CN113019955A - Intelligent ore sorting equipment and method based on dual-energy X-ray - Google Patents
Intelligent ore sorting equipment and method based on dual-energy X-ray Download PDFInfo
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- CN113019955A CN113019955A CN202110292200.9A CN202110292200A CN113019955A CN 113019955 A CN113019955 A CN 113019955A CN 202110292200 A CN202110292200 A CN 202110292200A CN 113019955 A CN113019955 A CN 113019955A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/3416—Sorting according to other particular properties according to radiation transmissivity, e.g. for light, x-rays, particle radiation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/363—Sorting apparatus characterised by the means used for distribution by means of air
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B08—CLEANING
- B08B—CLEANING IN GENERAL; PREVENTION OF FOULING IN GENERAL
- B08B7/00—Cleaning by methods not provided for in a single other subclass or a single group in this subclass
- B08B7/02—Cleaning by methods not provided for in a single other subclass or a single group in this subclass by distortion, beating, or vibration of the surface to be cleaned
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Abstract
The invention discloses ore intelligent sorting equipment and method based on dual-energy X-ray. The invention comprises a material distribution device, an identification device, an execution device, a dust removal system, a heat dissipation system and an X-ray isolation protection system; the identification device comprises a dual-energy X-ray generating device arranged above the transmission belt and a dual-energy X-ray detector arranged below a direct-emitting point of the dual-energy X-ray generating device; the execution device consists of an electromagnetic valve, a separation cylinder and a nozzle and is used for controlling high-pressure air to blow and hit the ore in the throwing process of the target ore. According to the invention, good material and waste material pictures are acquired through image acquisition and are used as a deep learning model training sample set, the acquired ore sample images are processed, a target area is found and is divided, a small target image is sent into a trained classification model, confidence coefficients of good materials and waste materials are compared and identified, the material type is determined according to the category of multi-classification information after fusion, the ore sorting efficiency is improved, and meanwhile, the energy is saved and the environment is protected.
Description
Technical Field
The invention belongs to the technical field of ore sorting, and particularly relates to intelligent ore sorting equipment and method based on dual-energy X-ray.
Background
With the rapid development of each field of modern industry, the demand of the market for ores, particularly metal ores, is larger and larger, and meanwhile, the requirement for the taste of the ores is higher and higher, however, many mining methods of mines are still laggard at present, the ore sorting is finished by manual sorting, workers need to endure long-time machine noise and dust infection, and the method has low production efficiency, high sorting economic cost and low sorting precision. In recent years, CCD cameras and X-ray imaging modes are utilized, the mode utilizes different attenuation degrees of raw materials with different densities under the irradiation of X-rays to present different projection intensities, so that images with different gray features are generated, and then fusion information is utilized to classify and identify materials. However, the image recognition mode is greatly influenced by environment and light source, the algorithm robustness is poor, and the problems of materials, connection, superposition or shielding and the like cannot be solved, so that certain defects and shortcomings are provided.
In order to overcome the defects and shortcomings of the traditional ore dressing mode and precision, the intelligent ore sorting equipment and method based on the dual-energy X-ray are provided. The device adopts a high-specification light intersection source, integrates a high-voltage generator, an X-ray tube, a control circuit and a heat dissipation system, and has more uniform signals and stronger penetrating power; meanwhile, an imported high-performance dual-energy X-ray detector is selected as the image acquisition device, full-spectrum multi-spectrum-segment scanning imaging is performed, the image signal-to-noise ratio is higher, and the material characteristics are more prominent; the material classification and identification is realized by analyzing and identifying the ore image through a deep learning neural network article identification technology, and information such as material texture characteristics, density distribution characteristics, granularity, K value, duty ratio and the like is fused, so that the accuracy is high; the execution device accurately controls high-pressure air to blow the waste materials and the impurities, so that accurate, efficient and energy-saving full-automatic ore dry separation is realized.
