Coal quality detection method, device and equipment based on target calibration and medium

Abstract

The embodiment of the invention discloses a coal quality detection method, device and equipment based on target calibration and a medium, and relates to the field of laser detection, the method comprises the following steps: receiving current spectral data aiming at a target substance, determining a deviation coefficient of theoretical spectral data and the current spectral data according to the theoretical spectral data and the current spectral data of the target substance, and determining the deviation coefficient of the theoretical spectral data and the current spectral data; wherein the theoretical spectral data comprises a plurality of spectral reference lines, and the spectral reference lines are used for representing physical parameters of a spectral peak and providing a reference basis for determination of a deviation coefficient; receiving spectral data for coal quality, and calibrating the spectral data of the coal quality according to the deviation coefficient; and determining the coal quality components according to the calibrated spectrum data of the coal quality. According to the technical scheme provided by the invention, the accuracy of determining the coal components can be improved, and the problem that manual detection of the coal components is time-consuming and labor-consuming is solved.

Description

Technical Field

[0001] The present invention relates to the field of laser detection, and in particular to a coal quality detection method, device, equipment and medium based on target calibration. Background Art

[0002] As an important energy material at present, coal is widely used in various industrial productions. However, since other substances may be contained in the coal mining process, it is necessary to conduct component testing to determine the concentration of coal or other substances and then apply them to industrial production.

[0003] The current method for detecting coal composition mainly involves manual sampling, and then conducting laboratory testing on the sampled material to determine its composition. However, the current detection method is time-consuming and labor-intensive. Furthermore, due to the limitations of the manual sampling method itself, there is a large gap between different samples, resulting in low accuracy in coal composition detection. Summary of the invention

[0004] The present invention provides a coal quality detection method, device, equipment and medium based on target calibration. Through the technical solution provided by the present invention, the accuracy of coal quality component detection can be improved, and the problem of time-consuming and labor-intensive manual detection can be solved.

[0005] In a first aspect, an embodiment of the present invention provides a coal quality detection method based on target calibration, comprising:

[0006] Receiving current spectral data for a target substance, and determining a deviation coefficient of the theoretical spectral data and the current spectral data according to theoretical spectral data of the target substance and the current spectral data, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used to characterize physical parameters of spectral peaks and provide a reference benchmark for determining the deviation coefficient;

[0007] receiving spectral data for coal quality, and calibrating the spectral data for the coal quality according to the deviation coefficient;

[0008] The coal composition is determined based on the calibrated coal spectrum data.

[0009] In a second aspect, an embodiment of the present invention provides a coal quality detection device based on target calibration, comprising:

[0010] A deviation coefficient determination module, used for receiving current spectral data for a target substance, and determining a deviation coefficient between the theoretical spectral data and the current spectral data according to the theoretical spectral data of the target substance and the current spectral data, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used for characterizing the physical parameters of the spectral peaks and providing a reference benchmark for determining the deviation coefficient;

[0011] A calibration module, used for receiving spectral data of coal quality and calibrating the spectral data of coal quality according to the deviation coefficient;

[0012] The component determination module is used to determine the coal quality components according to the calibrated coal quality spectral data.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0014] at least one processor; and,

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the coal quality detection method based on target calibration as described in any one of the embodiments of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement any one of the target calibration-based coal quality detection methods described in the embodiments of the present invention when executed.

[0018] The embodiment of the present invention provides a coal quality detection method, device, equipment and medium based on target calibration, the method comprising: receiving current spectral data for a target substance, determining the deviation coefficient of the theoretical spectral data and the current spectral data according to the theoretical spectral data and the current spectral data of the target substance, wherein the theoretical spectral data includes a plurality of spectral reference lines, the spectral reference lines are used to characterize the physical parameters of the spectral peak, and provide a reference benchmark for determining the deviation coefficient; receiving spectral data for coal quality, calibrating the spectral data of the coal quality according to the deviation coefficient; determining the coal quality components according to the calibrated spectral data of the coal quality. Specifically, since the detection environment will affect the detection result, the deviation coefficient characterizing the environmental impact can be determined by determining the theoretical spectral data and the current spectral data of the target substance, and then the spectral data of the coal quality can be calibrated with the deviation coefficient, which can improve the accuracy of the spectral data of the coal quality, and then improve the accuracy of the coal quality component detection. The technical solution provided by the present invention can improve the accuracy of coal quality component detection and solve the problem of time-consuming and labor-intensive manual detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a coal quality detection method based on target calibration provided in Example 1 of the present invention;

