CN112764051A - Intelligent ore identification method and device by combining laser radar with vibration signal - Google Patents
Intelligent ore identification method and device by combining laser radar with vibration signal Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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Abstract
The invention discloses an artificial intelligence ore identification method and device based on laser imaging, relates to the technical field of photoelectric imaging, image processing and target detection, and can obtain the volume characteristics of ore by using range echo point cloud collected by a laser radar and make up for the defect of identifying ore by using a single vibration signal. The invention comprises the following steps: the ore drops from the discharge gate, freely falls to the ground on the vibration signal acquisition board, and the collision produces vibration signal, gathers vibration signal, demodulates vibration signal and obtains the impact characteristic parameter. The ore falls onto a conveyor belt which moves at a constant speed after impacting the vibration signal acquisition board; and scanning the ore by using a laser radar to obtain range echo point cloud data of the ore, and processing the range echo point cloud data to obtain the volume parameter of the ore. And performing combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and identifying the ore types of the combined characteristic parameters by using a neural network model.
Description
Technical Field
The invention relates to the technical field of photoelectric imaging, image processing and target detection, in particular to an intelligent ore identification method and device by combining a laser radar with a vibration signal.
Background
In the industrial field, especially in the field of object detection and identification based on conveyor belts, object segmentation, positioning, identification and other steps are indispensable, but the implementation of these steps is usually established on a clear image without background noise interference. For a common CCD, when the background is complex, an ideal effect cannot be achieved by using an algorithm to remove the background noise of an image, so that the difficulty of object segmentation and positioning is improved, and finally the recognition rate is reduced.
The existing ore identification method has poor applicability and stability and higher requirement on the cleanness degree of the ore surface. For ores with different physical properties, such as coal and gangue, when their volumes are similar, the vibration signals generated by the falling impact will be different greatly. In recent years, some domestic scholars develop research on ore identification based on vibration signals, but due to the lack of relevant theories and the limitation of production sites, the progress of engineering for ore identification based on vibration signals is severely restricted.
Therefore, an ore identification scheme capable of compensating the defects of the ore identified by a single vibration signal is needed.
Disclosure of Invention
In view of the above, the invention provides an intelligent ore identification method and device by combining a laser radar with a vibration signal, which can obtain the volume characteristics of an ore by using the range echo point cloud collected by the laser radar, make up for the defect of identifying the ore by using a single vibration signal by using the volume characteristics, and have engineering significance.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
the ore drops from the discharge gate, freely falls to the ground on the vibration signal acquisition board, and the striking produces vibration signal, gathers vibration signal, right vibration signal demodulates obtains the impact characteristic parameter.
The ore falls onto a conveyor belt which moves at a constant speed after impacting the vibration signal acquisition board.
And scanning the ore by using a laser radar to obtain range echo point cloud data of the ore, and processing the range echo point cloud data to obtain the volume parameter of the ore.
And performing combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and identifying the ore types of the combined characteristic parameters by using a neural network model.
Further, gather vibration signal, demodulate vibration signal and obtain the impact characteristic parameter, specifically be:
the ore drops from the discharge gate, and the kinetic equation of the in-process that freely falls to the ground on the vibration signal acquisition board is:
wherein M is a structural mass matrix of the vibration signal acquisition board; c is a plate structure damping matrix of the vibration signal acquisition plate; k is a structural rigidity matrix of the vibration signal acquisition board; x (t) is the total nodal displacement of the vibration signal acquisition board, an
The speed of the vibration signal acquisition board is acquired,
acceleration of the vibration signal acquisition board is taken as t is a time variable; f is a load matrix;
during the collision, the initial time of the collision is t1When the collision time Δ t → 0, the collision impulse I is:
the impact characteristic parameter is the ore velocity increment delta v of the ore and the vibration signal acquisition plate at the moment of collision separation2；
Δv2＝M-1I+(1+e)v1
Wherein v is1Collecting the instantaneous speed of the ore before the collision for the ore and the vibration signal acquisition board; and e is the recovery coefficient of the vibration signal acquisition board.
Further, the volume characteristic parameter is the volume integral of the range echo point cloud data of the ore in a three-dimensional space.
