A visual sorting method and system for photovoltaic quartz sand

Abstract

This invention discloses a visual sorting method and system for photovoltaic quartz sand, relating to the field of visual material sorting. It includes a single-particle feeding and arrangement module for continuously and uniformly conveying photovoltaic quartz sand material through a vibrating feed and guiding alignment mechanism, transforming the material into a stable, discrete, equidistant, and ordered arrangement of single particles; and a capture module for acquiring multispectral images of the photovoltaic quartz sand material with overlapping conveying path boundaries, obtaining raw data of multidimensional optical imaging of the material's surface and subsurface layers. This invention, through simultaneous acquisition of multidimensional optical data from the material's surface and subsurface layers via multispectral imaging, effectively captures subtle impurity information that is difficult to detect using traditional methods. It can accurately identify various trace impurity elements such as Al, Fe, Ca, Ti, Na, and K, enabling efficient separation and collection of qualified and unqualified materials, effectively improving the accuracy, efficiency, and stability of photovoltaic quartz sand sorting.

Description

Technical Field

[0001] This invention relates to the field of visual material selection technology, specifically to a visual sorting method and system for photovoltaic quartz sand. Background Technology

[0002] Photovoltaic quartz sand is a core high-purity raw material in the photovoltaic industry chain. It is mainly composed of silicon dioxide with a purity of over 99.99% and extremely low impurity content. It is mainly used to manufacture quartz crucibles required for pulling monocrystalline silicon and is also a key raw material for photovoltaic glass. Its purity and quality directly affect the quality of silicon wafers, photoelectric conversion efficiency, and module life.

[0003] Patent application No. 201911174246.X discloses a method, equipment, and storage medium for detecting inclusion content in quartz sand. This application aims to solve the problem that "existing methods for detecting inclusion content in quartz sand mainly use a polarizing microscope to observe quartz sand and determine the proportion of quartz sand particles containing inclusions in the field of view. However, existing detection methods are slow in detecting inclusion content in quartz sand."

[0004] However, existing visual sorting methods are difficult to simultaneously identify multiple trace impurities such as Al, Fe, and Ca in photovoltaic quartz sand, and cannot meet the preset standard for the total content of impurity elements in photovoltaic quartz sand (≤25μg / g).

[0005] To address this, we propose a visual sorting method and system for photovoltaic quartz sand. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a visual sorting method and system for photovoltaic quartz sand, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a visual sorting system for photovoltaic quartz sand, comprising: The system comprises the following modules: a single-particle feeding and arrangement module, which continuously and uniformly conveys photovoltaic quartz sand material through a vibratory feeding and guiding alignment mechanism, transforming the material into a stable arrangement of discrete, equidistant, and ordered single particles; a capture module, which acquires multispectral images of the photovoltaic quartz sand material with overlapping conveying path boundaries, obtaining raw multi-dimensional optical imaging data of the material's surface and subsurface layers; a preprocessing module, which performs noise reduction, distortion correction, and format unification on the acquired raw multi-dimensional optical imaging data, outputting standardized image data to be analyzed; an extraction module, which performs pixel-level feature extraction on the standardized image data, obtaining feature parameters of the entire area of ​​each single particle to construct a corresponding feature dataset; a generation module, which performs correlation identification between optical features and trace impurity elements based on the feature dataset of each single particle, and simultaneously generates corresponding sorting control commands based on the identification results; and a sorting and discharging module, which receives sorting control commands and performs sorting processing on the corresponding single photovoltaic quartz sand material, separating qualified and unqualified materials for discharge; wherein, trace impurities include at least Al, Fe, Ca, Ti, Na, and K.

[0008] Furthermore, the single-particle feeding and arrangement module includes a vibrating feeding unit, a guiding and aligning unit, and a uniform speed conveying unit connected in sequence. The vibrating feeding unit is used to disperse and feed photovoltaic quartz sand material with preset vibration parameters. The guiding and aligning unit is used to regulate the posture and constrain the path of the dispersed material through a guiding channel that matches the particle size of the single material, so that the material forms a single column along a single conveying direction. The uniform speed conveying unit is used to convey the material in the single column at a preset constant linear velocity. By matching and controlling the conveying speed and the feeding frequency, the spacing between adjacent materials in the column is stabilized within a preset spacing range, forming a stable arrangement state of discrete single particles and equidistant order.