[0021] Figure 2 A flow chart of a coal quality detection method based on target calibration provided in the second embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of a coal quality detection device based on target calibration provided in Embodiment 3 of the present invention;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the technical solution of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0027] Embodiment 1

[0028] Figure 1 A flow chart of a coal quality detection method based on target calibration is provided in Example 1 of the present invention. The method can be applied to the detection of the composition of coal mixtures / coal qualities, and can be executed by a coal quality detection device based on target calibration, which can be implemented by software and / or hardware and configured in various servers or computers.

[0029] like Figure 1 As shown, including:

[0030] Step 110: Receive current spectral data for a target substance, and determine a deviation coefficient of the theoretical spectral data and the current spectral data based on theoretical spectral data of the target substance and the current spectral data, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used to characterize physical parameters of spectral peaks and provide a reference benchmark for determining the deviation coefficient.

[0031] Wherein, the target substance is a high-purity substance containing specific spectral data and characteristics. The deviation effect of the detection environment on the overall detection work can be determined based on the laboratory data of the target substance and the actual data on site. In an embodiment of the present invention, the deviation coefficient can be determined based on the theoretical spectral data and current spectral data of the target substance. Wherein, the theoretical spectral data can be the spectral data of the laboratory for the target substance, and the current spectral data is the spectral data of the target substance in the current detection environment. Further spectral data can be a laser induced breakdown spectral data (Laser-Induced Breakdown Spectroscopy, LIBS), which can be obtained by a LIBS detector, which is not limited here. It should be noted that the target substance can be a substance with stable properties at room temperature, such as silicon dioxide, aluminum, copper or graphite with a purity greater than 99.9%.

[0032] Since spectral detection has strict requirements on the temperature, light and humidity in the environment, the actual detection environment is often very complex. Therefore, the deviation coefficient is used to characterize the degree of deviation of the current environment on the detection results. If the deviation coefficient is too large, it means that there is a large deviation between the current spectral data and the theoretical spectral data, indicating that the current environment has a great influence on the detection results. If the composition of coal quality is tested in the current environment, a large deviation will occur. Therefore, the deviation coefficient can be determined and then used to calibrate the detection results to improve the accuracy of the detection.

[0033] Specifically, the spectral data includes multiple spectral peaks. In order to accurately characterize the spectral peaks in the theoretical spectral data, corresponding spectral reference lines can be generated according to the spectral peaks in the theoretical spectral data, and then according to the spectral reference lines, a standard reference can be provided for determining the calibration coefficient. For example, the corresponding deviation coefficient can be determined based on the intensity and wavelength of the spectral peaks in the current spectral data and the intensity and wavelength of the spectral peaks represented by the spectral reference lines.

[0034] Optionally, the method for determining the spectrum reference line of the theoretical spectrum data includes:

[0035] Theoretical spectral data of the target substance under a preset environment are obtained, a set number of theoretical spectral peaks are determined according to physical parameters of the spectral peaks in the theoretical spectral data, and spectral reference lines are determined according to the theoretical spectral peaks.

[0036] Among them, the physical parameters include the intensity and wavelength of the spectral peak. Furthermore, a reference system is established with the wavelength as the horizontal axis and the intensity as the vertical axis, and the vertical line between the peak point of the spectral peak and the horizontal axis is used as the spectral reference line of the spectral peak.

[0037] Optionally, a theoretical spectrum of the target substance under a preset environment is obtained, wherein the number of spectrum peaks in different light bands of the theoretical spectrum exceeds a set number threshold;

[0038] For any light band in the theoretical spectrum diagram, determining a set number of theoretical spectrum peaks according to the intensity of the spectrum peaks of the light band;

[0039] The spectrum reference line is determined according to the intensity and wavelength of the theoretical spectrum peak.

[0040] The preset environment is the standard environment of the laboratory. The environment of the actual coal quality testing site can also be obtained in advance, and then the same environment can be set up in the laboratory as the preset environment. There is no restriction here. Further, in order to ensure the accuracy of the calibration coefficient, the spectral lines of multiple spectral peaks can be determined, and the number threshold is set to characterize the minimum spectral peak required for determining the deviation coefficient.