Further, the vibration signal acquisition board is a metal plate
Further, the combined characteristic parameter is a ratio of the impact characteristic parameter to the volume characteristic parameter.
Further, the identification of ore types is carried out by combining the characteristic parameters and utilizing a neural network model, and the method specifically comprises the following steps: and identifying the joint characteristic parameters by using the convolutional neural network model.
The invention also provides an intelligent ore recognition device combining the laser radar with the vibration signal, which comprises a conveyor belt, a vibration signal acquisition module, a laser radar module and a comprehensive processing module;
a discharge port is formed in one end of the conveying belt, and a vibration signal acquisition module is arranged below the discharge port; a laser radar module is arranged above the middle part of the conveyor belt; the conveyor belt moves at a constant speed.
The vibration signal acquisition module comprises a vibration signal acquisition board and a vibration signal sensor, the ore drops from the discharge port, the ore freely falls to the ground to generate a vibration signal on the vibration signal acquisition board, the vibration signal sensor which is generated on the vibration signal acquisition board acquires a vibration signal, and the vibration signal is transmitted to the comprehensive processing module.
The ore drops to the conveyer belt behind striking vibration signal acquisition board, and even running passes through the laser radar module, obtains the range echo point cloud data of ore by the scanning of laser radar module, and range echo electric cloud transmits to comprehensive processing module.
And the comprehensive processing module demodulates the vibration signal to obtain the impact characteristic parameter.
And the comprehensive processing module processes the distance echo point cloud data of the ore to obtain the volume parameter of the ore.
And the comprehensive processing module performs combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and the neural network model is utilized to identify the ore types according to the combined characteristic parameters.
Further, the vibration signal acquisition board is a metal board.
Has the advantages that:
1. according to the artificial intelligent ore identification method and device based on laser imaging, provided by the embodiment of the invention, the ore is identified by combining the vibration signal of ore falling with the laser radar, so that the identification accuracy of the ore can be greatly improved. The identification precision of the laser radar to the volume can reach millimeter level, the precision of the vibration signal sensor in the embodiment of the invention can reach 20mv/g, and the density physical property of the ore can be sensitively obtained through multi-mode characteristic parameters. The method can well solve the problem that the recognition rate is influenced by high water content and dust covering on the surface of the ore. The recognition rate of ores with covered surfaces, such as coal and gangue, reaches 97 percent. The working process is independent of ambient light, and the anti-vibration device has strong anti-vibration capability and explosion-proof property.
2. The artificial intelligent ore identification method and device based on laser imaging provided by the invention have strong environmental adaptability, do not need a light source and are free from ray radiation hazard.
3. The artificial intelligent ore identification method and device based on laser imaging provided by the invention have real-time performance on ore identification on a conveyor belt by applying a convolutional neural network algorithm.
Drawings
Fig. 1 is a flowchart of an intelligent ore identification method by combining a laser radar with a vibration signal according to an embodiment of the present invention;
fig. 2 is a structural diagram of an intelligent ore identification device combining a laser radar and a vibration signal according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides an intelligent ore identification method combining a laser radar with a vibration signal, which comprises the following steps as shown in figure 1:
s1, the ore falls from the discharge hole and freely falls to the vibration signal acquisition board. The vibration signal acquisition plate is a metal plate, and specifically, a steel plate can be adopted.
And S2, colliding the ore with the vibration signal acquisition board to generate a vibration signal, and acquiring the vibration signal. When the ore strikes the vibration signal acquisition board, the corresponding information of time and impulse can be obtained through the vibration signal sensor, and the impact characteristic parameters can be obtained through demodulation. And demodulating the vibration signal to obtain an impact characteristic parameter.
