CN111982838A - Hyperspectrum-based coal rock identification and detection method - Google Patents
Hyperspectrum-based coal rock identification and detection method Download PDFInfo
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Abstract
The invention discloses a hyperspectral coal rock identification and detection method. In particular to the field of automatic coal mining. The specific method comprises the following steps: the method comprises the steps of aligning a detection probe of system equipment to a detection area to be identified, preprocessing spectral data after the hyperspectral data are obtained, further normalizing the spectral data by using a continuum removal method, establishing a coal rock detection and identification model by combining with an SVM algorithm, and obtaining an identification detection result by the model. In addition, all the processing data are transmitted to the database, and the database stores all the data and gives comparison identification detection results in the database. And finally, outputting and displaying the identification detection result obtained by the model and the comparison identification result given by the database. The coal rock identification detection method provided by the invention can quickly and accurately obtain the detection result of coal rock identification, and has important significance for intelligent and unmanned coal mining.
Description
Technical Field
The invention relates to the field of automatic coal mining, in particular to a hyperspectral coal rock identification and detection method.
Background
Coal, as a common fossil energy, has long become an important socio-economic life line in our country. According to recent survey and statistics, the main energy consumption of China is about 70% of coal, the proportion of the coal in civil energy, chemical raw materials and electric energy is respectively 80%, 60% and 67%, and in northeast areas, the coal energy is still used as the main energy for heating in winter. Currently, the economic development trend of China is good, although the use proportion of coal is reduced, the coal is still one of important energy sources consumed in China in a future period of time. Therefore, the development of coal-related technologies has great significance for promoting economic growth.
Under the background of the strong national advocated intelligent manufacturing, coal rock recognition is taken as a key technical point for realizing unmanned intelligent coal mining, and the importance of the coal rock recognition is visible. The current coal rock identification technology still has more or less application problems due to the limitation of principle technology. The traditional identification detection method comprises the following steps: the method comprises the steps of sound wave detection, image analysis, thermal infrared detection, gamma ray detection, terahertz spectrum detection and the like, and the methods do not distinguish from the material essential attributes of the coal rock. The hyperspectral technology acts on material detection and identification by using the spectral resolution of lambda/100 (lambda is the spectral wavelength), can detect and identify the material components in situ, quickly and at low cost, has the basic principle that specific chemical components and composition structures can generate specific spectral reflection and absorption characteristics under specific wavelength, and establishes a characteristic detection and identification model of a curve according to the absorption characteristics, so that the identification and detection results of coal and rock can be quickly and accurately obtained, and the development of the intelligent and unmanned coal mining technology is promoted.
Disclosure of Invention
The invention provides a hyperspectral coal rock identification detection method, which aims to quickly and accurately obtain a coal rock identification detection result. The method can quickly obtain the identification and detection result under the condition of complex mining operation, and the accuracy rate of the identification result is high.
The technical scheme adopted by the invention is as follows:
a hyperspectral coal rock identification and detection method comprises the following steps of:
s1, detecting the sample to be detected by using hyperspectral detection equipment under the normal operation of the equipment;
s2, carrying out spectrum preprocessing on the acquired spectrum data;
s3, performing normalization operation on the preprocessed spectral data by adopting continuum elimination method, and establishing a coal rock detection and identification model by combining SVM algorithm technology;
s4, sending the detection result of the coal rock identification into a database for storage and carrying out data comparison and analysis;
s5, outputting the comparison result of the database and the result of the field identification of the coal rock detection model;
further, the normal operation of the device in step S1 is to align the detection probe to the coal rock identification area, provide supplementary lighting by an external spotlight, and acquire and process the coal rock data detected by the hyperspectral technology by the sampling computer.
Further, the spectrum preprocessing operation of step S2 is to use CWT transform to correct the background and SG smoothing method to eliminate random noise in the spectrum signal.
Wherein the CWT transformation formula is as follows:
f (t) is a spectral signal, t is a time domain signal,
is a wavelet basis function, a is a translation parameter, and C is a wavelet coefficient.
Further, in the step S3, the spectral curve is normalized by using a continuum removal method, which is beneficial to comparison and extraction of subsequent characteristic bands, and an SVM technique is used to establish a model for coal rock recognition, wherein the specific algorithm flow is as follows:
s301, let t be the t-th node on the curve, the wavelength array be wl (i), and the reflectivity array be x (i), where i is 1, 2, …, N.
(1) Let (WL (1), X (1)) be the initial point of the envelope and assign the initial point of the original spectral data to this point.
(2) Using the point (WL (i), X (i)) as the initial point and searching the point (WL (j), X (j)) along the wavelength increasing direction of the spectrum curve according to a certain selection rule; wherein the selection rule is as follows: let the straight line of (WL (i), X (i)) and (WL (j), X (j)) be L, the original curve should not be higher than the straight line L all the time, and add the point corresponding coordinates of (WL (j), X (j)) into the envelope node table.