[0041] Exemplarily, the highest intensity and the second highest intensity spectral peak are selected as the target theoretical spectral peaks, and then the spectral reference line is determined according to the intensity and wavelength of the theoretical spectral peak. Further, to ensure the accuracy of the inspection, multiple spectral reference lines can also be determined to determine a more accurate deviation coefficient, which is not limited here.

[0042] Step 120: Receive spectral data of coal quality, and calibrate the spectral data of coal quality according to the deviation coefficient.

[0043] Specifically, the spectral data of coal quality may be the spectral data of LIBS for coal quality, and the spectral data of coal quality includes the spectral data of each coal quality component in the coal quality, including the spectral peak corresponding to each coal quality component. Specifically, due to the complexity of the detection environment, the spectral data of the coal quality actually detected will produce deviations, affecting the accuracy of the detection. Therefore, the spectral data of the coal quality may be calibrated according to the determined deviation coefficient. Specifically, the deviation coefficient may include an intensity deviation coefficient and a wavelength deviation coefficient, which are respectively used to calibrate the intensity and wavelength of the spectral peak of the coal quality. Then, the calibrated spectral data of the coal quality is used for component detection to improve the accuracy of component detection.

[0044] For example, if the theoretical spectrum data is A0 and the current spectrum data is A1, the deviation coefficient P between A1 and A0 can be determined by the properties of A1 and A0, where P represents the fixed deviation of the test result caused by the test environment. Since the current test environment is consistent, when the spectrum data of coal quality is tested, the spectrum data of coal quality will have the same deviation due to the test environment. Therefore, the spectrum data B0 of coal quality can be calibrated according to the deviation coefficient P to generate the calibrated spectrum data B1 of coal quality. Then, the calibrated spectrum data B1 of coal quality is used for component detection to improve the accuracy of component detection.

[0045] Optionally, determining the spectral peaks of the coal components based on the spectral data of the coal quality;

[0046] Calibrate the wavelength of the spectral peak of the coal quality component according to the wavelength deviation coefficient;

[0047] The intensity of the spectral peak of the coal quality component is calibrated according to the intensity deviation coefficient.

[0048] The coal quality components may include coal, sand, iron or other components in the coal mixture. The coal quality components correspond to unique spectral peaks in the spectral data of the coal quality, but due to the problem of the detection environment, the spectral peaks in the spectral data of the coal quality may be biased. Therefore, the wavelength of the spectral peak of the coal quality component can be calibrated according to the wavelength deviation coefficient; the intensity of the spectral peak of the coal quality component can be calibrated according to the intensity deviation coefficient.

[0049] Step 130: determine the coal quality composition according to the calibrated coal quality spectrum data.

[0050] Optionally, step 130 includes: obtaining a base model, wherein the base model includes wavelength data and intensity data of each coal component that may exist in the coal;

[0051] Based on the basis model, the actual coal components in the coal quality represented by the spectral data are determined according to the wavelength and intensity of the calibrated spectral peak.

[0052] Specifically, the base model includes wavelength data and intensity data of each coal component that may exist in the coal quality. The wavelength and intensity of the calibrated spectral peak are compared with the wavelength data and intensity data in the base model. If the comparison is successful, it means that the coal quality includes the coal component.

[0053] Optionally, the embodiment of the present invention further provides an exemplary method for determining a base model and theoretical spectral data, and the training process of the base model includes:

[0054] The spectrum, temperature, and humidity data generated by the LIBS instrument for the same batch of samples and the test result data obtained by traditional instruments according to national standards must be obtained under the same environmental temperature and humidity conditions, which are set as calibration reference conditions. Generate spectrum, temperature, and humidity data for each sample under different temperature and humidity conditions, where the spectrum needs to deduct the background data of air when no sample is placed, that is, the original spectrum data; the output data of the same batch of dry-basis coal samples are obtained by the manufacturer's traditional instruments according to national standards, which can be C, S, heavy metal element concentrations, calorific value, ash content, etc. Samples with abnormal value data must be eliminated; the original spectrum data set is cleaned to screen out abnormal outliers;