The kinetic equation of the ore during the collision can be expressed in particular by the following formula:
wherein M is a structural mass matrix of the vibration signal acquisition board; c is a plate structure damping matrix of the vibration signal acquisition plate; k is a structural rigidity matrix of the vibration signal acquisition board; x (t) is the total nodal displacement of the vibration signal acquisition board, an
The speed of the vibration signal acquisition board is acquired,
acceleration of the vibration signal acquisition board is taken as t is a time variable; f is a load matrix;
during the collision, the initial time of the collision is t1When the collision time Δ t → 0, the collision impulse I is:
the impact characteristic parameter is the ore velocity increment delta v of the ore and the vibration signal acquisition plate at the moment of collision separation2；
Δv2＝M-1I+(1+e)v1
Wherein v is1Collecting the instantaneous speed of the ore before the collision for the ore and the vibration signal acquisition board; and e is the recovery coefficient of the vibration signal acquisition board.
S3, the ore falls onto a conveyor belt moving at a constant speed, and the ore 6 is scanned by a laser radar.
And S4, transmitting laser signals to the ore on the conveyor belt by the laser radar, wherein the laser signals are reflected on the surface of the ore, and the laser radar is provided with a laser return signal receiving unit which receives the laser return signals transmitted by the laser signal transmitting unit and performs data conversion to obtain the distance echo point cloud of the ore. And processing the distance echo point cloud data to obtain the volume parameter of the ore. The volume characteristic parameter is the volume integral of the point cloud in the three-dimensional space.
S5, performing combined processing on the impact characteristic parameter and the volume parameter to obtain a combined characteristic parameter; the joint feature vectors are input into a trained recognition model in a convolutional neural network.
In the present invention, the information after the joint processing can be identified by using a convolutional neural network model, specifically:
and performing combined processing on the existing ore impact characteristic parameters and volume parameters to construct a convolutional neural network model, and training and testing the convolutional neural network model by using the jointly processed existing ore impact characteristic parameters and volume information to obtain the trained convolutional neural network model. Wherein, the existing ore impact characteristic parameters and volume parameters are acquired in advance, and the corresponding ore types are also known.
And performing combined processing on the ore texture and the appearance information, inputting the combined information into the trained convolutional neural network model, and outputting the recognition result of the ore by the trained convolutional neural network model.
And S6, identifying the ore type by utilizing the neural network model for the combined characteristic parameters, and outputting an ore identification result. In the present invention, the characteristic parameters can be identified by using a convolutional neural network model, specifically:
the artificial intelligent ore identification method based on laser imaging utilizes the vibration signal of ore falling and combines the laser radar to identify the ore, and can greatly improve the identification accuracy of the ore. The identification precision of the laser radar to the volume can reach millimeter level, the precision of the vibration signal sensor can reach 20mv/g, and the density physical property of the ore can be sensitively obtained through multi-mode characteristic parameters. The method can well solve the problem that the recognition rate is influenced by high water content and dust covering on the surface of the ore. The working process is independent of ambient light, and the anti-vibration device has strong anti-vibration capability and explosion-proof property. And the convolutional neural network model is utilized to identify the ore impact characteristic parameters and the volume parameters more accurately. And the operation process is simple, the applicability is good, and the ore on the conveying belt can be identified in real time.
As shown in fig. 2, another embodiment of the present invention further provides an intelligent ore identification device with a laser radar combined with a vibration signal, the device including: conveyer belt 4, vibration signal acquisition module 3, laser radar module 2 and comprehensive processing module 5.
A discharge port 1 is formed in one end of the conveyor belt 4, and a vibration signal acquisition module 3 is arranged below the discharge port 1; and a laser radar module 2 is arranged above the middle part of the conveyor belt 4.
Vibration signal acquisition module 3 includes vibration signal acquisition board and vibration signal sensor, and ore 6 drops from the discharge gate, freely falls to the ground and produces vibration signal on the vibration signal acquisition board, and the vibration signal sensor who produces on the vibration signal acquisition board gathers and gathers vibration signal, transmits vibration signal to comprehensive processing module 5.
The ore drops on conveyer belt 4 behind the striking vibration signal acquisition board, and even running passes through laser radar module 2, obtains the range echo point cloud data of ore by the scanning of laser radar module 2, and range echo electric cloud transmits to comprehensive processing module 5.
The comprehensive processing module 5 demodulates the vibration signal to obtain the impact characteristic parameter.
And the comprehensive processing module 5 processes the distance echo point cloud data of the ore to obtain the volume parameter of the ore.