[0009] Furthermore, the capture module includes several linear array multispectral imaging units, a synchronization triggering unit, and a dark-field imaging cavity that coincide with the boundary of the conveying path. The synchronization triggering unit generates a synchronization trigger signal based on the real-time conveying speed of the uniform conveying unit and the arrangement position of individual material particles, controlling the exposure sequence of all linear array multispectral imaging units to be completely matched with the material conveying sequence. The linear array multispectral imaging unit includes at least a visible light band imaging group corresponding to the characteristics of the photovoltaic quartz sand matrix, and a near-infrared and short-wave infrared band imaging group corresponding to the characteristic absorption peaks of each trace impurity element. The optical axis of each imaging unit is perpendicular to the material conveying plane, and the imaging field of view completely covers the boundary of the material conveying path. The dark-field imaging cavity is used to isolate ambient stray light interference and provide a constant preset illumination environment for the imaging process, so that the acquired multidimensional optical imaging raw data simultaneously includes diffuse reflection imaging data of the material surface and transmission imaging data of the subsurface. Furthermore, during the preprocessing module's operation phase, based on the synchronous trigger signal, pixel-level spatiotemporal registration is performed on the imaging data of the same single particle material acquired in different bands, ensuring that the pixel coordinates of the imaging data of the same material in all bands correspond one-to-one. Then, based on the pre-calibrated imaging system distortion parameters, geometric distortion correction is performed on the registered imaging data of each band, and band-adapted noise filtering is performed on the corrected imaging data. Finally, the grayscale value range of the imaging data of all bands is mapped to a unified preset value range to output standardized image data to be analyzed.

[0010] Furthermore, the extraction module performs pixel-level feature extraction and global feature extraction to construct a feature dataset for a single particle. Pixel-level feature extraction is used to extract multi-band optical feature parameters for each pixel within the imaging area of ​​a single particle, i.e., the impurity feature saliency factor of the pixel is calculated using the following formula: ; In the formula: The significance factor of impurity features for pixel (x,y); This represents the total number of characteristic bands. Let be the standardized deviation of pixel (x,y) in the i-th feature band; Let (x, y) be the standardized gray value of pixel (x, y) in the i-th feature band; The reference gray value is the pre-calibrated value of the photovoltaic quartz sand pure matrix in the i-th characteristic band. denoted as the calibration standard deviation of the grayscale value of the pure matrix in the i-th characteristic band; This is the average of the absolute values ​​of the standardized deviations of the pixel across all feature bands. This is a preset minimum constant; Global feature extraction is based on the saliency factor of impurity features of all pixels. It extracts the spatial distribution features, impurity proportion features, and impurity band response features of the entire region of a single particle. All pixel-level feature parameters are integrated with global feature parameters to construct the feature dataset corresponding to the single particle.

[0011] Furthermore, in the correlation identification stage between optical features and trace impurity elements performed by the generation module, for each preset target trace impurity element, based on the feature dataset of a single particle, the correlation identification factor corresponding to that element is calculated. The presence and content level of the element are then determined by comparing the correlation identification factor with a preset identification threshold. The correlation identification factor for the j-th target trace impurity element is: ; In the formula: , is the correlation identification factor for the j-th target trace impurity element; This represents the total number of pixels in the imaging area of ​​a single particle. The significance factor of impurity features for pixel (x,y) within a single particle; A pre-calibrated set of exclusive characteristic bands that match the characteristic absorption peaks of the j-th target trace impurity element; This is a band response indicator function, which is used when pixel (x,y) is in... When the standardized deviation of all characteristic bands is greater than the preset deviation threshold, the function takes the value of 1; otherwise, it takes the value of 0. It is the average of the absolute values ​​of the standardized deviation of all pixels in the imaging region of a single particle under the i-th feature band; For the j-th target trace impurity element in The average value of the pre-calibrated reference deviation; This is a preset minimum constant.

[0012] Furthermore, based on the results of the correlation identification, the generation module performs a qualification determination on individual materials and generates corresponding sorting control instructions. The qualification determination follows the following rules: when the correlation identification factor of any target trace impurity element corresponding to a single material exceeds the preset threshold range, the single material is determined to be unqualified; otherwise, it is determined to be qualified. The sorting control instructions include the material's location information, timing information, and sorting action information. The location information and timing information are generated in real time based on the material's conveying speed and arrangement position to ensure that the sorting action is completely matched with the arrival time of the target material. The sorting action information is used to control the sorting and discharge module to perform the corresponding sorting action for qualified or unqualified materials.

[0013] Furthermore, the sorting and discharging module includes a high-speed air blowing actuator, a discharging and diverting chamber, and a collection unit. The high-speed air blowing actuator receives sorting control commands and, based on the timing and action information within the commands, outputs a high-pressure air blowing action of corresponding duration the instant the target non-conforming material arrives at the blowing station, changing the falling trajectory of the non-conforming material. The discharging and diverting chamber is equipped with mutually isolated qualified material channels and non-conforming material channels. Qualified material enters the qualified material channel along its natural falling trajectory, while non-conforming material, whose trajectory has been changed by air blowing, enters the non-conforming material channel. The collection unit is connected to the qualified material channel and the non-conforming material channel respectively to complete the classified collection of qualified and non-conforming materials.

[0014] Furthermore, the single-particle feeding and arrangement module is connected to the capture module via industrial real-time wired communication. The capture module is connected to the preprocessing module and the extraction module via industrial real-time wired communication. The extraction module is connected to the generation module and the sorting and discharge module via industrial real-time wired communication.