[0055] 5 to 15 characteristic wavelengths and their intensities with good repeatability are found from the original spectral data as input data, and the 5 to 15 characteristic wavelengths and the range of 5 nm around their wavelengths are set as the characteristic wavelength detection range of the sample; a mapping data set is established by one-to-one correspondence between the input data and the output data according to the sample; a training set and a prediction set are divided, and fitting is performed using algorithms such as a multivariate linear algorithm or a neural network fitting algorithm, wherein the output indicator during fitting is a single coal quality indicator, and the parameters are optimized and adjusted according to the feedback of the prediction effect; a single indicator output fitting model is obtained with spectrum, temperature, and humidity data as input and element content, calorific value, or ash content, and other data as output; multiple single indicator output fitting models are packaged in parallel, so that the entire model group takes spectrum, temperature, and humidity data as input and multiple data such as element content, calorific value, or ash content as output, and multiple independent models are identified in parallel internally, and finally a base model under the calibration reference condition of the LIBS instrument is obtained by combination.

[0056] An exemplary method of generating theoretical spectral data includes:

[0057] The calibration reference conditions are generated by adjusting the laboratory environment, wherein the calibration reference conditions include ambient temperature, humidity, brightness and other conditions.

[0058] The targets are placed in the LIBS instrument for multiple tests, with the target surface located exactly at the focus of the pulsed laser for irradiation until spectral data with high repeatability and a relative standard deviation of <2% are obtained. The wavelength and intensity of the extreme peak in each segmented spectrum are taken and set as the target characteristic wavelength and intensity of the target under the calibration reference condition; targets of at least two substances are selected for testing to ensure that there are significant extreme peaks in each segmented spectrum, and the extreme peaks are significantly present during multiple observations and restart observations. Finally, the extreme peak characteristic wavelengths and intensities of multiple targets together constitute the spectral reference line of the full wavelength range;

[0059] Furthermore, no less than two spectral reference lines can be set in each spectroscopic segment, including their wavelength and intensity data; illustratively, when two spectral reference lines are set, the highest point and the second highest point can be taken to form a binary target reference system, such as target A+B. When multi-segment spectroscopic is performed, the calibration reference target characteristics under the calibration reference conditions are: peak wavelength a nm, intensity X, peak wavelength b nm, intensity Y, peak wavelength c nm, intensity Z, peak wavelength d nm, intensity V, and so on in the first spectroscopic segment. The data of each spectroscopic segment are combined to obtain the theoretical spectral data.

[0060] The embodiment of the present invention provides a coal quality detection method based on target calibration. The method includes: receiving current spectral data for a target substance, determining the deviation coefficient of the theoretical spectral data and the current spectral data according to the theoretical spectral data and the current spectral data of the target substance, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used to characterize the physical parameters of the spectral peak and provide a reference benchmark for determining the deviation coefficient; receiving spectral data for coal quality, calibrating the spectral data of the coal quality according to the deviation coefficient; and determining the coal quality components according to the calibrated spectral data of the coal quality. Specifically, since the detection environment will affect the detection result, the deviation coefficient characterizing the environmental impact can be determined by determining the theoretical spectral data and the current spectral data of the target substance, and then the spectral data of the coal quality is calibrated with the deviation coefficient, which can improve the accuracy of the spectral data of the coal quality, and then improve the accuracy of the coal quality component detection, wherein the deviation coefficient includes a wavelength deviation coefficient and an intensity deviation coefficient, which are used to calibrate the wavelength and intensity of the spectral peak of the coal quality component. The technical solution provided by the present invention can improve the accuracy of coal quality component detection and solve the problem of time-consuming and labor-intensive manual detection.

[0061] Embodiment 2

[0062] Figure 2 A flow chart of a coal quality detection method based on target calibration provided in the second embodiment of the present invention. This embodiment further defines a method for determining a deviation coefficient based on the above embodiments, such as Figure 2 As shown, including:

[0063] Step 210: Receive current spectrum data for the target substance.

[0064] Step 220: Determine the wavelength region of the spectrum peak in the current spectrum data.

[0065] Among them, the wavelength regions of the spectral peaks of different substances are different, and the same substance may also have multiple spectral peaks. Therefore, it is necessary to locate the spectral peak in the current spectral data, determine the wavelength region of the spectral peak, and use it to compare with the spectral peak of the corresponding region in the theoretical spectral data.