Disclosure of Invention
The invention aims to provide dual-energy X-ray-based ore intelligent sorting equipment and method, which are characterized in that good material and waste material pictures are acquired through image acquisition and serve as a deep learning model training sample set, the acquired ore sample images are processed, a target area is found and divided, and then small target images are sent into a trained classification model, so that the problems that the existing ore and waste rock are not easy to distinguish and the manual dry sorting efficiency is low are solved.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to an intelligent ore sorting device based on dual-energy X-ray, which comprises a distributing device, an identification device, an execution device, a dust removal system, a heat dissipation system and an X-ray isolation protection system,
the material distribution device is used for inputting ores into the material port from the vibration hopper and evenly spreading the ores on the transmission belt through the material distribution driving motor;
the identification device comprises a dual-energy X-ray generating device arranged above the transmission belt and a dual-energy X-ray detector arranged below a direct-emitting point of the dual-energy X-ray generating device;
the execution device consists of an electromagnetic valve, a separation cylinder and a nozzle and is used for controlling high-pressure air to blow and hit the ore in the throwing process of the target ore;
the dust removal system is arranged on the vibration hopper and is used for removing dust in the ore conveying process;
the heat dissipation system is arranged on the identification device and used for dissipating heat generated in the working process of the dual-energy X-ray generation device;
the X-ray isolation protection system is a lead room covered outside the dual-energy X-ray generating device and used for preventing X-rays from damaging a human body.
Preferably, the dual-energy X-ray detector is fixed by a groove type base; the groove type base is used for adjusting the typesetting radian of the detector according to the ray incidence angle and correcting deformation of material imaging.
Preferably, the nozzle of the actuating device is a high-pressure air nozzle; the high-pressure air nozzles are arranged in an array mode, and the opening and closing states and the opening and closing time of the high-pressure air nozzles at corresponding positions are intelligently controlled according to the position and shape information of an executed object.
Preferably, the beam shape of the outlet of the dual-energy X-ray generating device is fan-shaped; and a collimator with a fan-shaped opening is arranged at the outlet of the dual-energy X-ray generating device.
Preferably, the maximum radiation fan angle range of the fan shape of the collimator is 80-105 degrees.
The invention relates to an intelligent ore sorting method based on dual-energy X-ray, which comprises the following steps:
step S1: selecting high-grade ore, dividing the high-grade ore into good material and waste material, and collecting a deep learning model training sample set by an image acquisition device;
step S2: adjusting the gain of the material picture according to a histogram equalization method, and storing the gain-enhanced image;
step S3: eliminating salt and pepper noise in the image by using a median filtering algorithm of the digital image;
step S4: corroding the image by using a morphological algorithm;
step S5: searching possible target areas by adopting a connected domain search algorithm, and removing false targets by utilizing a segmentation threshold;
step S6: segmenting a target image, rasterizing the target image into 25 × 25 small targets, and marking a background image except the targets as a mask image;
step S7: sending the small target image into a deep learning network classification model, and comparing and identifying the confidence coefficients of good materials and waste materials;
step S8: calculating the granularity of each classification of the rasterized image, 8-connected classification proportion, an R value and a K value score;
step S9: and according to the category of the fused multi-classification information, multiplying the weighted factor of each judging characteristic respectively, calculating the score value of each material which is identified as good material and bad material, and comparing the score value with a set target classification threshold value to determine the material type.
Preferably, in step S2, the image gain enhancement formula is as follows:
qij＝(pij-Bj)*(Gj-Bj)；
in the formula, GjThe average value of the pixels of the jth column of the empty belt image collected when the radiation source is opened is represented; b isjAverage pixel values representing column j of the source-off spatio-temporal belt image; p is a radical ofijAnd the pixel represents the ith row and the jth column of the real-time material image.
Preferably, in step S9, the calculation formula for determining the material type is:
Ad＝ρ1*P+ρ2*V+ρ3*K+...+ρn*R；
in the formula, Ad is a comprehensive score value of the material, ρ 1, ρ 2, ρ 3,. ρ n represents a weighting factor, P represents a category score value calculated by the depth network model, V represents a category score ratio of the rasterized small object, and R represents an R characteristic value calculated according to the lange-beer law.