[0015] On the other hand, a visual sorting method for photovoltaic quartz sand includes: Photovoltaic quartz sand material is dispersed by vibration feeding, guided and constrained by alignment, and controlled by uniform conveying to form a stable arrangement of discrete, equidistant, and ordered single particles. By matching the conveying speed and feeding frequency, the spacing between adjacent materials in the queue is stabilized within a preset range. A multispectral imaging unit coinciding with the boundary of the conveying path acquires multi-band optical imaging data of the material in a dark environment, synchronously triggering the matching of the imaging time sequence with the material conveying time sequence to obtain raw multi-dimensional optical imaging data of the material's surface and subsurface layers. Pixel-level spatiotemporal registration and geometric distortion correction are performed on the multi-band imaging data, and band-adaptive noise reduction and unified grayscale mapping are applied to the corrected data to output standardized unsolved problems. The system analyzes image data; performs pixel-level feature extraction on standardized images and calculates the impurity feature significance factor, marking effective impurity pixels. Simultaneously, it extracts the spatial distribution, proportion, and band response features of impurities based on the effective impurity pixels, integrating them to form a single-particle material feature dataset. Based on the feature dataset, it calculates the correlation identification factor of each trace impurity element, determines the presence and content level of the element, completes the qualification judgment of single-particle materials based on the impurity identification results, and generates corresponding sorting control commands in real time. According to the sorting control commands, it triggers a high-speed air blowing action to change the falling trajectory of unqualified materials, so that qualified and unqualified materials are guided separately through the diversion cavity and collected by the collection unit.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention utilizes multispectral imaging to simultaneously acquire multidimensional optical data from the surface and subsurface layers of materials, effectively capturing minute impurity information that is difficult to detect using traditional methods. It can accurately identify various trace impurity elements such as Al, Fe, Ca, Ti, Na, and K. During system operation, after image data noise reduction, distortion correction, and spatiotemporal registration, the pixel details of material edges and impurities are fully preserved. Based on pixel-level and global feature extraction, the spatial distribution, proportion, and band response characteristics of impurities are accurately analyzed to accurately determine the presence and content level of impurities, eliminating misjudgment and missed detection of impurities. Furthermore, the sorting action is precisely matched with the material conveying sequence, and the trajectory of unqualified materials is adjusted in real time through high-speed air blowing, enabling efficient diversion and collection of qualified and unqualified materials, effectively improving the accuracy, efficiency, and stability of photovoltaic quartz sand sorting. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of a visual sorting system for photovoltaic quartz sand. Figure 2 This is a flowchart illustrating a visual sorting method for photovoltaic quartz sand. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment provides a visual sorting system for photovoltaic quartz sand, such as... Figure 1 As shown, it includes: The single-particle feeding and arrangement module is used to continuously and uniformly convey photovoltaic quartz sand material through a vibratory feeding and guiding alignment mechanism, transforming the material into a stable arrangement state of discrete single particles with equal spacing. The single-particle feeding and arrangement module includes a vibration feeding unit, a guiding and aligning unit, and a uniform speed conveying unit connected in sequence; The vibration feeding unit is used to disperse and feed photovoltaic quartz sand material with preset vibration parameters to break up the agglomeration and adhesion between materials; The guiding and aligning unit is used to straighten the posture and constrain the path of the dispersed material through a guiding flow channel that matches the particle size of the single material, so that the material forms a single queue along a single conveying direction. The uniform speed conveying unit is used to convey materials in a single column at a preset constant linear speed. By matching and controlling the conveying speed and the feeding frequency, the spacing between adjacent materials in the column is stabilized within a preset spacing range, forming a stable arrangement state of discrete single particles and equidistant order. The capture module is used to acquire multispectral images of photovoltaic quartz sand material with single-particle arrangement, which coincide with the boundary of the conveying path, and obtain raw data of multi-dimensional optical imaging of the material surface and subsurface. The capture module includes several linear multispectral imaging units that coincide with the boundary of the transport path, a synchronous triggering unit, and a dark-field imaging cavity; The synchronous triggering unit is used to generate a synchronous triggering signal based on the real-time conveying speed of the uniform conveying unit and the arrangement position of the single material, so as to control the exposure sequence of all linear array multispectral imaging units to be completely matched with the material conveying sequence. The linear array multispectral imaging unit includes at least a visible light band imaging group corresponding to the characteristics of photovoltaic quartz sand matrix, a near-infrared-short-wave infrared band imaging group corresponding to the characteristic absorption peaks of each trace impurity element, and a dedicated narrowband filter imaging unit adapted to Na and K light elements. The optical axis of each imaging unit is perpendicular to the material conveying plane, and the imaging field of view completely covers the boundary of the material conveying path. The dark-field imaging cavity is used to isolate ambient stray light interference and provide a constant preset illumination environment for the imaging process. This allows the acquired multi-dimensional optical imaging raw data to simultaneously include diffuse reflection imaging data of the material surface and transmission imaging data of the subsurface. Narrow-band filtering enhances the spectral response of light elements, and the baseline subtraction algorithm ensures a trace detection accuracy of ≤25μg / g. The preprocessing module is used to perform noise reduction, distortion correction, and format unification processing on the acquired multi-dimensional optical imaging raw data, and output standardized image data to be analyzed. During the preprocessing module operation phase, based on the synchronous trigger signal, the imaging data of the same single particle material collected in different bands are spatiotemporally registered at the pixel level, so that the pixel coordinates of the imaging data of all bands of the same material correspond one-to-one. Then, based on the pre-calibrated imaging system distortion parameters, geometric distortion correction is performed on the registered imaging data of each band to eliminate pixel offset caused by imaging angle and optical lens. The corrected imaging data is then subjected to band-adaptive noise filtering to preserve pixel details of material edges and impurity features. Finally, the grayscale value range of the imaging data of all bands is mapped to a unified preset value range, and real-time adaptive calibration of pure matrix reference is performed simultaneously to output standardized image data to be analyzed. It should be noted that adaptive calibration can eliminate optical differences in quartz sand matrices from different origins, eliminating the need for repeated calibration. The extraction module is used to extract pixel-level features from the standardized image data to obtain the feature parameters of the entire area of ​​a single particle of material in order to construct the corresponding feature dataset. The extraction module performs pixel-level feature extraction and global feature extraction to construct a feature dataset for a single particle. Pixel-level feature extraction is used to extract multi-band optical feature parameters for each pixel within the imaging area of ​​a single particle, i.e., the impurity feature saliency factor of the pixel is calculated using the following formula: ; In the formula: The significance factor of impurity features for pixel (x,y); This represents the total number of characteristic bands. Let be the standardized deviation of pixel (x,y) in the i-th feature band; Let (x, y) be the standardized gray value of pixel (x, y) in the i-th feature band; The reference gray value is the pre-calibrated value of the photovoltaic quartz sand pure matrix in the i-th characteristic band. denoted as the calibration standard deviation of the grayscale value of the pure matrix in the i-th characteristic band; This is the average of the absolute values ​​of the standardized deviations of the pixel across all feature bands. This is a preset minimum constant used to avoid the denominator being 0; The above formula first standardizes the gray values ​​of a single pixel in each feature band to eliminate the differences in optical response between different bands. Then, it quantifies the overall deviation of the pixel in the full feature band through root mean square operation. At the same time, it introduces the ratio of the range to the mean of the deviation between bands to amplify the multi-band response differences between impurity pixels and pure quartz matrix, effectively