The comprehensive processing module 5 performs combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and performs ore type identification by using a neural network model according to the combined characteristic parameters.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent ore identification method combining a laser radar with a vibration signal is characterized by comprising the following steps:
ore falls from a discharge port, freely falls to a vibration signal acquisition board, is impacted to generate a vibration signal, acquires the vibration signal, and demodulates the vibration signal to obtain an impact characteristic parameter;
the ore falls onto a conveyor belt which moves at a constant speed after impacting the vibration signal acquisition board;
scanning the ore by using a laser radar to obtain range echo point cloud data of the ore, and processing the range echo point cloud data to obtain a volume parameter of the ore;
and performing combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and identifying the ore types of the combined characteristic parameters by using a neural network model.
2. The method according to claim 1, wherein the acquiring the vibration signal, and the demodulating the vibration signal to obtain the impact characteristic parameter includes:
the ore drops from the discharge gate, and the kinetic equation of the in-process that freely falls to the ground on the vibration signal acquisition board is:
wherein M is a structural mass matrix of the vibration signal acquisition board; c is a plate structure damping matrix of the vibration signal acquisition plate; k is a structural rigidity matrix of the vibration signal acquisition board; x (t) is the total nodal displacement of the vibration signal acquisition board, an
The speed of the vibration signal acquisition board is acquired,
acceleration of the vibration signal acquisition board is taken as t is a time variable; f is a load matrix;
during the collision, the initial time of the collision is t1When the collision time Δ t → 0, the collision impulse I is:
the impact characteristic parameter is the ore velocity increment delta v of the ore and the vibration signal acquisition plate at the moment of collision separation2；
Δv2＝M-1I+(1+e)v1
Wherein v is1Collecting the instantaneous speed of the ore before the collision for the ore and the vibration signal acquisition board; and e is the recovery coefficient of the vibration signal acquisition board.
3. The method according to claim 1, characterized in that the volume characteristic parameter is a volume integral in three-dimensional space of range echo point cloud data of the ore (6).
4. The method of claim 1, wherein the vibration signal acquisition board is a metal board.
5. A method according to any one of claims 1 to 4, wherein the combined characteristic is the ratio of the impact characteristic to the volume characteristic.
6. The method according to any one of the claims 5, characterized in that the joint feature parameters are used for identifying the ore type by means of a neural network model, in particular:
and identifying the joint characteristic parameters by using a convolutional neural network model.
7. An intelligent ore recognition device combining a laser radar with a vibration signal is characterized by comprising a conveyor belt (4), a vibration signal acquisition module (3), a laser radar module (2) and a comprehensive processing module (5);
a discharge port (1) is formed in one end of the conveyor belt (4), and the vibration signal acquisition module (3) is arranged below the discharge port (1); the laser radar module (2) is arranged above the middle part of the conveyor belt (4); the conveyor belt (4) moves at a constant speed;
the ore (6) falls from the discharge hole and freely falls to the vibration signal acquisition board to generate vibration signals, and the vibration signal sensor generated on the vibration signal acquisition board acquires the vibration signals and transmits the vibration signals to the comprehensive processing module (5);
ore falls onto the conveyor belt (4) after impacting the vibration signal acquisition board, runs stably through the laser radar module (2), and is scanned by the laser radar module (2) to obtain range echo point cloud data of the ore, and the range echo electric cloud is transmitted to the comprehensive processing module (5);
the comprehensive processing module (5) demodulates the vibration signal to obtain an impact characteristic parameter;
the comprehensive processing module (5) processes the distance echo point cloud data of the ore to obtain a volume parameter of the ore;
and the comprehensive processing module (5) performs combined processing on the impact characteristic parameters and the volume parameters to obtain combined characteristic parameters, and identifies the ore types by utilizing a neural network model according to the combined characteristic parameters.