(3) And (3) repeating the step (2) until i is equal to N, ending the traversal, and connecting the line segments of the two adjacent points.
(4) The envelope removal, then, has a reflectivity of:
where z (i) is the data value on the polygonal line segment corresponding to the reflectivity array x (i), where i is 1, 2, …, N.
S302, the spectral data with envelope elimination at 12 wavelength points in the curve are used as training data, the coal rock types of 70 samples are used as prediction target data, and a coal rock type Support Vector Classification (SVC) prediction model is established.
The SVC optimal interface function is:
wherein
b*Optimal solution of Lagrange transformation method for finding optimal interface in high-dimensional space, K (x)iX) is RBF kernel, c is penalty factor, yicThe type of the ith training sample is coded. Using 5-fold cross validation to solve, the parameter searching range is 2-10～210The index step is taken to be 0.5.
Further, in the step S4, all the data are sent to a database for storage, and the current spectrum identification and comparison result is given by combining the existing data information of the database.
Further, in step S5, the comparison information of the database and the result obtained by identifying the coal-rock model are displayed to the user, where the comparison information is used as an auxiliary reference, and the result is used as a main result of the detection.
The invention has the beneficial effects that:
1. the invention can obtain the coal rock identification detection result in a very fast time under the complex coal mining operation environment.
2. The invention has high identification accuracy. The detection model provides the detection result, and the database provides the comparison result. The detection system is simple and reliable, and is suitable for complex field operation environments.
Drawings
Fig. 1 is a general flow chart of the present invention.
Detailed Description
As shown in fig. 1, a hyperspectral coal rock identification detection method includes the following steps:
s1, detecting the sample to be detected by using hyperspectral detection equipment under the normal operation of the equipment;
s2, carrying out spectrum preprocessing on the acquired spectrum data;
s3, performing normalization operation on the preprocessed spectral data by adopting continuum elimination method, and establishing a coal rock detection and identification model by combining SVM algorithm technology;
s4, sending the detection result of the coal rock identification into a database for storage and carrying out data comparison and analysis;
s5, outputting the comparison result of the database and the result of the field identification of the coal rock detection model;
further, the normal operation of the device in step S1 is to align the detection probe to the coal rock identification area, provide supplementary lighting by an external spotlight, and acquire and process the coal rock data detected by the hyperspectral technology by the sampling computer.
Further, the spectrum preprocessing operation of step S2 is to use CWT transform to correct the background and SG smoothing method to eliminate random noise in the spectrum signal.
Wherein the CWT transformation formula is as follows:
f (t) is a spectral signal, t is a time domain signal,
is a wavelet basis function, a is a translation parameter, and C is a wavelet coefficient.
Further, in the step S3, the spectral curve is normalized by using a continuum removal method, which is beneficial to comparison and extraction of subsequent characteristic bands, and an SVM technique is used to establish a model for coal rock recognition, wherein the specific algorithm flow is as follows:
s301, let t be the t-th node on the curve, the wavelength array be wl (i), and the reflectivity array be x (i), where i is 1, 2, …, N.
(1) Let (WL (1), X (1)) be the initial point of the envelope and assign the initial point of the original spectral data to this point.
(2) Using the point (WL (i), X (i)) as the initial point and searching the point (WL (j), X (j)) along the wavelength increasing direction of the spectrum curve according to a certain selection rule. Wherein the selection rule is as follows: let the straight line of (WL (i), X (i)) and (WL (j), X (j)) be L, the original curve should not be higher than the straight line L all the time, and add the point corresponding coordinates of (WL (j), X (j)) into the envelope node table.
(3) And (3) repeating the step (2) until i is equal to N, ending the traversal, and connecting the line segments of the two adjacent points.
(4) The envelope removal, then, has a reflectivity of:
where z (i) is the data value on the polygonal line segment corresponding to the reflectivity array x (i), where i is 1, 2, …, N.
S302, the spectral data with envelope elimination at 12 wavelength points in the curve are used as training data, the coal rock types of 70 samples are used as prediction target data, and a coal rock type Support Vector Classification (SVC) prediction model is established.
The SVC optimal interface function is:
wherein
b*Optimal solution of Lagrange transformation method for finding optimal interface in high-dimensional space, K (x)iX) is RBF kernel, c is penalty factor, yicThe type of the ith training sample is coded. Using 5-fold cross validation to solve, the parameter searching range is 2-10～210The index step is taken to be 0.5.
Further, in the step S4, all the data are sent to a database for storage, and the current spectrum identification and comparison result is given by combining the existing data information of the database.