highlighting the weak optical signals brought by trace impurities. In addition, it avoids the calculation risk of the denominator being zero by using a minimum constant. It can accurately quantify the significance of impurity features of a single pixel. Even the subtle optical changes brought by trace impurities in the subsurface layer of the material can be effectively captured, thus providing a pixel-level accurate quantitative basis for subsequent full-domain feature extraction and element correlation identification of impurities. Global feature extraction is used to extract the spatial distribution features, proportion features, and band response features of impurities in the entire region of a single grain of material based on the impurity feature saliency factors of all pixels. All pixel-level feature parameters are integrated with global feature parameters to construct the feature dataset corresponding to the single grain of material. Among them, the extraction of impurity spatial distribution characteristics is as follows: First, within the imaging area of ​​a single particle, pixels with impurity feature significance factors greater than a preset significance threshold are marked as valid impurity pixels. Connectivity analysis is performed on all valid impurity pixels to divide at least one impurity connected domain. Then, the contour size, centroid position, and spacing parameters of each impurity connected domain are extracted. The number of impurity connected domains, spatial distribution dispersion, and aggregation parameters are counted to form the impurity spatial distribution characteristics of a single particle. Extraction of impurity proportion features: The total number of effective impurity pixels in the imaging area of ​​a single particle is counted, and the area ratio of the total number of effective impurity pixels to the total number of pixels in the imaging area of ​​a single particle is calculated. At the same time, the cumulative value of the impurity feature significance factor of all effective impurity pixels is calculated as the ratio of the cumulative total value of the impurity feature significance factor of all pixels in the imaging area of ​​a single particle to the total value of the impurity feature significance factor of all pixels in the imaging area of ​​a single particle. The impurity proportion features of a single particle are formed by combining the two proportion parameters. Extraction of impurity band response features: For all effective impurity pixels, the mean, extreme values ​​and distribution dispersion of their standardized deviations under each feature band are statistically analyzed. The response difference parameters between different feature bands are extracted. At the same time, the exclusive feature bands corresponding to each trace impurity element are matched, and the statistical features of the standardized deviations within the exclusive feature bands are statistically analyzed to form the impurity band response features of a single particle. The generation module is used to perform correlation identification between optical features and trace impurity elements based on the feature dataset of single particles, and simultaneously generate corresponding sorting control instructions based on the identification results. The generation module performs the correlation identification stage between optical features and trace impurity elements. For each preset target trace impurity element, based on the feature dataset of a single particle, the correlation identification factor corresponding to the element is calculated. By comparing the correlation identification factor with the preset identification threshold, the existence of the element and the content level are determined. The correlation identification factor for the j-th target trace impurity element is: ; In the formula: , is the correlation identification factor for the j-th target trace impurity element; This represents the total number of pixels in the imaging area of ​​a single particle. The significance factor of impurity features for pixel (x,y) within a single particle; A pre-calibrated set of exclusive characteristic bands that match the characteristic absorption peaks of the j-th target trace impurity element; This is a band response indicator function, which is used when pixel (x,y) is in... When the standardized deviation of all characteristic bands is greater than the preset deviation threshold, the function takes the value of 1; otherwise, it takes the value of 0. It is the average of the absolute values ​​of the standardized deviation of all pixels in the imaging region of a single particle under the i-th feature band; For the j-th target trace impurity element in The average value of the pre-calibrated reference deviation; This is a preset minimum constant; The above formula filters out effective pixels that match the characteristic absorption characteristics of the element through the band response indicator function, and completes the weighted statistics of the entire domain of a single material by combining the pixel-level impurity characteristic significance factor. At the same time, an exponential term is introduced to amplify the response difference characteristics between the element's exclusive bands, deeply binding the optical characteristics of the material with the spectral absorption characteristics of specific trace impurity elements. This can accurately quantify the overall occurrence level of the corresponding impurity element in a single material, effectively avoiding the identification interference caused by other impurities or matrix optical fluctuations, and providing a highly distinguishable quantitative core indicator for the determination of the existence and content level of impurity elements. Existence identification follows this rule: For the j-th target trace impurity element, assign its corresponding correlation identification factor. Compare with the preset existence determination threshold; if If the value is greater than or equal to the preset presence threshold, then the target trace impurity element is determined to exist in the single particle of material; if... If the amount is less than the preset existence threshold, it is determined that the target trace impurity element does not exist in the single particle material. Content level determination follows this procedure: For a target trace impurity element that has been identified as present, at least two progressively increasing content level thresholds are pre-set. Adjacent content level thresholds form corresponding content level intervals, and each content level interval corresponds one-to-one with the content level of the element. The associated identification factor corresponding to the element is then used. Compare with the threshold values ​​for each content level to determine The content level range into which the sample falls is determined as the content level of the target trace impurity element in the single particle. The existence determination threshold and the content level threshold are both set based on the correspondence between the pre-calibrated correlation identification factor and the actual content of the trace impurity element; Based on the results of the association recognition, the generation module performs a qualification judgment on individual materials and generates corresponding sorting control instructions; The qualification determination shall be subject to: When the correlation identification factor of any target trace impurity element corresponding to a single particle exceeds the preset threshold range, the single particle is determined to be unqualified material; otherwise, it is determined to be qualified material. The sorting control instructions include the material's location information, timing information, and sorting action information. The location information and timing information are generated in real time based on the material's conveying speed and arrangement position, which are used to ensure that the sorting action is completely matched with the arrival time of the target material. The sorting action information is used to control the sorting and discharge module to perform the sorting action of the corresponding qualified or unqualified material. The sorting and discharge module is used to receive sorting control commands and perform sorting processing on the corresponding single photovoltaic quartz sand material, so that qualified materials and unqualified materials are discharged separately. The sorting and discharging module includes a high-speed air blowing execution unit, a discharging and diverting cavity, and a material collection unit; The high-speed air blowing actuator is used to receive sorting control commands. Based on the timing and action information in the commands, it outputs a high-pressure air blowing action of corresponding duration the instant the target non-conforming material arrives at the blowing station, thereby changing the falling trajectory of the non-conforming material. The discharge diversion chamber is equipped with mutually isolated qualified material flow channels and unqualified material flow channels. Qualified material enters the qualified material flow channel along the natural falling trajectory, while unqualified material, whose trajectory is changed by air blowing, enters the unqualified material flow channel. The collection unit is connected to the qualified material flow channel and the unqualified material flow channel respectively to complete the classified collection of qualified and unqualified materials; Among them, trace impurities include at least Al, Fe, Ca, Ti, Na, and K; The single-particle feeding and arrangement module is connected to the capture module via industrial real-time wired communication. The capture module is connected to the preprocessing module and the extraction module via industrial real-time wired communication. The extraction module is connected to the generation module and the sorting and discharge module via industrial real-time wired communication.