[0066] Step 230: determine the wavelength region of the theoretical spectrum data as a target region, wherein the number of spectrum peaks in the target region is the same as the number of spectrum peaks in the wavelength region.

[0067] Step 240: Determine the deviation coefficient of the theoretical spectrum data and the current spectrum data according to the intensity and wavelength of the spectrum reference line of the target area and the intensity and wavelength of the spectrum peak in the wavelength area.

[0068] The target region is the region where the spectral peak to be compared is located.

[0069] Specifically, due to the complexity of the detection environment, the spectral peak in the current spectral data may be deformed, such as causing changes in wavelength and intensity. Therefore, it is necessary to determine the spectral peak to be compared in the target area, and then determine the deviation coefficient through the spectral peak in the current spectral data and the spectral reference line in the theoretical spectral data.

[0070] Specifically, the target area includes spectral reference lines representing the corresponding spectral peaks, and the spectral reference lines are generated according to the wavelength and intensity of the corresponding spectral peaks. Therefore, the required deviation coefficient can be determined based on the wavelength and intensity of the spectral reference lines in the theoretical spectral data and the wavelength and intensity of the corresponding spectral peaks in the current spectral data.

[0071] Optionally, step 240 includes:

[0072] Determine a spectral peak in the wavelength region as an actual spectral peak, and determine the wavelength and intensity of the actual spectral peak;

[0073] Determine the intensity and wavelength of the corresponding theoretical spectrum peak according to the intensity and wavelength of the spectrum reference line;

[0074] generating at least one theoretical spectrum peak group including two different theoretical spectrum peaks according to the theoretical spectrum peaks;

[0075] generating at least one actual spectrum peak group including two different actual spectrum peaks according to the actual spectrum peaks;

[0076] Based on the correspondence between the theoretical spectrum peaks and the actual spectrum peaks, determining a group correspondence between the theoretical spectrum peak group and the actual spectrum peak group;

[0077] The deviation coefficient of the theoretical spectral data and the current spectral data is determined based on the wavelength and intensity of the theoretical spectral peak, the wavelength and intensity of the actual spectral peak, the theoretical wavelength difference of the theoretical spectral peak group, the actual wavelength difference of the actual spectral peak group and the group correspondence.

[0078] The spectral peak in the wavelength region is the actual spectral peak in the current spectral data, and the wavelength and intensity of the actual spectral peak can be determined. The wavelength and intensity of the theoretical spectral peak in the theoretical spectral data can be determined according to the spectral reference line.

[0079] The theoretical spectrum peak group includes two different spectrum peaks in the theoretical spectrum data; and the actual spectrum peak group includes two different spectrum peaks in the current spectrum data.

[0080] Specifically, since there is a one-to-one correspondence between the spectral peaks in the current spectral data and the spectral peaks in the theoretical spectral data, there is also a one-to-one correspondence between the theoretical spectral peak group and the actual spectral peak group. Specifically, if the theoretical spectral peak group corresponds to each spectral peak in the actual spectral peak group, then the theoretical spectral peak group and the actual spectral peak group are corresponding spectral peak groups.

[0081] Exemplarily, the deviation coefficient may be determined by the following formula, wherein the deviation coefficient includes an intensity deviation coefficient and a wavelength deviation coefficient.

[0082] Intensity deviation coefficient = the intensity of the peak of the theoretical spectrum ÷ the intensity of the peak of the actual spectrum

[0083] The wavelength deviation coefficient includes the wavelength direction transformation intercept and the wavelength direction transformation coefficient.

[0084] The wavelength direction variation intercept = the wavelength of the highest peak of the actual spectrum peak - the wavelength of the highest peak of the theoretical spectrum peak.

[0085] Wavelength direction conversion coefficient = wavelength difference of spectral peaks in the theoretical spectral peak group - wavelength difference of spectral peaks in the actual spectral peak group.

[0086] It should be noted that the theoretical spectral peak and the actual spectral peak in the same formula are the corresponding spectral peaks in the theoretical spectral data and the current spectral data, and the theoretical spectral peak group and the actual spectral peak group are also corresponding spectral peak groups.

[0087] Optionally, the average intensity ratio is determined according to the intensity ratio of the actual spectral peaks corresponding to the theoretical spectral peaks;

[0088] If the average intensity ratio does not meet the preset threshold, the current spectrum data of the target material is reacquired.