Further, in step S5, the comparison information of the database and the result obtained by identifying the coal-rock model are displayed to the user, where the comparison information is used as an auxiliary reference, and the result is used as a main result of the detection.
Claims (6)
1. A hyperspectral coal rock identification and detection method is characterized by comprising the following steps: the method comprises the following steps:
s1, detecting the sample to be detected by using hyperspectral detection equipment under the normal operation of the equipment;
s2, carrying out spectrum preprocessing on the acquired spectrum data;
s3, performing normalization operation on the preprocessed spectral data by adopting continuum elimination method, and establishing a coal rock detection and identification model by combining SVM algorithm technology;
s4, sending the detection result of the coal rock identification into a database for storage and carrying out data comparison and analysis;
and S5, outputting the comparison result of the database and the result of the field identification of the coal rock detection model.
2. The hyperspectral coal rock identification and detection method according to claim 1 is characterized in that: the normal operation of the device in the step S1 is to align the detection probe to the coal rock identification area, provide supplementary lighting by an external spotlight, and acquire and process the coal rock data detected by hyperspectral technology by the sampling computer.
3. The hyperspectral coal rock identification and detection method according to claim 1 is characterized in that: the spectrum preprocessing operation in step S2 is to adopt CWT transformation to correct the background and SG smoothing method to eliminate random noise in the spectrum signal; wherein the CWT transformation formula is as follows:
f (t) is a spectral signal, t is a time domain signal,
is a wavelet basis function, a is a translation parameter, and C is a wavelet coefficient.
4. The hyperspectral coal rock identification and detection method according to claim 1 is characterized in that: in the step S3, the spectral curve is normalized by using a continuum removal method, which is beneficial to comparison and extraction of subsequent characteristic bands, and an SVM technique is used to establish a model for coal rock recognition, wherein the specific algorithm flow is as follows:
s301, let t be the t-th node on the curve, the wavelength array wl (i), the reflectivity array x (i), i ═ 1, 2, …, N:
(1) setting (WL (1), X (1)) as an envelope initial point, and assigning the initial point of the original spectrum data to the point;
(2) using the point (WL (i), X (i)) as the initial point and searching the point (WL (j), X (j)) along the wavelength increasing direction of the spectrum curve according to a certain selection rule; wherein the selection rule is as follows: let the straight line of (WL (i), X (i)) and (WL (j), X (j)) be L, the original curve must not be higher than the straight line L all the time, and add the point corresponding coordinates of (WL (j), X (j)) into the envelope node table;
(3) repeating the step (2) until i is equal to N, finishing traversal, and connecting line segments of two adjacent points;
(4) the envelope removal, then, has a reflectivity of:
wherein z (i) is a data value on the polygonal line segment corresponding to the reflectivity array x (i), i is 1, 2, …, N;
s302, establishing a coal rock type Support Vector Classification (SVC) prediction model by taking spectral data subjected to envelope elimination at 12 wavelength points in a curve as training data and coal rock types of 70 samples as prediction target data;
the SVC optimal interface function is:
wherein
b*Optimal solution of Lagrange transformation method for finding optimal interface in high-dimensional space, K (x)iX) is RBF kernel, c is penalty factor, yicThe type code of the ith training sample is coded; using 5-fold cross validation to solve, the parameter searching range is 2-10～210The index step is taken to be 0.5.
5. The hyperspectral coal rock identification and detection method according to claim 1 is characterized in that: and step S4, sending all the data into a database for storage, and giving the result of current spectrum identification and comparison by combining the existing data information of the database.