[0022] In this embodiment, the single-particle feeding and arrangement module continuously and uniformly conveys the photovoltaic quartz sand material through a vibratory feeding and guiding alignment mechanism, transforming the material into a stable arrangement of discrete, equidistant, and ordered single particles. The capture module then performs multispectral image acquisition of the photovoltaic quartz sand material, coinciding with the boundary of the conveying path, obtaining raw multi-dimensional optical imaging data of the material's surface and subsurface layers. Simultaneously, the preprocessing module performs noise reduction, distortion correction, and format unification on the acquired raw multi-dimensional optical imaging data, outputting standardized image data to be analyzed. The extraction module then performs pixel-level feature extraction on the standardized image data to obtain feature parameters for the entire area of ​​each single particle, constructing a corresponding feature dataset. The generation module further performs correlation identification between optical features and trace impurity elements based on the single-particle feature dataset, simultaneously generating corresponding sorting control commands based on the identification results. Finally, the sorting and discharge module receives the sorting control commands and performs sorting processing on the corresponding single photovoltaic quartz sand material, separating qualified and unqualified materials for discharge.

[0023] In the above embodiments, the system can organize photovoltaic quartz sand into single-particle ordered state for transport. Through multispectral imaging, it can accurately collect optical information of the surface and subsurface layers of the material. After precise image processing, it can extract impurity features at the pixel level, accurately identify trace impurities such as aluminum, iron, calcium, titanium, sodium, and potassium, and determine their content. It can automatically complete the sorting of qualified and unqualified materials, thereby improving the sorting accuracy and efficiency of quartz sand, avoiding errors in manual sorting, strictly ensuring the purity of raw materials, and adapting to the stringent requirements of the photovoltaic industry for raw materials.