The invention has the following beneficial effects:
according to the invention, good material and waste material pictures are acquired through image acquisition and are used as a deep learning model training sample set, the acquired ore sample images are processed, a target area is found and is divided, a small target image is sent into a trained classification model, confidence coefficients of good materials and waste materials are compared and identified, the material type is determined according to the category of multi-classification information after fusion, the ore sorting efficiency is improved, and meanwhile, the energy is saved and the environment is protected.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an intelligent ore sorting device based on dual-energy X-ray according to the present invention;
fig. 2 is a flow chart of an intelligent ore sorting method based on dual-energy X-ray according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is an intelligent ore sorting device based on dual-energy X-ray, which comprises a material distribution device, an identification device, an execution device, a dust removal system, a heat dissipation system and an X-ray isolation protection system,
the material distribution device is used for inputting the ores into the material port from the vibration hopper and evenly spreading the ores on the transmission belt through the material distribution driving motor;
the identification device comprises a dual-energy X-ray generating device arranged above the transmission belt and a dual-energy X-ray detector arranged below a direct-emitting point of the dual-energy X-ray generating device;
the execution device consists of an electromagnetic valve, a separation cylinder and a nozzle and is used for controlling high-pressure air to blow and hit the ores in the throwing process of the target ores;
the dust removal system is arranged on the vibration hopper and is used for removing dust in the ore conveying process;
the heat dissipation system is arranged on the identification device and used for dissipating heat generated in the working process of the dual-energy X-ray generation device;
the X-ray isolation protection system is a lead room covered outside the dual-energy X-ray generating device and used for preventing X-rays from damaging a human body.
The dual-energy X-ray detector is fixed by a groove type base; the groove type base is used for adjusting the typesetting radian of the detector according to the ray incidence angle and correcting deformation of material imaging.
Wherein, the nozzle of the execution device is a high-pressure air nozzle; the high-pressure air nozzles are arranged in an array mode, and the opening and closing states and the opening and closing time of the high-pressure air nozzles at corresponding positions are intelligently controlled according to the position and shape information of an executed object.
Wherein, the beam shape of the outlet of the dual-energy X-ray generating device is fan-shaped; the outlet of the dual-energy X-ray generating device is provided with a collimator with a fan-shaped opening facing downwards.
Wherein, the maximum radiation fan angle range of the fan shape of the collimator is 80-105 degrees.
Referring to fig. 2, the present invention is a dual-energy X-ray based intelligent ore sorting method, including the following steps:
step S1: selecting high-grade ore, dividing the high-grade ore into good material and waste material, and collecting a deep learning model training sample set by an image acquisition device;
step S2: adjusting the gain of the material picture according to a histogram equalization method, and storing the gain-enhanced image;
step S3: eliminating salt and pepper noise in the image by using a median filtering algorithm of the digital image;
step S4: corroding the image by using a morphological algorithm, and searching adhesion among targets;
step S5: searching possible target areas by adopting a connected domain search algorithm, and removing false targets by utilizing a segmentation threshold;
step S6: segmenting a target image, rasterizing the target image into 25 × 25 small targets, and marking a background image except the targets as a mask image;
step S7: sending the small target image into a deep learning network classification model, and comparing and identifying the confidence coefficients of good materials and waste materials;
step S8: calculating the granularity of each classification of the rasterized image, 8-connected classification proportion, an R value and a K value score;
step S9: and according to the category of the fused multi-classification information, multiplying the weighted factor of each judging characteristic respectively, calculating the score value of each material which is identified as good material and bad material, and comparing the score value with a set target classification threshold value to determine the material type.
In step S2, the image gain enhancement formula is as follows:
qij＝(pij-Bj)*(Gj-Bj)；
in the formula, GjThe average value of the pixels of the jth column of the empty belt image collected when the radiation source is opened is represented; b isjRepresenting the jth column of the space-time belt image with the source switched offA pixel average value; p is a radical ofijAnd the pixel represents the ith row and the jth column of the real-time material image.