[0089] Specifically, the intensity ratio can characterize the degree of deviation between the theoretical spectral peak and the corresponding actual spectral peak, and the average intensity ratio can characterize the average degree of deviation. If the average intensity ratio does not meet the preset threshold, it means that there may be problems in the current data acquisition process, or the current environment is harsh, which seriously affects the collection of spectral data. Therefore, it is necessary to re-acquire the current spectral data of the target substance.

[0090] Step 250: Receive spectral data for coal quality, and calibrate the spectral data for coal quality according to the deviation coefficient.

[0091] Specifically, the spectral peak of the coal quality can be determined based on the spectral data of the coal quality, and then the intensity and wavelength of the spectral peak of the coal quality can be determined.

[0092] The wavelength of the spectral data of coal quality after calibration = the wavelength of the spectral peak of coal quality + the wavelength direction conversion coefficient × (the wavelength of the spectral peak of coal quality - the wavelength of the spectral peak with the highest intensity in the current target data) - the wavelength direction conversion intercept

[0093] The intensity of the spectral data of coal quality after calibration = the intensity of the spectral peak of coal quality × the intensity direction conversion coefficient.

[0094] The wavelength direction transformation intercept, the wavelength direction transformation coefficient and the intensity direction transformation coefficient may be determined according to the formula in step 240 .

[0095] Step 260: determine the coal composition based on the calibrated coal spectrum data.

[0096] An embodiment of the present invention provides a coal quality detection method based on target calibration. The method can determine the deviation coefficient through the intensity and wavelength of the spectral reference line in the theoretical spectral data and the intensity and wavelength of the spectral peak in the current spectral data, and then use the deviation coefficient to calibrate the spectral data of the coal quality. This method can improve the accuracy of the spectral data of the coal quality, and thus improve the accuracy of coal quality component detection.

[0097] Embodiment 3

[0098] Figure 3 This is a schematic diagram of the structure of a coal quality detection device based on target calibration provided in the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0099] The deviation coefficient determination module 310 is used to receive the current spectrum data for the target substance, and determine the deviation coefficient of the theoretical spectrum data and the current spectrum data according to the theoretical spectrum data and the current spectrum data of the target substance, wherein the theoretical spectrum data includes a plurality of spectrum reference lines, and the spectrum reference lines are used to characterize the physical parameters of the spectrum peak and provide a reference benchmark for determining the deviation coefficient;

[0100] A calibration module 320, for receiving spectral data of coal quality, and calibrating the spectral data of the coal quality according to the deviation coefficient;

[0101] The component determination module 330 is used to determine the coal quality components according to the calibrated spectral data of the coal quality.

[0102] The beneficial effect of the target calibration-based coal quality detection device provided by the embodiment of the present invention is that: since the detection environment will affect the detection result, by determining the theoretical spectral data and current spectral data of the target substance, the deviation coefficient characterizing the environmental impact can be determined, and then the spectral data of the coal quality can be calibrated with the deviation coefficient, which can improve the accuracy of the spectral data of the coal quality, and then improve the accuracy of the coal quality component detection. The technical solution provided by the device of the present invention can improve the accuracy of coal quality component detection and solve the problem of time-consuming and labor-intensive manual detection.

[0103] Optionally, the coal quality detection device based on target calibration also includes: a spectral reference line determination module, which is used to obtain theoretical spectral data of the target material under a preset environment, determine a set number of theoretical spectral peaks according to physical parameters of the spectral peaks in the theoretical spectral data, and determine the spectral reference line according to the theoretical spectral peaks.

[0104] Furthermore, the spectrum reference line determination module specifically includes:

[0105] An acquisition unit, used for acquiring a theoretical spectrum of a target substance under a preset environment, wherein the number of spectrum peaks in different light bands of the theoretical spectrum exceeds a set number threshold;

[0106] A determination unit, for determining a set number of theoretical spectrum peaks according to the intensity of the spectrum peaks of any light band in the theoretical spectrum diagram;

[0107] The spectrum reference line determination unit is used to determine the spectrum reference line according to the intensity and wavelength of the theoretical spectrum peak.