[0024] Referring to the system in the above embodiments, the following is an application example of the system: To meet the production requirements of high-purity photovoltaic quartz sand, a photovoltaic new material manufacturer has implemented this system. It enables automated and precise sorting of six trace impurities—Al, Fe, Ca, Ti, Na, and K—in quartz sand, completely replacing the traditional sorting method and improving material purity and production efficiency.

[0025] After the operation starts, the quartz sand material first enters the single-particle feeding and arrangement module. The module breaks up the agglomerated and sticky material through the vibrating feeding unit, and then uses the guide channel matched with the quartz sand particle size to regulate the material posture and constrain the conveying path, so that the material forms a single queue. Finally, it is stably conveyed by the uniform speed conveying unit, so that the adjacent materials in the queue maintain an equidistant and discrete stable arrangement, providing the basic conditions for subsequent accurate detection.

[0026] The neatly arranged materials then enter the capture module. In the dark-field imaging cavity, which is isolated from ambient stray light, multiple linear array multispectral imaging units, under the control of the synchronous triggering unit, match the material conveying sequence to complete the exposure and simultaneously acquire visible light, near-infrared and short-wave infrared images. This allows for the complete acquisition of multi-dimensional optical raw data of diffuse reflection and subsurface transmission of the quartz sand surface, with the imaging range fully covering the material conveying path.

[0027] The raw imaging data is transmitted to the preprocessing module. The module first performs pixel-level spatiotemporal registration of the imaging data of the same material in different bands, then corrects the geometric distortion according to the precalibrated parameters, filters out noise and retains impurities and material edge details, and finally unifies the gray value range of each band image and outputs standardized image data to be analyzed.

[0028] The extraction module performs pixel-level and global feature extraction on the standardized image. First, it calculates the impurity feature saliency factor for each pixel and marks the effective impurity pixels. Then, it obtains the spatial distribution features such as impurity contour size, centroid position, and distribution dispersion through connected component analysis. It calculates the impurity pixel area ratio and the cumulative ratio of saliency factors to form the impurity ratio feature. At the same time, it statistically analyzes the response differences of each band and the specific band deviation features, and integrates them to form a complete feature dataset for a single particle material.

[0029] The generation module calculates the correlation identification factors of six trace impurity elements based on the feature dataset, and determines the presence and content level of each impurity according to the preset threshold. During the operation, some quartz sand is judged as unqualified material by the system because the correlation identification factor of Fe element exceeds the qualified threshold range. At the same time, the system combines the material conveying position and time sequence to generate accurate sorting control instructions in real time.

[0030] After receiving the sorting instruction, the high-speed air blowing execution unit accurately matches the arrival time of the unqualified materials and instantly outputs high-pressure air to change their falling trajectory. The qualified materials enter the qualified material flow channel of the diversion cavity along the natural falling path, while the unqualified materials deflected by the air blow enter the independently isolated unqualified material flow channel. Finally, the collection unit completes the classification and collection of the two types of materials.

[0031] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of the visual sorting system for photovoltaic quartz sand in Example 1 is provided below: A visual sorting method for photovoltaic quartz sand includes: Photovoltaic quartz sand material is dispersed by vibration feeding, guided and constrained by alignment and uniform conveying control, forming a stable arrangement of discrete single particles with equal spacing. By matching the conveying speed and feeding frequency, the spacing between adjacent materials in the queue is stabilized within a preset range. By using a multispectral imaging unit that coincides with the boundary of the conveying path, multi-band optical imaging data of materials are collected in a dark field environment. Simultaneously, the imaging timing is matched with the material conveying timing to obtain raw multi-dimensional optical imaging data of the material surface and subsurface. Pixel-level spatiotemporal registration and geometric distortion correction are performed on multi-band imaging data, and band-adaptive noise reduction and unified grayscale mapping are applied to the corrected data to output standardized image data to be analyzed. Pixel-level feature extraction is performed on the standardized image and the impurity feature significance factor is calculated. Valid impurity pixels are marked. At the same time, the spatial distribution, proportion and band response features of impurities are extracted based on the valid impurity pixels and integrated to form a single-particle material feature dataset. Based on the feature dataset, the correlation identification factor of each trace impurity element is calculated to determine the presence and content level of the element. Based on the impurity identification results, the qualification of single material is determined and corresponding sorting control instructions are generated in real time. The high-speed air blowing action is triggered by the sorting control command, which changes the falling trajectory of the unqualified materials, so that the qualified materials and unqualified materials are guided separately through the diversion cavity and collected by the collection unit.