6. The hyperspectral coal rock identification and detection method according to claim 1 is characterized in that: in step S5, the comparison information of the database and the result obtained by identifying the coal-rock model are displayed to the user, the former is used as an auxiliary reference, and the latter is used as the main result of the detection.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112560597A (en) * | 2020-12-02 | 2021-03-26 | 吉林大学 | Microscopic hyperspectral COVID-19 detection and identification method |
| CN112881306A (en) * | 2021-01-15 | 2021-06-01 | 吉林大学 | Hyperspectral image-based method for rapidly detecting ash content of coal |
| CN112871751A (en) * | 2021-02-03 | 2021-06-01 | 精英数智科技股份有限公司 | Method and device for identifying coal and coal gangue |
| CN113029995A (en) * | 2021-03-10 | 2021-06-25 | 太原理工大学 | Linear frequency modulation coal rock radiation detection device and method |
| CN113406296A (en) * | 2021-06-24 | 2021-09-17 | 辽宁工程技术大学 | Coal petrography intelligent recognition system based on degree of depth learning |
| CN115165847A (en) * | 2022-07-07 | 2022-10-11 | 中煤科工集团上海有限公司 | Coal rock spectrum sensing device and coal mining machine comprising same |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104596943A (en) * | 2014-12-30 | 2015-05-06 | 中国矿业大学 | Indoor spectral layered measuring method for poisonous and harmful elements at mine reclamation area |
| CN105718749A (en) * | 2016-01-29 | 2016-06-29 | 清华大学 | Coal quality characteristic analysis method based on large database identification |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| CN107727592A (en) * | 2017-10-10 | 2018-02-23 | 中国矿业大学 | A kind of coal-rock interface identification method based on coal petrography high spectrum reflection characteristic |
| CN108458989A (en) * | 2018-04-28 | 2018-08-28 | 江苏建筑职业技术学院 | A kind of Coal-rock identification method based on Terahertz multi-parameter spectrum |
| CN108535214A (en) * | 2018-04-08 | 2018-09-14 | 浙江大学 | A method of Trichoderma is identified based on hyperspectral technique |
| CN108627486A (en) * | 2018-05-10 | 2018-10-09 | 江南大学 | A method of measuring the active principle and chemical composition content of Chinese medicine |
| CN109520950A (en) * | 2018-12-20 | 2019-03-26 | 淮阴工学院 | The insensitive chemical component spectral method of detection of a kind of pair of spectral shift |
| CN109901240A (en) * | 2019-04-03 | 2019-06-18 | 中国矿业大学 | A method of detecting residual coal spontaneous combustion region of appearing |
| 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 |
| CN111122469A (en) * | 2019-12-25 | 2020-05-08 | 吉林大学 | Method for determining feldspar content in igneous rock |
-
2020
- 2020-08-25 CN CN202010860621.2A patent/CN111982838A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104596943A (en) * | 2014-12-30 | 2015-05-06 | 中国矿业大学 | Indoor spectral layered measuring method for poisonous and harmful elements at mine reclamation area |
| CN105718749A (en) * | 2016-01-29 | 2016-06-29 | 清华大学 | Coal quality characteristic analysis method based on large database identification |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| CN107727592A (en) * | 2017-10-10 | 2018-02-23 | 中国矿业大学 | A kind of coal-rock interface identification method based on coal petrography high spectrum reflection characteristic |
| CN108535214A (en) * | 2018-04-08 | 2018-09-14 | 浙江大学 | A method of Trichoderma is identified based on hyperspectral technique |
| CN108458989A (en) * | 2018-04-28 | 2018-08-28 | 江苏建筑职业技术学院 | A kind of Coal-rock identification method based on Terahertz multi-parameter spectrum |
| CN108627486A (en) * | 2018-05-10 | 2018-10-09 | 江南大学 | A method of measuring the active principle and chemical composition content of Chinese medicine |
| CN109520950A (en) * | 2018-12-20 | 2019-03-26 | 淮阴工学院 | The insensitive chemical component spectral method of detection of a kind of pair of spectral shift |
| CN109901240A (en) * | 2019-04-03 | 2019-06-18 | 中国矿业大学 | A method of detecting residual coal spontaneous combustion region of appearing |
| 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 |
| CN111122469A (en) * | 2019-12-25 | 2020-05-08 | 吉林大学 | Method for determining feldspar content in igneous rock |
Non-Patent Citations (1)
| Title |
|---|
| 杨恩 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112560597A (en) * | 2020-12-02 | 2021-03-26 | 吉林大学 | Microscopic hyperspectral COVID-19 detection and identification method |
| CN112881306A (en) * | 2021-01-15 | 2021-06-01 | 吉林大学 | Hyperspectral image-based method for rapidly detecting ash content of coal |
| CN112871751A (en) * | 2021-02-03 | 2021-06-01 | 精英数智科技股份有限公司 | Method and device for identifying coal and coal gangue |
| CN113029995A (en) * | 2021-03-10 | 2021-06-25 | 太原理工大学 | Linear frequency modulation coal rock radiation detection device and method |
| CN113029995B (en) * | 2021-03-10 | 2022-09-27 | 太原理工大学 | Linear frequency modulation coal rock radiation detection device and method |
| CN113406296A (en) * | 2021-06-24 | 2021-09-17 | 辽宁工程技术大学 | Coal petrography intelligent recognition system based on degree of depth learning |
| CN115165847A (en) * | 2022-07-07 | 2022-10-11 | 中煤科工集团上海有限公司 | Coal rock spectrum sensing device and coal mining machine comprising same |
| CN116383704A (en) * | 2023-04-17 | 2023-07-04 | 中煤科工集团上海有限公司 | LIBS single spectral line-based coal and rock identification method |
| CN116383704B (en) * | 2023-04-17 | 2024-05-28 | 中煤科工集团上海有限公司 | LIBS single spectral line-based coal and rock identification method |
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