effect compensation coefficients based on the non-uniform characteristics of light intensity distribution in the dynamic scattered light field data; an edge reconstruction module for reconstructing the boundaries of the halo effect region at the edge of the particle in the dynamic scattering light field data based on the halo effect compensation coefficient and the geometric constraint condition of laser scattering, so as to obtain accurate particle boundary data; A particle size distribution analysis module is used to extract geometric characteristic parameters of the raw ore particles based on the precise particle boundary data and generate a real-time particle size distribution curve; A classification strategy generation module is used to determine the classification control strategy of the raw ore particles according to the deviation characteristics of the real-time particle size distribution curve and the preset particle size classification standard; The dynamic control module is used to adjust the operating parameters of the crushing equipment in real time based on the hierarchical control strategy to optimize the particle size distribution of the raw ore particles.

2. The system according to claim 1, wherein: The method for identifying the halo effect area includes: Extracting light intensity gradient mutation regions as candidate halo effect regions based on the gradient characteristics of light intensity distribution in the dynamic scattered light field data; Calculating a radial range of light intensity diffusion according to radial diffusion characteristics of light intensity in the candidate halo effect region; determining a spatial boundary of a halo effect region according to the radius range and the incident angle of the laser beam; The halo effect compensation coefficient is generated according to the non-uniformity of the light intensity distribution within the spatial boundary.

3. The system according to claim 1, wherein: The boundary reconstruction method includes: adjusting the light intensity distribution of the halo effect area in the dynamic scattered light field data according to the halo effect compensation coefficient to generate compensated light field data; constructing a candidate set of edge points based on light intensity gradient characteristics of particle edges in the compensated light field data; According to the geometric constraint conditions of the laser scattering, edge points that meet the continuity conditions in the candidate set are screened to form an initial boundary curve; According to the curvature change trend of the initial boundary curve, the curvature mutation points are eliminated to obtain the accurate particle boundary data.

4. The system according to claim 1, wherein: The method for extracting the geometric feature parameters comprises: Constructing a boundary polygon model of the raw ore particles based on the precise particle boundary data; Calculating the equivalent diameter, shape factor, and boundary complexity of the ore particles based on the vertex distribution characteristics of the boundary polygon model, wherein the shape factor is determined by the ratio of the perimeter to the area of ​​the boundary polygon, and the boundary complexity is calculated by the Fourier descriptor of the boundary curve; The real-time particle size distribution curve is generated according to the statistical distribution of the equivalent diameter, shape factor and boundary complexity.

5. The system according to claim 1, wherein: The method for determining the hierarchical control strategy includes: Extracting peak characteristics and width characteristics of the particle size distribution according to the real-time particle size distribution curve; determining a deviation direction of the particle size distribution according to a deviation between the peak characteristic and the preset particle size classification standard; Determining the uniformity level of the particle size distribution based on the degree of matching between the width feature and the preset particle size classification standard; The hierarchical control strategy is generated according to the offset direction and the uniformity level, wherein the hierarchical control strategy includes a rotation speed adjustment amplitude of the crushing device and a sieve hole size adjustment step of the screening device.

6. The system according to claim 2, wherein: The method for calculating the radius range of the light intensity diffusion includes: constructing a light intensity attenuation curve according to the radial distribution of light intensity within the candidate halo effect region; determining a diffusion boundary of the halo effect according to the inflection point position of the light intensity attenuation curve; Calculating the radius range of the light intensity diffusion according to the distance between the diffusion boundary and the incident point of the laser beam; According to the dynamic change trend of the radius range, the pseudo halo effect area caused by environmental noise is eliminated.

7. The system according to claim 3, wherein: The method for eliminating curvature mutation points includes: Extracting candidate mutation points whose curvature values ​​exceed a preset curvature threshold according to the local curvature of the initial boundary curve; Determining whether the candidate mutation point is caused by a halo effect residual based on the consistency of the light intensity gradient of the boundary points before and after the candidate mutation point; If the candidate mutation point is caused by the halo effect residual, the candidate mutation point is smoothed by interpolation; If the candidate mutation point is not caused by the halo effect residual, the candidate mutation point is retained as the true feature point of the particle boundary.

8. The system according to claim 4, wherein: The method for generating the real-time particle size distribution curve comprises: constructing a histogram of particle size distribution according to the statistical distribution of the equivalent diameters; performing shape correction on the histogram according to the distribution characteristics of the shape factors; performing complexity smoothing on the histogram according to the distribution characteristics of the boundary complexity; The real-time particle size distribution curve is fitted based on the corrected and smoothed histogram.

9. The system according to claim 5, characterized in that The method for determining the uniformity level includes: Calculating a coefficient of variation of the width of the particle size distribution based on the width characteristic; determining an initial uniformity level of the particle size distribution based on a ratio of the width variation coefficient to a preset uniformity threshold; determining a multimodal characteristic of the particle size distribution based on whether a secondary peak exists in the real-time particle size distribution curve; If multimodal characteristics exist, the initial uniformity level is adjusted according to the height and width of the secondary peak to obtain a final uniformity level.

10. The system according to claim 1, wherein: The real-time adjustment method of the operating parameters includes: Determine the adjustment direction and adjustment range of the crushing equipment speed according to the hierarchical control strategy; According to the adjustment direction, predict the change trend of the particle size distribution of the raw ore particles after the rotation speed is adjusted; Calculating a feedback correction coefficient for speed adjustment based on a deviation between the change trend and the preset particle size classification standard; According to the feedback correction coefficient, the speed adjustment range is dynamically adjusted, and the sieve hole size of the screening equipment is synchronously updated to obtain optimized operating parameters.

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