[0108] Optionally, the deviation coefficient determination module 310 includes:

[0109] A wavelength region determination unit, used to determine the wavelength region of the spectrum peak in the current spectrum data;

[0110] a target region determining unit, configured to determine a wavelength region of the theoretical spectrum data as a target region, wherein the number of spectrum peaks in the target region is the same as the number of spectrum peaks in the wavelength region;

[0111] The deviation coefficient determination unit is used to determine the deviation coefficients of theoretical spectrum data and current spectrum data according to the intensity and wavelength of the spectrum reference line of the target area and the intensity and wavelength of the spectrum peak in the wavelength area.

[0112] The deviation coefficient determination unit includes:

[0113] A first determining subunit, configured to determine a spectral peak in a wavelength region as an actual spectral peak, and determine a wavelength and an intensity of the actual spectral peak;

[0114] A second determination subunit, used to determine the intensity and wavelength of the corresponding theoretical spectrum peak according to the intensity and wavelength of the spectrum reference line;

[0115] A theoretical spectrum peak group subunit, used for generating at least one theoretical spectrum peak group including two different theoretical spectrum peaks according to the theoretical spectrum peaks;

[0116] an actual spectrum peak group subunit, used for generating at least one actual spectrum peak group including two different actual spectrum peaks according to the actual spectrum peaks;

[0117] A group correspondence determination subunit, used to determine the group correspondence between the theoretical spectrum peak group and the actual spectrum peak group based on the correspondence between the theoretical spectrum peak and the actual spectrum peak;

[0118] The deviation coefficient determination subunit is used to determine the deviation coefficient of the theoretical spectral data and the current spectral data based on the wavelength and intensity of the theoretical spectral peak, the wavelength and intensity of the actual spectral peak, the theoretical wavelength difference of the theoretical spectral peak group, the actual wavelength difference of the actual spectral peak group and the group correspondence.

[0119] Optionally, the deviation coefficient determination unit further includes:

[0120] The inspection unit is used to determine the average intensity ratio according to the intensity ratio of the actual spectrum peaks corresponding to each theoretical spectrum peak; if the average intensity ratio does not meet the preset threshold, the current spectrum data of the target substance is re-acquired.

[0121] Optionally, the calibration module 320 includes:

[0122] A spectral peak determination unit, used for determining the spectral peak of the coal quality component according to the spectral data of the coal quality;

[0123] A wavelength calibration unit, used for calibrating the wavelength of the spectrum peak of the coal quality component according to the wavelength deviation coefficient;

[0124] The intensity calibration unit is used to calibrate the intensity of the spectral peak of the coal quality component according to the intensity deviation coefficient.

[0125] Optionally, the component determination module 330 includes:

[0126] An acquisition unit, used for acquiring a base model, wherein the base model includes wavelength data and intensity data of each coal component that may exist in the coal;

[0127] The detection unit is used to determine the coal quality components actually existing in the coal quality represented by the spectral data based on the base model and the wavelength and intensity of the calibrated spectral peak.

[0128] The coal quality detection device based on target calibration provided in the embodiment of the present invention can execute the coal quality detection method based on target calibration provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0129] Embodiment 4

[0130] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0131] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a coal quality detection method based on target calibration.

[0134] In some embodiments, the coal quality detection method based on target calibration can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the coal quality detection method based on target calibration described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the coal quality detection method based on target calibration in any other appropriate manner (for example, by means of firmware).

[0135] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0137] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0140] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0142] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A coal quality detection method based on target calibration, characterized in that: include: Receiving current spectral data for a target substance, and determining a deviation coefficient of the theoretical spectral data and the current spectral data according to theoretical spectral data of the target substance and the current spectral data, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used to characterize physical parameters of spectral peaks and provide a reference benchmark for determining the deviation coefficient; receiving spectral data for coal quality, and calibrating the spectral data for the coal quality according to the deviation coefficient; The coal composition is determined based on the calibrated coal spectrum data.

2. The method according to claim 1, characterized in that The method for determining the spectrum reference line of the theoretical spectrum data includes: Theoretical spectral data of the target substance under a preset environment are obtained, a set number of theoretical spectral peaks are determined according to physical parameters of the spectral peaks in the theoretical spectral data, and spectral reference lines are determined according to the theoretical spectral peaks.