8. The method of claim 7, wherein the vibration signal acquisition board is a metal board.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117550310A (en) * | 2024-01-11 | 2024-02-13 | 赣州好朋友科技有限公司 | Explosion-proof feed divider with self-interacting circumference diameter function |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4600105A (en) * | 1983-03-23 | 1986-07-15 | Sphere Investments Limited | Method and apparatus for sorting objects of ore by monitoring reflected radiation |
| GB2280956A (en) * | 1991-02-20 | 1995-02-15 | Gersan Ets | Detecting diamonds in a plurality of objects |
| DE102005010867A1 (en) * | 2005-03-07 | 2006-10-19 | Commodas Daten- Und Systemtechnik Nach Mass Gmbh | Particle analysis or identification device for use in conveying flow, has linear sensors and processing unit calculating absorption characteristic of particles from signals of sensors of different spectral sensitivity |
| US20130026263A1 (en) * | 2011-06-29 | 2013-01-31 | Minesense Technologies Ltd. | Extracting mined ore, minerals or other materials using sensor-based sorting |
| JP2017083348A (en) * | 2015-10-29 | 2017-05-18 | 住友金属鉱山株式会社 | Ore selection method and device thereof |
| JP2017080732A (en) * | 2015-10-29 | 2017-05-18 | 住友金属鉱山株式会社 | Ore screening method and device |
| US20190049560A1 (en) * | 2018-08-14 | 2019-02-14 | Rita Chattopadhyay | Lidar-based object detection and classification |
| CN110924946A (en) * | 2019-12-23 | 2020-03-27 | 中国矿业大学 | Top coal caving device based on spectrum identification technology and use method thereof |
| CN111408546A (en) * | 2020-03-11 | 2020-07-14 | 武汉工程大学 | Ore detection method and system based on laser scanning imaging |
| CN111812671A (en) * | 2020-06-24 | 2020-10-23 | 北京佳力诚义科技有限公司 | Artificial intelligence ore recognition device and method based on laser imaging |
-
2020
- 2020-11-27 CN CN202011358795.5A patent/CN112764051A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4600105A (en) * | 1983-03-23 | 1986-07-15 | Sphere Investments Limited | Method and apparatus for sorting objects of ore by monitoring reflected radiation |
| GB2280956A (en) * | 1991-02-20 | 1995-02-15 | Gersan Ets | Detecting diamonds in a plurality of objects |
| DE102005010867A1 (en) * | 2005-03-07 | 2006-10-19 | Commodas Daten- Und Systemtechnik Nach Mass Gmbh | Particle analysis or identification device for use in conveying flow, has linear sensors and processing unit calculating absorption characteristic of particles from signals of sensors of different spectral sensitivity |
| US20130026263A1 (en) * | 2011-06-29 | 2013-01-31 | Minesense Technologies Ltd. | Extracting mined ore, minerals or other materials using sensor-based sorting |
| JP2017083348A (en) * | 2015-10-29 | 2017-05-18 | 住友金属鉱山株式会社 | Ore selection method and device thereof |
| JP2017080732A (en) * | 2015-10-29 | 2017-05-18 | 住友金属鉱山株式会社 | Ore screening method and device |
| US20190049560A1 (en) * | 2018-08-14 | 2019-02-14 | Rita Chattopadhyay | Lidar-based object detection and classification |
| CN110924946A (en) * | 2019-12-23 | 2020-03-27 | 中国矿业大学 | Top coal caving device based on spectrum identification technology and use method thereof |
| CN111408546A (en) * | 2020-03-11 | 2020-07-14 | 武汉工程大学 | Ore detection method and system based on laser scanning imaging |
| CN111812671A (en) * | 2020-06-24 | 2020-10-23 | 北京佳力诚义科技有限公司 | Artificial intelligence ore recognition device and method based on laser imaging |
Non-Patent Citations (2)
| Title |
|---|
| 宋庆军 等: "多传感器信息融合的放煤过程参数化研究", 自动化仪表, vol. 36, no. 5, pages 1 - 3 * |
| 雷静 等: "基于信息融合和神经网络的煤岩识别方法", 工矿自动化, vol. 43, no. 9 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117550310A (en) * | 2024-01-11 | 2024-02-13 | 赣州好朋友科技有限公司 | Explosion-proof feed divider with self-interacting circumference diameter function |
| CN117550310B (en) * | 2024-01-11 | 2024-05-03 | 赣州好朋友科技有限公司 | Explosion-proof feed divider with self-interacting circumference diameter function |
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