[0032] In summary, the system of this invention simultaneously acquires multidimensional optical data of the surface and subsurface layers of materials through multispectral imaging, effectively capturing subtle impurity information that is difficult to detect using traditional methods. It can accurately identify various trace impurity elements such as Al, Fe, Ca, Ti, Na, and K. During system operation, after imaging data noise reduction, distortion correction, and spatiotemporal registration, the pixel details of the material edges and impurities are completely preserved. Based on pixel-level and global feature extraction, the spatial distribution, proportion, and band response characteristics of impurities are accurately analyzed to accurately determine the presence and content level of impurities, eliminating misjudgment and missed detection of impurities. Furthermore, the sorting action is precisely matched with the material conveying sequence, and the trajectory of unqualified materials is adjusted in real time through high-speed air blowing, enabling efficient diversion and collection of qualified and unqualified materials, effectively improving the accuracy, efficiency, and stability of photovoltaic quartz sand sorting.

[0033] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A visual sorting system for photovoltaic quartz sand, characterized in that, The method comprises the following steps: A single-grain feeding arrangement module is used to continuously and uniformly transport photovoltaic quartz sand materials through vibration feeding and a guide alignment mechanism, and convert the materials into a stable arrangement state of single-grain dispersion, equidistance and order; A capture module is used to collect multi-spectral images of the single-grain arrangement photovoltaic quartz sand materials at the boundary of the transport path, and obtain multi-dimensional optical imaging original data of the surface and subsurface layers of the materials; A preprocessing module is used to perform noise reduction, distortion correction and format unification processing on the collected multi-dimensional optical imaging original data, and output standardized image data to be analyzed; An extraction module is used to extract pixel-level features from the standardized image data, and obtain feature parameters of the whole region of the single-grain materials to construct a corresponding feature data set; A generation module is used to perform associated identification of optical features and trace impurity elements based on the feature data set of the single-grain materials, and simultaneously generate corresponding sorting control instructions according to the identification results; A sorting discharge module is used to receive the sorting control instructions, perform sorting processing on the corresponding single-grain photovoltaic quartz sand materials, and classify and discharge qualified materials and unqualified materials. The trace impurities at least include Al, Fe, Ca, Ti, Na and K.

2. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The single-grain feeding arrangement module comprises a vibration feeding unit, a guide alignment unit and a uniform transport unit connected in sequence. The vibration feeding unit is used to disperse and feed the photovoltaic quartz sand materials at a preset vibration parameter. The guide alignment unit is used to posture and align the dispersed materials through a guide flow channel matched with the particle size of the single-grain materials, and form a single-column queue along a unique transport direction. The uniform transport unit is used to transport the materials in the single-column queue at a preset constant linear speed, and stabilize the spacing between adjacent materials in the queue within a preset spacing interval through matching and regulation of the transport speed and the feeding frequency, to form a stable arrangement state of single-grain dispersion, equidistance and order.

3. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The capture module comprises a plurality of groups of line array multi-spectral imaging units coinciding with the boundary of the transport path, a synchronous triggering unit and a dark field imaging cavity. The synchronous triggering unit is used to generate a synchronous triggering signal based on the real-time transport speed of the uniform transport unit and the arrangement position of the single-grain materials, and control the exposure timing of all line array multi-spectral imaging units to completely match the material transport timing. The line array multi-spectral imaging unit at least includes a visible light band imaging group corresponding to the characteristics of the photovoltaic quartz sand substrate, a near-infrared-short wave infrared band imaging group corresponding to the characteristic absorption peaks of each trace impurity element, and a dedicated narrow-band filtering imaging unit adapted to Na and K light elements. The optical axis of each imaging unit is perpendicular to the material transport plane, and the imaging field of view completely covers the boundary of the material transport path. The dark field imaging cavity is used to isolate environmental stray light interference, and provide a constant preset light environment for the imaging process, so that the collected multi-dimensional optical imaging original data simultaneously includes diffuse reflection imaging data of the surface layer of the material and transmission imaging data of the subsurface layer.

4. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The preprocessing module operates based on a synchronous trigger signal, performing pixel-level spatiotemporal registration of imaging data of the same single particle material acquired in different bands. This ensures that the pixel coordinates of all band imaging data of the same material correspond one-to-one. Then, based on the pre-calibrated imaging system distortion parameters, geometric distortion correction is performed on the registered imaging data of each band. The corrected imaging data is then subjected to band-adaptive noise filtering. Finally, the grayscale value range of all band imaging data is mapped to a unified preset value range to output standardized image data to be analyzed.

5. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The extraction module performs pixel-level feature extraction and global feature extraction to construct a feature dataset for a single particle. The pixel-level feature extraction is used to extract multi-band optical feature parameters for each pixel within the imaging area of ​​a single particle, i.e., to calculate the impurity feature saliency factor of the pixel using the following formula: ； In the formula: is the impurity feature saliency factor of the pixel point (x, y); is the total number of feature bands; is the normalized deviation of the pixel point (x, y) in the i-th feature band; is the normalized gray value of the pixel point (x, y) in the i-th feature band; is the pre-calibrated reference gray value of the photovoltaic quartz sand pure matrix in the i-th feature band; is the calibration standard deviation of the gray value of the pure matrix in the i-th feature band; is the average value of the absolute values of the normalized deviations of the pixel point in all feature bands; is a preset minimum constant; The global feature extraction is used to extract the spatial distribution features, proportion features, and band response features of impurities in the entire region of a single grain of material based on the impurity feature saliency factors of all pixels. It integrates all pixel-level feature parameters with global feature parameters to construct the feature dataset corresponding to the single grain of material.

6. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The generation module performs an optical feature and trace impurity element correlation identification stage. For each preset target trace impurity element, based on the feature dataset of a single particle, it calculates the corresponding correlation identification factor of the element. By comparing the correlation identification factor with the preset identification threshold, it identifies the existence of the element and determines its content level. The correlation identification factor for the j-th target trace impurity element is: ； In the formula: is the correlation identification factor of the jth target trace impurity element; is the total number of pixels in the single particle material imaging area; is the impurity feature saliency factor of the pixel point (x, y) in the single particle material; is the pre-labeled exclusive feature band set matched with the characteristic absorption peak of the jth target trace impurity element; is the band response indication function, when the pixel point (x, y) is in all the normalized deviations corresponding to the characteristic bands are greater than the preset deviation threshold, the function takes the value 1, otherwise, takes the value 0; is the average value of the absolute value of the normalized deviation of all pixel points in the single particle material imaging area under the i th characteristic band; is the pre-labeled reference deviation average value of the jth target trace impurity element in ; is the preset minimum constant.

7. A photovoltaic quartz sand visual selection system according to claim 6, characterized in that, Based on the results of the association recognition, the generation module performs a qualification judgment on individual particles and generates corresponding sorting control instructions; The qualification determination shall be subject to: When the correlation identification factor of any target trace impurity element corresponding to a single particle exceeds the preset threshold range, the single particle is determined to be unqualified material; otherwise, it is determined to be qualified material. The sorting control command includes the material's location information, timing information, and sorting action information. The location information and timing information are generated in real time based on the material's conveying speed and arrangement position, and are used to ensure that the sorting action is completely matched with the arrival time of the target material. The sorting action information is used to control the sorting and discharge module to perform the sorting action for the corresponding qualified or unqualified material.

8. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The sorting and discharging module includes a high-speed air blowing execution unit, a discharging and diverting cavity, and a material collection unit; The high-speed air blowing actuator is used to receive sorting control commands. Based on the timing and action information in the commands, it outputs a high-pressure air blowing action of corresponding duration at the moment when the target non-conforming material arrives at the blowing station, thereby changing the falling trajectory of the non-conforming material. The discharge diversion cavity is equipped with mutually isolated qualified material flow channels and unqualified material flow channels. Qualified material enters the qualified material flow channel along the natural falling trajectory, while unqualified material, whose trajectory is changed by air blowing, enters the unqualified material flow channel. The material collection unit is connected to the qualified material flow channel and the unqualified material flow channel respectively to complete the classified collection of qualified and unqualified materials.

9. A photovoltaic quartz sand visual selection system according to claim 1, characterized in that, The single-particle feeding and arrangement module is interconnected with a capture module via industrial real-time wired communication. The capture module is interconnected with a preprocessing module and an extraction module via industrial real-time wired communication. The extraction module is interconnected with a generation module and a sorting and discharge module via industrial real-time wired communication.

10. A method for visual selection of photovoltaic quartz sand, which is a method for implementing a visual selection system for photovoltaic quartz sand as claimed in any one of claims 1 to 9, characterized in that, include: Photovoltaic quartz sand material is dispersed by vibration feeding, guided and constrained by alignment and uniform conveying control, forming a stable arrangement of discrete single particles with equal spacing. By matching the conveying speed and feeding frequency, the spacing between adjacent materials in the queue is stabilized within a preset range. By using a multispectral imaging unit that coincides with the boundary of the conveying path, multi-band optical imaging data of materials are collected in a dark field environment. Simultaneously, the imaging timing is matched with the material conveying timing to obtain raw multi-dimensional optical imaging data of the material surface and subsurface. Pixel-level spatiotemporal registration and geometric distortion correction are performed on multi-band imaging data, and band-adaptive noise reduction and unified grayscale mapping are applied to the corrected data to output standardized image data to be analyzed. Pixel-level feature extraction is performed on the standardized image and the impurity feature significance factor is calculated. Valid impurity pixels are marked. At the same time, the spatial distribution, proportion and band response features of impurities are extracted based on the valid impurity pixels and integrated to form a single-particle material feature dataset. Based on the feature dataset, the correlation identification factor of each trace impurity element is calculated to determine the presence and content level of the element. Based on the impurity identification results, the qualification of single material is determined and corresponding sorting control instructions are generated in real time. The high-speed air blowing action is triggered by the sorting control command, which changes the falling trajectory of the unqualified materials, so that the qualified materials and unqualified materials are guided separately through the diversion cavity and collected by the collection unit.

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