3. The method according to claim 2, characterized in that The step of acquiring theoretical spectral data of a target substance under a preset environment, determining a set number of theoretical spectral peaks according to physical parameters of the spectral peaks in the theoretical spectral data, and determining spectral reference lines according to the theoretical spectral peaks includes: Acquiring a theoretical spectrum of the target substance under a preset environment, wherein the number of spectrum peaks in different light bands of the theoretical spectrum exceeds a set number threshold; For any light band in the theoretical spectrum diagram, determining a set number of theoretical spectrum peaks according to the intensity of the spectrum peaks of the light band; The spectrum reference line is determined according to the intensity and wavelength of the theoretical spectrum peak.

4. The method according to claim 1, characterized in that The step of determining the deviation coefficient of the theoretical spectral data and the current spectral data according to the theoretical spectral data and the current spectral data of the target substance comprises: Determine the wavelength region of the spectrum peak in the current spectrum data; Determining a wavelength region of the theoretical spectrum data as a target region, wherein the number of spectrum peaks in the target region is the same as the number of spectrum peaks in the wavelength region; The deviation coefficient of the theoretical spectral data and the current spectral data is determined according to the intensity and wavelength of the spectral reference line of the target area and the intensity and wavelength of the spectral peak in the wavelength area.

5. The method according to claim 4, characterized in that Determining the deviation coefficient of the theoretical spectrum data and the current spectrum data according to the intensity and wavelength of the spectrum reference line of the target area and the intensity and wavelength of the spectrum peak in the wavelength area includes: Determine a spectral peak in the wavelength region as an actual spectral peak, and determine the wavelength and intensity of the actual spectral peak; Determine the intensity and wavelength of the corresponding theoretical spectrum peak according to the intensity and wavelength of the spectrum reference line; generating at least one theoretical spectrum peak group including two different theoretical spectrum peaks according to the theoretical spectrum peaks; generating at least one actual spectrum peak group including two different actual spectrum peaks according to the actual spectrum peaks; Based on the correspondence between the theoretical spectrum peaks and the actual spectrum peaks, determining a group correspondence between the theoretical spectrum peak group and the actual spectrum peak group; The deviation coefficient of the theoretical spectral data and the current spectral data is determined based on the wavelength and intensity of the theoretical spectral peak, the wavelength and intensity of the actual spectral peak, the theoretical wavelength difference of the theoretical spectral peak group, the actual wavelength difference of the actual spectral peak group and the group correspondence.

6. The method according to claim 5, characterized in that Also includes: Determine the average intensity ratio according to the intensity ratio of the actual spectrum peaks corresponding to each theoretical spectrum peak; If the average intensity ratio does not meet the preset threshold, the current spectrum data of the target material is reacquired.

7. The method according to claim 1, characterized in that The deviation coefficient includes a wavelength deviation coefficient and an intensity deviation coefficient. The receiving spectral data for coal quality and calibrating the spectral data for coal quality according to the deviation coefficient include: Determine the spectral peaks of coal components based on the spectral data of coal quality; Calibrate the wavelength of the spectral peak of the coal quality component according to the wavelength deviation coefficient; The intensity of the spectral peak of the coal quality component is calibrated according to the intensity deviation coefficient.

8. The method according to claim 7, characterized in that The method of determining the coal quality composition according to the calibrated coal quality spectral data comprises: Acquire a base model, wherein the base model includes wavelength data and intensity data of each coal component that may exist in the coal; Based on the basis model, the actual coal components in the coal quality represented by the spectral data are determined according to the wavelength and intensity of the calibrated spectral peak.

9. A coal quality detection device based on target calibration, characterized in that: include: A deviation coefficient determination module, used for receiving current spectral data for a target substance, and determining a deviation coefficient between the theoretical spectral data and the current spectral data according to the theoretical spectral data of the target substance and the current spectral data, wherein the theoretical spectral data includes a plurality of spectral reference lines, and the spectral reference lines are used for characterizing the physical parameters of the spectral peaks and providing a reference benchmark for determining the deviation coefficient; A calibration module, used for receiving spectral data of coal quality and calibrating the spectral data of coal quality according to the deviation coefficient; The component determination module is used to determine the coal quality components according to the calibrated coal quality spectral data.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the coal quality detection method based on target calibration as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the coal quality detection method based on target calibration as described in any one of claims 1 to 8 when executed.

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