In step S9, the calculation formula for determining the material type is:
Ad＝ρ1*P+ρ2*V+ρ3*K+...+ρn*R；
in the formula, Ad is a comprehensive score value of the material, ρ 1, ρ 2, ρ 3,. and ρ n represent weighting factors, P represents a category score value calculated by the depth network model, V represents a category score ratio of the rasterized small object, and R represents an R characteristic value calculated according to the lange-beer law.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. The utility model provides an ore intelligence sorting facilities based on dual energy X ray, includes distributing device, recognition device, execute device, dust pelletizing system, cooling system and X ray isolation protection system, its characterized in that:
the material distribution device is used for inputting ores into the material port from the vibration hopper and evenly spreading the ores on the transmission belt through the material distribution driving motor;
the identification device comprises a dual-energy X-ray generating device arranged above the transmission belt and a dual-energy X-ray detector arranged below a direct-emitting point of the dual-energy X-ray generating device;
the execution device consists of an electromagnetic valve, a separation cylinder and a nozzle and is used for controlling high-pressure air to blow and hit the ore in the throwing process of the target ore;
the dust removal system is arranged on the vibration hopper and is used for removing dust in the ore conveying process;
the heat dissipation system is arranged on the identification device and used for dissipating heat generated in the working process of the dual-energy X-ray generation device;
the X-ray isolation protection system is a lead room covered outside the dual-energy X-ray generating device and used for preventing X-rays from damaging a human body.
2. The dual-energy X-ray based ore intelligent separation equipment as claimed in claim 1, wherein the dual-energy X-ray detector is fixed by a groove type base; the groove type base is used for adjusting the typesetting radian of the detector according to the ray incidence angle and correcting deformation of material imaging.
3. The dual-energy X-ray-based ore intelligent separation equipment as claimed in claim 1, wherein a nozzle of the execution device is a high-pressure air nozzle; the high-pressure air nozzles are arranged in an array mode, and the opening and closing states and the opening and closing time of the high-pressure air nozzles at corresponding positions are intelligently controlled according to the position and shape information of an executed object.
4. The dual-energy X-ray-based ore intelligent separation equipment as claimed in claim 1, wherein the shape of the beam at the outlet of the dual-energy X-ray generation device is fan-shaped; and a collimator with a fan-shaped opening is arranged at the outlet of the dual-energy X-ray generating device.
5. The dual-energy X-ray-based ore intelligent separation equipment as claimed in claim 4, wherein the maximum radiation fan angle of the fan shape of the collimator is in the range of 80-105 degrees.
6. An intelligent ore sorting method based on dual-energy X-ray is characterized by comprising the following steps:
step S1: selecting high-grade ore, dividing the high-grade ore into good material and waste material, and collecting a deep learning model training sample set by an image acquisition device;
step S2: adjusting the gain of the material picture according to a histogram equalization method, and storing the gain-enhanced image;
step S3: eliminating salt and pepper noise in the image by using a median filtering algorithm of the digital image;
step S4: corroding the image by using a morphological algorithm;
step S5: searching possible target areas by adopting a connected domain search algorithm, and removing false targets by utilizing a segmentation threshold;
step S6: segmenting a target image, rasterizing the target image into 25 × 25 small targets, and marking a background image except the targets as a mask image;
step S7: sending the small target image into a deep learning network classification model, and comparing and identifying the confidence coefficients of good materials and waste materials;
step S8: calculating the granularity of each classification of the rasterized image, 8-connected classification proportion, an R value and a K value score;
step S9: and according to the category of the fused multi-classification information, multiplying the weighted factor of each judging characteristic respectively, calculating the score value of each material which is identified as good material and bad material, and comparing the score value with a set target classification threshold value to determine the material type.
7. The dual-energy X-ray-based ore intelligent sorting method according to claim 6, wherein in the step S2, the image gain enhancement formula is as follows:
qij＝(pij-Bj)*(Gj-Bj)；
in the formula, GjThe average value of the pixels of the jth column of the empty belt image collected when the radiation source is opened is represented; b isjAverage pixel values representing column j of the source-off spatio-temporal belt image; p is a radical ofijAnd the pixel represents the ith row and the jth column of the real-time material image.
8. The dual-energy X-ray based ore intelligent sorting method according to claim 6, wherein in the step S9, the calculation formula for determining the material type is as follows:
Ad＝ρ1*P+ρ2*V+ρ3*K+...+ρn*R；
in the formula, Ad is a comprehensive score value of the material, ρ 1, ρ 2, ρ 3,. ρ n represents a weighting factor, P represents a category score value calculated by the depth network model, V represents a category score ratio of the rasterized small object, and R represents an R characteristic value calculated according to the lange-beer law.
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Application publication date: 20210625 |